Leveraging machine learning models in evaluating ADMET properties for drug discovery and development
Review article
DOI:
https://doi.org/10.5599/admet.2772Keywords:
ADMET prediction, AI/ML, pharmacokinetics, computational toxicology, molecular descriptorsAbstract
Background and purpose: The evaluation of ADMET properties remains a critical bottleneck in drug discovery and development, contributing significantly to the high attrition rate of drug candidates. Traditional experimental approaches are often time-consuming, cost-intensive, and limited in scalability. This review aims to investigate how recent advances in machine learning (ML) models are revolutionizing ADMET prediction by enhancing accuracy, reducing experimental burden, and accelerating decision-making during early-stage drug development. Experimental approach: This article systematically examines the current landscape of ML applications in ADMET prediction, including the types of algorithms employed, common molecular descriptors and datasets used, and model development workflows. It also explores public databases, model evaluation metrics, and regulatory considerations relevant to computational toxicology. Emphasis is placed on supervised and deep learning techniques, model validation strategies, and the challenges of data imbalance and model interpretability. Key results: ML-based models have demonstrated significant promise in predicting key ADMET endpoints, outperforming some traditional quantitative structure - activity relationship (QSAR) models. These approaches provide rapid, cost-effective, and reproducible alternatives that integrate seamlessly with existing drug discovery pipelines. Case studies discussed in this review illustrate the successful deployment of ML models for solubility, permeability, metabolism, and toxicity predictions. Conclusion: Machine learning has emerged as a transformative tool in ADMET prediction, offering new opportunities for early risk assessment and compound prioritization. While challenges such as data quality, algorithm transparency, and regulatory acceptance persist, continued integration of ML with experimental pharmacology holds the potential to substantially improve drug development efficiency and reduce late-stage failures.
Downloads
References
[1] N. Berdigaliyev, M. Aljofan. An overview of drug discovery and development. Future Medicinal Chemistry 12 (2020) 939-947. https://doi.org/10.4155/fmc-2019-0307 DOI: https://doi.org/10.4155/fmc-2019-0307
[2] Z. Pei. Computer-aided drug discovery: From traditional simulation methods to language models and quantum computing. Cell Reports Physical Science 5 (2024) 11-21. https://doi.org/10.1016/j.xcrp.2024.102334 DOI: https://doi.org/10.1016/j.xcrp.2024.102334
[3] J. Vamathevan, D. Clark, P. Czodrowski, I. Dunham, E. Ferran, G. Lee, B. Li, A. Madabhushi, P. Shah, M. Spitzer, S. Zhao. Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery 18 (2019) 463-477. https://doi.org/10.1038/s41573-019-0024-5 DOI: https://doi.org/10.1038/s41573-019-0024-5
[4] S. Dara, S. Dhamercherla, S.S. Jadav, C.M. Babu, M.J. Ahsan. Machine Learning in Drug Discovery: A Review. Artificial Intelligence Review 55 (2022) 1947-1999. https://doi.org/10.1007/s10462-021-10058-4 DOI: https://doi.org/10.1007/s10462-021-10058-4
[5] G. Xiong, Z. Wu, J. Yi, L. Fu, Z. Yang, C. Hsieh, M. Yin, X. Zeng, C. Wu, A. Lu, X. Chen, T. Hou, D. Cao. ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research 49 (2021) W5-W14. https://doi.org/10.1093/nar/gkab255 DOI: https://doi.org/10.1093/nar/gkab255
[6] J.A. Pradeepkiran, S.B. Sainath. Brucella Melitensis: Identification and Characterization of Potential Drug Targets, Elsevier, Texas, US, (2021) 133-176. https://doi.org/10.1016/C2020-0-03079-3 DOI: https://doi.org/10.1016/C2020-0-03079-3
[7] D. Sun, W. Gao, H. Hu, S. Zhou. Why 90% of clinical drug development fails and how to improve it? Acta Pharmaceutica Sinica B 12 (2022) 3049-3062. https://doi.org/10.1016/j.apsb.2022.02.002 DOI: https://doi.org/10.1016/j.apsb.2022.02.002
[8] J. Jiménez-Luna, F. Grisoni, N. Weskamp, G. Schneider. Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opinion on Drug Discovery 16 (2021) 949-959. https://doi.org/10.1080/17460441.2021.1909567 DOI: https://doi.org/10.1080/17460441.2021.1909567
[9] S. Harrer, P. Shah, B. Antony, J. Hu. Artificial Intelligence for Clinical Trial Design. Trends in Pharmacological Sciences 40 (2019) 577-591. https://doi.org/10.1016/j.tips.2019.05.005 DOI: https://doi.org/10.1016/j.tips.2019.05.005
[10] M.J. Wildey, A. Haunso, M. Tudor, M. Webb, J.H. Connick. High-Throughput Screening. Annual Reports in Medicinal Chemistry 50 (2017) 149-195. https://doi.org/10.1016/bs.armc.2017.08.004 DOI: https://doi.org/10.1016/bs.armc.2017.08.004
[11] S. Nag, A.T.K. Baidya, A. Mandal, A.T. Mathew, B. Das, B. Devi, R. Kumar. Deep learning tools for advancing drug discovery and development. 3 Biotech 12 (2022) 110. https://doi.org/10.1007/s13205-022-03165-8 DOI: https://doi.org/10.1007/s13205-022-03165-8
[12] C.R. Hooijmans, R.B.M. De Vries, M. Ritskes-Hoitinga, M.M. Rovers, M.M. Leeflang, J. IntHout, K.E. Wever, L. Hooft, H. de Beer, T. Kuijpers, M.R. Macleod, E.S. Sena, G. ter Riet, R.L. Morgan, K.A. Thayer, A.A. Rooney, G.H. Guyatt, H.J. Schünemann, M.W. Langendam. Facilitating healthcare decisions by assessing the certainty in the evidence from preclinical animal studies. PLoS ONE 13(1) (2018) e0187271. https://doi.org/10.1371/journal.pone.0187271 DOI: https://doi.org/10.1371/journal.pone.0187271
[13] N. Singh, P. Vayer, S. Tanwar, J.-L. Poyet, K. Tsaioun, B.O. Villoutreix. Drug discovery and development: introduction to the general public and patient groups. Frontiers in Drug Discovery 3 (2023) 1201419. https://doi.org/10.3389/fddsv.2023.1201419 DOI: https://doi.org/10.3389/fddsv.2023.1201419
[14] F. Wu, Y. Zhou, L. Li, X. Shen, G. Chen, X. Wang, X. Liang, M. Tan, Z. Huang. Computational Approaches in Preclinical Studies on Drug Discovery and Development. Frontiers in Chemistry 8 (2020) 726. https://doi.org/10.3389/fchem.2020.00726 DOI: https://doi.org/10.3389/fchem.2020.00726
[15] D. Wang, W. Liu, Z. Shen, L. Jiang, J. Wang, S. Li, H. Li. Deep learning based drug metabolites prediction. Frontiers in Pharmacology 10 (2020) 1586. https://doi.org/10.3389/fphar.2019.01586 DOI: https://doi.org/10.3389/fphar.2019.01586
[16] Y. Wu, G. Wang. Machine learning based toxicity prediction: From chemical structural description to transcriptome analysis. International Journal of Molecular Sciences 19(8) (2018) 2358. https://doi.org/10.3390/ijms19082358 DOI: https://doi.org/10.3390/ijms19082358
[17] K. Maharana, S. Mondal, B. Nemade. Data pre-processing and data augmentation techniques. Global Transitions Proceedings 3 (2022) 91-99. https://doi.org/10.1016/j.gltp.2022.04.020 DOI: https://doi.org/10.1016/j.gltp.2022.04.020
[18] K. Bhayani, D. Tanna, V. Maan, Dhiraj, S. Kumar. An exploration of the impact of Feature quality versus Feature quantity on the performance of a machine learning model. in: IEEE Int. Conf. Contemp. Comput. Commun., IEEE, Bangalore, India, (2023): pp.1-5 https://doi.org/10.1109/InC457730.2023.10262824 DOI: https://doi.org/10.1109/InC457730.2023.10262824
[19] S.C. Rathi, S. Misra, R. Colomo-Palacios, R. Adarsh, L.B.M. Neti, L. Kumar. Empirical evaluation of the performance of data sampling and feature selection techniques for software fault prediction. Expert Systems with Applications 223 (2023) 119806. https://doi.org/10.1016/j.eswa.2023.119806 DOI: https://doi.org/10.1016/j.eswa.2023.119806
[20] F. Caloni, I. De Angelis, T. Hartung. Replacement of animal testing by integrated approaches to testing and assessment (IATA): a call for in vivitrosi. Archives of Toxicology 96 (2022) 1935-1950. https://doi.org/10.1007/s00204-022-03299-x DOI: https://doi.org/10.1007/s00204-022-03299-x
[21] J. Grzegorzewski, J. Brandhorst, K. Green, D. Eleftheriadou, Y. Duport, F. Barthorscht, A. Köller, D.Y.J. Ke, S. De Angelis, M. König. PK-DB: Pharmacokinetics database for individualized and stratified computational modeling. Nucleic Acids Research 49 (2021) D1358-D1364. https://doi.org/10.1093/nar/gkaa990 DOI: https://doi.org/10.1093/nar/gkaa990
[22] E. Pihan, L. Colliandre, J.F. Guichou, D. Douguet. E-Drug3D: 3D structure collections dedicated to drug repurposing and fragment-based drug design. Bioinformatics 28 (2012) 1540-1541. https://doi.org/10.1093/bioinformatics/bts186 DOI: https://doi.org/10.1093/bioinformatics/bts186
[23] D.S. Wishart, Y.D. Feunang, A.C. Guo, E.J. Lo, A. Marcu, J.R. Grant, T. Sajed, D. Johnson, C. Li, Z. Sayeeda, N. Assempour, I. Iynkkaran, Y. Liu, A. MacIejewski, N. Gale, A. Wilson, L. Chin, R. Cummings, Di. Le, A. Pon, C. Knox, M. Wilson. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Research 46 (2018) D1074-D1082. https://doi.org/10.1093/nar/gkx1037 DOI: https://doi.org/10.1093/nar/gkx1037
[24] A. Gaulton, L.J. Bellis, A.P. Bento, J. Chambers, M. Davies, A. Hersey, Y. Light, S. McGlinchey, D. Michalovich, B. Al-Lazikani, J.P. Overington. ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Research 40 (2012) D1100-D1107. https://doi.org/10.1093/nar/gkr777 DOI: https://doi.org/10.1093/nar/gkr777
[25] Y. Zhou, Y. Zhang, D. Zhao, X. Yu, X. Shen, Y. Zhou, S. Wang, Y. Qiu, Y. Chen, F. Zhu. T TD: Ther apeutic Targ et D atabase describing tar get drugg ability inf ormation. Nucleic Acids Research 52 (2024) D1465-D1477. https://doi.org/10.1093/nar/gkad751 DOI: https://doi.org/10.1093/nar/gkad751
[26] R.R. Sayre, J.F. Wambaugh, C.M. Grulke. Database of pharmacokinetic time-series data and parameters for 144 environmental chemicals. Scientific Data 7 (2020) 122. https://doi.org/10.1038/s41597-020-0455-1 DOI: https://doi.org/10.1038/s41597-020-0455-1
[27] S. Kim, J. Chen, T. Cheng, A. Gindulyte, J. He, S. He, Q. Li, B.A. Shoemaker, P.A. Thiessen, B. Yu, L. Zaslavsky, J. Zhang, E.E. Bolton. PubChem 2019 update: Improved access to chemical data. Nucleic Acids Research 47 (2019) D1102-D1109. https://doi.org/10.1093/nar/gky1033 DOI: https://doi.org/10.1093/nar/gky1033
[28] T. Sterling, J.J. Irwin. ZINC 15 - Ligand Discovery for Everyone. Journal of Chemical Information and Modeling 55 (2015) 2324-2337. https://doi.org/10.1021/acs.jcim.5b00559 DOI: https://doi.org/10.1021/acs.jcim.5b00559
[29] S. Preissner, K. Kroll, M. Dunkel, C. Senger, G. Goldsobel, D. Kuzman, S. Guenther, R. Winnenburg, M. Schroeder, R. Preissner. SuperCYP: A comprehensive database on Cytochrome P450 enzymes including a tool for analysis of CYP-drug interactions. Nucleic Acids Research 38 (2009) D237-D243. https://doi.org/10.1093/nar/gkp970 DOI: https://doi.org/10.1093/nar/gkp970
[30] Y. Xu, X. Liu, W. Xia, J. Ge, C.W. Ju, H. Zhang, J.Z.H. Zhang. ChemXTree: A Feature-Enhanced Graph Neural Network-Neural Decision Tree Framework for ADMET Prediction. Journal of Chemical Information and Modeling 64(22) (2024) 8440-8452. https://doi.org/10.1021/acs.jcim.4c01186 DOI: https://doi.org/10.1021/acs.jcim.4c01186
[31] E.N. Feinberg, E. Joshi, V.S. Pande, A.C. Cheng. Improvement in ADMET Prediction with Multitask Deep Featurization. Journal of Medicinal Chemistry 63 (2020) 8835-8848. https://doi.org/10.1021/acs.jmedchem.9b02187 DOI: https://doi.org/10.1021/acs.jmedchem.9b02187
[32] M. Malekipirbazari, V. Aksakalli, W. Shafqat, A. Eberhard. Performance comparison of feature selection and extraction methods with random instance selection. Expert Systems with Applications 179 (2021) 115072. https://doi.org/10.1016/j.eswa.2021.115072 DOI: https://doi.org/10.1016/j.eswa.2021.115072
[33] B. Venkatesh, J. Anuradha. A review of Feature Selection and its methods. Cybernetics and Information Technologies 19 (2019) 3-26. https://doi.org/10.2478/CAIT-2019-0001 DOI: https://doi.org/10.2478/cait-2019-0001
[34] N. Sánchez-Maroño, A. Alonso-Betanzos, M. Tombilla-Sanromán. Filter methods for feature selection - A comparative study. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4881 LNCS (2007) 178-187. https://doi.org/10.1007/978-3-540-77226-2_19 DOI: https://doi.org/10.1007/978-3-540-77226-2_19
[35] S.S.S.J. Ahmed, V. Ramakrishnan. Systems biological approach of molecular descriptors connectivity: Optimal descriptors for oral bioavailability prediction. PLoS ONE 7 (2012) e40654. https://doi.org/10.1371/journal.pone.0040654 DOI: https://doi.org/10.1371/journal.pone.0040654
[36] I. Tsamardinos, G. Borboudakis, P. Katsogridakis, P. Pratikakis, V. Christophides. A greedy feature selection algorithm for Big Data of high dimensionality. Machine Learning 108 (2019) 149-202. https://doi.org/10.1007/s10994-018-5748-7 DOI: https://doi.org/10.1007/s10994-018-5748-7
[37] H. Liu, M. Zhou, Q. Liu. An embedded feature selection method for imbalanced data classification. IEEE/CAA Journal of Automatica Sinica 6 (2019) 703-715. https://doi.org/10.1109/JAS.2019.1911447 DOI: https://doi.org/10.1109/JAS.2019.1911447
[38] H.H. Hsu, C.W. Hsieh, M. Da Lu. Hybrid feature selection by combining filters and wrappers. Expert Systems with Applications 38 (2011) 8144-8150. https://doi.org/10.1016/j.eswa.2010.12.156 DOI: https://doi.org/10.1016/j.eswa.2010.12.156
[39] P. Carracedo-Reboredo, J. Liñares-Blanco, N. Rodríguez-Fernández, F. Cedrón, F.J. Novoa, A. Carballal, V. Maojo, A. Pazos, C. Fernandez-Lozano. A review on machine learning approaches and trends in drug discovery. Computational and Structural Biotechnology Journal 19 (2021) 4538-4558. https://doi.org/10.1016/j.csbj.2021.08.011 DOI: https://doi.org/10.1016/j.csbj.2021.08.011
[40] F. Grisoni, D. Ballabio, R. Todeschini, V. Consonni. Molecular descriptors for structure-activity applications: A hands-on approach. Methods in Molecular Biology 1800 (2018) 3-53. https://doi.org/10.1007/978-1-4939-7899-1_1 DOI: https://doi.org/10.1007/978-1-4939-7899-1_1
[41] A. Mauri, V. Consonni, R. Todeschini. Molecular descriptors. Handbook of Computational Chemistry (2017) 2065-2093. https://doi.org/10.1007/978-3-319-27282-5_51 DOI: https://doi.org/10.1007/978-3-319-27282-5_51
[42] T. Mueller, A.G. Kusne, R. Ramprasad. Machine Learning in Materials Science: Recent Progress and Emerging Applications. Reviews in Computational Chemistry 29 (2016) 186-273. https://doi.org/10.1002/9781119148739.ch4 DOI: https://doi.org/10.1002/9781119148739.ch4
[43] R. Todeschini, V. Consonni. Molecular Descriptors for Chemoinformatics. Molecular Descriptors for Chemoinformatics 2 (2010) 1-252. https://doi.org/10.1002/9783527628766 DOI: https://doi.org/10.1002/9783527628766
[44] A.I. Odugbemi, C. Nyirenda, A. Christoffels, S.A. Egieyeh. Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors. Computational and Structural Biotechnology Journal 23 (2024) 2964-2977. https://doi.org/10.1016/j.csbj.2024.07.003 DOI: https://doi.org/10.1016/j.csbj.2024.07.003
[45] J. Deng, Z. Yang, H. Wang, I. Ojima, D. Samaras, F. Wang. A systematic study of key elements underlying molecular property prediction. Nature Communications 14 (2023) 6395. https://doi.org/10.1038/s41467-023-41948-6 DOI: https://doi.org/10.1038/s41467-023-41948-6
[46] B. Chandrasekaran, S.N. Abed, O. Al-Attraqchi, K. Kuche, R.K. Tekade. Computer-Aided Prediction of Pharmacokinetic (ADMET) Properties. Dosage Form Design Parameters 2 (2018) 731-755. https://doi.org/10.1016/B978-0-12-814421-3.00021-X DOI: https://doi.org/10.1016/B978-0-12-814421-3.00021-X
[47] D. Wan, J. Yang, T. Zhang, Y. Xiong. A novel method to identify influential nodes based on hybrid topology structure. Physical Communication 58 (2023) 102046. https://doi.org/10.1016/j.phycom.2023.102046 DOI: https://doi.org/10.1016/j.phycom.2023.102046
[48] D.C. Kombo, K. Tallapragada, R. Jain, J. Chewning, A.A. Mazurov, J.D. Speake, T.A. Hauser, S. Toler. 3D molecular descriptors important for clinical success. Journal of Chemical Information and Modeling 53 (2013) 327-342. https://doi.org/10.1021/ci300445e DOI: https://doi.org/10.1021/ci300445e
[49] D. Fourches, J. Ash. 4D- quantitative structure-activity relationship modeling: making a comeback. Expert Opinion on Drug Discovery 14 (2019) 1227-1235. https://doi.org/10.1080/17460441.2019.1664467 DOI: https://doi.org/10.1080/17460441.2019.1664467
[50] E. Jung, J. Kim, M. Kim, D.H. Jung, H. Rhee, J.M. Shin, K. Choi, S.K. Kang, M.K. Kim, C.H. Yun, Y.J. Choi, S.H. Choi. Artificial neural network models for prediction of intestinal permeability of oligopeptides. BMC Bioinformatics 8 (2007) 245. https://doi.org/10.1186/1471-2105-8-245 DOI: https://doi.org/10.1186/1471-2105-8-245
[51] R. Kumar, A. Sharma, M.H. Siddiqui, R.K. Tiwari. Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques. Current Drug Discovery Technologies 14 (2017) 244 - 254. https://doi.org/10.2174/1570163814666170404160911 DOI: https://doi.org/10.2174/1570163814666170404160911
[52] V. Acuña-Guzman, M.E. Montoya-Alfaro, L.P. Negrón-Ballarte, C. Solis-Calero. A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru. Pharmaceuticals 17(6) (2024) 750. https://doi.org/10.3390/ph17060750 DOI: https://doi.org/10.3390/ph17060750
[53] P. Stenberg, U. Norinder, K. Luthman, P. Artursson. Experimental and computational screening models for the prediction of intestinal drug absorption. Journal of Medicinal Chemistry 44 (2001) 1927-1937. https://doi.org/10.1021/jm001101a DOI: https://doi.org/10.1021/jm001101a
[54] T. Hou, J. Wang, W. Zhang, X. Xu. ADME evaluation in drug discovery. 7. prediction of oral absorption by correlation and classification. Journal of Chemical Information and Modeling 47 (2007) 208-218. https://doi.org/10.1021/ci600343x DOI: https://doi.org/10.1021/ci600343x
[55] E. Deconinck, H. Ates, N. Callebaut, E. Van Gyseghem, Y. Vander Heyden. Evaluation of chromatographic descriptors for the prediction of gastro-intestinal absorption of drugs. Journal of Chromatography A 1138 (2007) 190-202. https://doi.org/10.1016/j.chroma.2006.10.068 DOI: https://doi.org/10.1016/j.chroma.2006.10.068
[56] A. Talevi, M. Goodarzi, E. V. Ortiz, P.R. Duchowicz, C.L. Bellera, G. Pesce, E.A. Castro, L.E. Bruno-Blanch. Prediction of drug intestinal absorption by new linear and non-linear QSPR. European Journal of Medicinal Chemistry 46 (2011) 218-228. https://doi.org/10.1016/j.ejmech.2010.11.005 DOI: https://doi.org/10.1016/j.ejmech.2010.11.005
[57] A. Yan, Z. Wang, Z. Cai. Prediction of human intestinal absorption by GA feature selection and support vector machine regression. International Journal of Molecular Sciences 9 (2008) 1961-1976. https://doi.org/10.3390/ijms9101961 DOI: https://doi.org/10.3390/ijms9101961
[58] J. Shen, F. Cheng, Y. Xu, W. Li, Y. Tang. Estimation of ADME properties with substructure pattern recognition. Journal of Chemical Information and Modeling 50 (2010) 1034-1041. https://doi.org/10.1021/ci100104j DOI: https://doi.org/10.1021/ci100104j
[59] R. Bei, J. Thomas, S. Kapur, M. Woldeyes, A. Rauk, J. Robarge, J. Feng, K. Abbou Oucherif. Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach. MAbs 16 (2024) 2352887. https://doi.org/10.1080/19420862.2024.2352887 DOI: https://doi.org/10.1080/19420862.2024.2352887
[60] Y. Kamiya, K. Handa, T. Miura, J. Ohori, A. Kato, M. Shimizu, M. Kitajima, H. Yamazaki. Machine Learning Prediction of the Three Main Input Parameters of a Simplified Physiologically Based Pharmacokinetic Model Subsequently Used to Generate Time-Dependent Plasma Concentration Data in Humans after Oral Doses of 212 Disparate Chemicals. Biological and Pharmaceutical Bulletin 45 (2022) 124-128. https://doi.org/10.1248/bpb.b21-00769 DOI: https://doi.org/10.1248/bpb.b21-00769
[61] V.D. Karalis. Machine Learning in Bioequivalence: Towards Identifying an Appropriate Measure of Absorption Rate. Applied Sciences (Switzerland) 13(1) (2023) 418. https://doi.org/10.3390/app13010418 DOI: https://doi.org/10.3390/app13010418
[62] K. Kumar, V. Chupakhin, A. Vos, D. Morrison, D. Rassokhin, M.J. Dellwo, K. McCormick, E. Paternoster, H. Ceulemans, R.L. Desjarlais. Development and implementation of an enterprise-wide predictive model for early absorption, distribution, metabolism and excretion properties. Future Medicinal Chemistry 13 (2021) 1639-1654. https://doi.org/10.4155/fmc-2021-0138 DOI: https://doi.org/10.4155/fmc-2021-0138
[63] U. Fagerholm, S. Hellberg, O. Spjuth. Article advances in predictions of oral bioavailability of candidate drugs in man with new machine learning methodology. Molecules 26(9) (2021) 2572. https://doi.org/10.3390/molecules26092572 DOI: https://doi.org/10.3390/molecules26092572
[64] H. Bennett-Lenane, B.T. Griffin, J.P. O’Shea. Machine learning methods for prediction of food effects on bioavailability: A comparison of support vector machines and artificial neural networks. European Journal of Pharmaceutical Sciences 168 (2022) 106018. https://doi.org/10.1016/j.ejps.2021.106018 DOI: https://doi.org/10.1016/j.ejps.2021.106018
[65] S.S.S. Ng, Y. Lu. Evaluating the Use of Graph Neural Networks and Transfer Learning for Oral Bioavailability Prediction. Journal of Chemical Information and Modeling 63 (2023) 5035-5044. https://doi.org/10.1021/acs.jcim.3c00554 DOI: https://doi.org/10.1021/acs.jcim.3c00554
[66] K. Holt, S. Nagar, K. Korzekwa. Methods to Predict Volume of Distribution. Current Pharmacology Reports 5 (2019) 391-399. https://doi.org/10.1007/s40495-019-00186-5 DOI: https://doi.org/10.1007/s40495-019-00186-5
[67] X. Liu, B.J. Smith, C. Chen, E. Callegari, S.L. Becker, X. Chen, J. Cianfrogna, A.C. Doran, S.D. Doran, J.P. Gibbs, N. Hosea, J. Liu, F.R. Nelson, M.A. Szewc, J. Van Deusen. Use of a physiologically based pharmacokinetic model to study the time to reach brain equilibrium: An experimental analysis of the role of blood-brain barrier permeability, plasma protein binding, and brain tissue binding. Journal of Pharmacology and Experimental Therapeutics 313 (2005) 1254-1262. https://doi.org/10.1124/jpet.104.079319 DOI: https://doi.org/10.1124/jpet.104.079319
[68] Q.R. Smith, R. Samala. In situ and in vivo animal models. AAPS Advances in the Pharmaceutical Sciences Series 10 (2014) 199-211. https://doi.org/10.1007/978-1-4614-9105-7_7 DOI: https://doi.org/10.1007/978-1-4614-9105-7_7
[69] N.N. Wang, C. Huang, J. Dong, Z.J. Yao, M.F. Zhu, Z.K. Deng, B. Lv, A.P. Lu, A.F. Chen, D.S. Cao. Predicting human intestinal absorption with modified random forest approach: a comprehensive evaluation of molecular representation, unbalanced data, and applicability domain issues. RSC Advances 7 (2017) 19007-19018. https://doi.org/10.1039/C6RA28442F DOI: https://doi.org/10.1039/C6RA28442F
[70] E. V. Radchenko, A.S. Dyabina, V.A. Palyulin. Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds. Molecules 25(24) (2020) 5901. https://doi.org/10.3390/MOLECULES25245901 DOI: https://doi.org/10.3390/molecules25245901
[71] V. Antontsev, A. Jagarapu, Y. Bundey, H. Hou, M. Khotimchenko, J. Walsh, J. Varshney. A hybrid modeling approach for assessing mechanistic models of small molecule partitioning in vivo using a machine learning-integrated modeling platform. Scientific Reports 11 (2021) 11143. https://doi.org/10.1038/s41598-021-90637-1 DOI: https://doi.org/10.1038/s41598-021-90637-1
[72] Y. Yuan, S. Chang, Z. Zhang, Z. Li, S. Li, P. Xie, W.P. Yau, H. Lin, W. Cai, Y. Zhang, X. Xiang. A novel strategy for prediction of human plasma protein binding using machine learning techniques. Chemometrics and Intelligent Laboratory Systems 199 (2020) 103962. https://doi.org/10.1016/j.chemolab.2020.103962 DOI: https://doi.org/10.1016/j.chemolab.2020.103962
[73] A. Khaouane, S. Ferhat, S. Hanini. A Novel Methodology for Human Plasma Protein Binding: Prediction, Validation, and Applicability Domain. Pharmaceutical and Biomedical Research 8 (2022) 311-322. https://doi.org/10.32598/pbr.8.4.1086.1 DOI: https://doi.org/10.32598/PBR.8.4.1086.1
[74] I.F. Martins, A.L. Teixeira, L. Pinheiro, A.O. Falcao. A Bayesian approach to in Silico blood-brain barrier penetration modeling. Journal of Chemical Information and Modeling 52 (2012) 1686-1697. https://doi.org/10.1021/ci300124c DOI: https://doi.org/10.1021/ci300124c
[75] H. Golmohammadi, Z. Dashtbozorgi, W.E. Acree. Quantitative structure-activity relationship prediction of blood-to-brain partitioning behavior using support vector machine. European Journal of Pharmaceutical Sciences 47 (2012) 421-429. https://doi.org/10.1016/j.ejps.2012.06.021 DOI: https://doi.org/10.1016/j.ejps.2012.06.021
[76] H. Iwata, T. Matsuo, H. Mamada, T. Motomura, M. Matsushita, T. Fujiwara, K. Maeda, K. Handa. Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data. Journal of Chemical Information and Modeling 62 (2022) 4057-4065. https://doi.org/10.1021/acs.jcim.2c00318 DOI: https://doi.org/10.1021/acs.jcim.2c00318
[77] N. Parrott, N. Manevski, A. Olivares-Morales. Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or in Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds. Molecular Pharmaceutics 19 (2022) 3858-3868. https://doi.org/10.1021/acs.molpharmaceut.2c00350 DOI: https://doi.org/10.1021/acs.molpharmaceut.2c00350
[78] V. Mulpuru, N. Mishra. In Silico Prediction of Fraction Unbound in Human Plasma from Chemical Fingerprint Using Automated Machine Learning. ACS Omega 6 (2021) 6791-6797. https://doi.org/10.1021/acsomega.0c05846 DOI: https://doi.org/10.1021/acsomega.0c05846
[79] H. Cao, J. Peng, Z. Zhou, Z. Yang, L. Wang, Y. Sun, Y. Wang, Y. Liang. Investigation of the Binding Fraction of PFAS in Human Plasma and Underlying Mechanisms Based on Machine Learning and Molecular Dynamics Simulation. Environmental Science and Technology 57 (2023) 17762-17773. https://doi.org/10.1021/acs.est.2c04400 DOI: https://doi.org/10.1021/acs.est.2c04400
[80] M. Riedl, S. Mukherjee, M. Gauthier. Descriptor-Free Deep Learning QSAR Model for the Fraction Unbound in Human Plasma. Molecular Pharmaceutics 20 (2023) 4984-4993. https://doi.org/10.1021/acs.molpharmaceut.3c00129 DOI: https://doi.org/10.1021/acs.molpharmaceut.3c00129
[81] F. Hammann, H. Gutmann, U. Baumann, C. Helma, J. Drewe. Classification of cytochrome P450 activities using machine learning methods. Molecular Pharmaceutics 6 (2009) 1920-1926. https://doi.org/10.1021/mp900217x DOI: https://doi.org/10.1021/mp900217x
[82] T. Fox, J. Kriegl. Machine Learning Techniques for In Silico Modeling of Drug Metabolism. Current Topics in Medicinal Chemistry 6 (2006) 1579-1591. https://doi.org/10.2174/156802606778108915 DOI: https://doi.org/10.2174/156802606778108915
[83] H.X. Liu, X.J. Yao, R.S. Zhang, M.C. Liu, Z.D. Hu, B.T. Fan. Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines. Journal of Computer-Aided Molecular Design 19 (2005) 499-508. https://doi.org/10.1007/s10822-005-9003-5 DOI: https://doi.org/10.1007/s10822-005-9003-5
[84] J.Y. Ryu, J.H. Lee, B.H. Lee, J.S. Song, S. Ahn, K.S. Oh. PredMS: a random forest model for predicting metabolic stability of drug candidates in human liver microsomes. Bioinformatics 38 (2022) 364-368. https://doi.org/10.1093/bioinformatics/btab547 DOI: https://doi.org/10.1093/bioinformatics/btab547
[85] M. Baranwal, A. Magner, P. Elvati, J. Saldinger, A. Violi, A. Violi, A.O. Hero. A deep learning architecture for metabolic pathway prediction. Bioinformatics 36 (2020) 2547-2553. https://doi.org/10.1093/bioinformatics/btz954
[86] N.N. Wang, X.G. Wang, G.L. Xiong, Z.Y. Yang, A.P. Lu, X. Chen, S. Liu, T.J. Hou, D.S. Cao. Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes. Journal of Cheminformatics 14 (2022) 23. https://doi.org/10.1186/s13321-022-00602-x DOI: https://doi.org/10.1186/s13321-022-00602-x
[87] H. Mamada, Y. Nomura, Y. Uesawa. Prediction Model of Clearance by a Novel Quantitative Structure-Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning. ACS Omega 6 (2021) 23570-23577. https://doi.org/10.1021/acsomega.1c03689 DOI: https://doi.org/10.1021/acsomega.1c03689
[88] C.E. Keefer, G. Chang, L. Di, N.A. Woody, D.A. Tess, S.M. Osgood, B. Kapinos, J. Racich, A.A. Carlo, A. Balesano, N. Ferguson, C. Orozco, L. Zueva, L. Luo. The Comparison of Machine Learning and Mechanistic In Vitro-In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance. Molecular Pharmaceutics 20 (2023) 5616-5630. https://doi.org/10.1021/acs.molpharmaceut.3c00502 DOI: https://doi.org/10.1021/acs.molpharmaceut.3c00502
[89] R. Rodríguez-Pérez, M. Trunzer, N. Schneider, B. Faller, G. Gerebtzoff. Multispecies Machine Learning Predictions of in Vitro Intrinsic Clearance with Uncertainty Quantification Analyses. Molecular Pharmaceutics 20 (2023) 383-394. https://doi.org/10.1021/acs.molpharmaceut.2c00680 DOI: https://doi.org/10.1021/acs.molpharmaceut.2c00680
[90] A. Andrews-Morger, M. Reutlinger, N. Parrott, A. Olivares-Morales. A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling. Molecular Pharmaceutics 20 (2023) 5052-5065. https://doi.org/10.1021/acs.molpharmaceut.3c00374 DOI: https://doi.org/10.1021/acs.molpharmaceut.3c00374
[91] T.T. Van Tran, H. Tayara, K.T. Chong. Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives. Pharmaceutics 15(4) (2023) 1260. https://doi.org/10.3390/pharmaceutics15041260 DOI: https://doi.org/10.3390/pharmaceutics15041260
[92] R. Watanabe, R. Ohashi, T. Esaki, H. Kawashima, Y. Natsume-Kitatani, C. Nagao, K. Mizuguchi. Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor. Scientific Reports 9 (2019) 18782. https://doi.org/10.1038/s41598-019-55325-1 DOI: https://doi.org/10.1038/s41598-019-55325-1
[93] D. Bassani, N.J. Parrott, N. Manevski, J.D. Zhang. Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules. Expert Opinion on Drug Discovery 19 (2024) 683-698. https://doi.org/10.1080/17460441.2024.2348157 DOI: https://doi.org/10.1080/17460441.2024.2348157
[94] S. Seal, M.-A. Trapotsi, V. Subramanian, O. Spjuth, N. Greene, A. Bender. PKSmart: An Open-Source Computational Model to Predict in vivo Pharmacokinetics of Small Molecules. BioRxiv (2024). https://doi.org/10.1101/2024.02.02.578658 DOI: https://doi.org/10.1101/2024.02.02.578658
[95] J. Fan, S. Shi, H. Xiang, L. Fu, Y. Duan, D. Cao, H. Lu. Predicting Elimination of Small-Molecule Drug Half-Life in Pharmacokinetics Using Ensemble and Consensus Machine Learning Methods. Journal of Chemical Information and Modeling 64 (2024) 3080-3092. https://doi.org/10.1021/acs.jcim.3c02030 DOI: https://doi.org/10.1021/acs.jcim.3c02030
[96] Y.W. Hsiao, U. Fagerholm, U. Norinder. In silico categorization of in vivo intrinsic clearance using machine learning. Molecular Pharmaceutics 10 (2013) 1318-1321. https://doi.org/10.1021/mp300484r DOI: https://doi.org/10.1021/mp300484r
[97] H. Iwata, T. Matsuo, H. Mamada, T. Motomura, M. Matsushita, T. Fujiwara, M. Kazuya, K. Handa. Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning. Journal of Pharmaceutical Sciences 110 (2021) 1834-1841. https://doi.org/10.1016/j.xphs.2021.01.020 DOI: https://doi.org/10.1016/j.xphs.2021.01.020
[98] Y. Kosugi, N. Hosea. Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using in Vitro Assay. Molecular Pharmaceutics 17 (2020) 2299-2309. https://doi.org/10.1021/acs.molpharmaceut.9b01294 DOI: https://doi.org/10.1021/acs.molpharmaceut.9b01294
[99] P. Paixão, L.F. Gouveia, J.A.G. Morais. Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks. European Journal of Pharmaceutical Sciences 39 (2010) 310-321. https://doi.org/10.1016/j.ejps.2009.12.007 DOI: https://doi.org/10.1016/j.ejps.2009.12.007
[100] S.W. Paine, P. Barton, J. Bird, R. Denton, K. Menochet, A. Smith, N.P. Tomkinson, K.K. Chohan. A rapid computational filter for predicting the rate of human renal clearance. Journal of Molecular Graphics and Modelling 29 (2010) 529-537. https://doi.org/10.1016/j.jmgm.2010.10.003 DOI: https://doi.org/10.1016/j.jmgm.2010.10.003
[101] Y. Wang, H. Liu, Y. Fan, X. Chen, Y. Yang, L. Zhu, J. Zhao, Y. Chen, Y. Zhang. In Silico Prediction of Human Intravenous Pharmacokinetic Parameters with Improved Accuracy. Journal of Chemical Information and Modeling 59 (2019) 3968-3980. https://doi.org/10.1021/acs.jcim.9b00300 DOI: https://doi.org/10.1021/acs.jcim.9b00300
[102] W. Guo, J. Liu, F. Dong, M. Song, Z. Li, M.K.H. Khan, T.A. Patterson, H. Hong. Review of machine learning and deep learning models for toxicity prediction. Experimental Biology and Medicine 248 (2023) 1952-1973. https://doi.org/10.1177/15353702231209421 DOI: https://doi.org/10.1177/15353702231209421
[103] P. Rana, S. Kogut, X. Wen, F. Akhlaghi, M.D. Aleo. Most Influential Physicochemical and in Vitro Assay Descriptors for Hepatotoxicity and Nephrotoxicity Prediction. Chemical Research in Toxicology 33 (2020) 1780-1790. https://doi.org/10.1021/acs.chemrestox.0c00040 DOI: https://doi.org/10.1021/acs.chemrestox.0c00040
[104] M.Z.I. Khan, J.N. Ren, C. Cao, H.Y.X. Ye, H. Wang, Y.M. Guo, J.R. Yang, J.Z. Chen. Comprehensive hepatotoxicity prediction: ensemble model integrating machine learning and deep learning. Frontiers in Pharmacology 15 (2024) 1441587. https://doi.org/10.3389/fphar.2024.1441587 DOI: https://doi.org/10.3389/fphar.2024.1441587
[105] R. Ancuceanu, M.V. Hovanet, A.I. Anghel, F. Furtunescu, M. Neagu, C. Constantin, M. Dinu. Computational models using multiple machine learning algorithms for predicting drug hepatotoxicity with the dilirank dataset. International Journal of Molecular Sciences 21(6) (2020) 2114. https://doi.org/10.3390/ijms21062114 DOI: https://doi.org/10.3390/ijms21062114
[106] Y. Lu, L. Liu, D. Lu, Y. Cai, M. Zheng, X. Luo, H. Jiang, K. Chen. Predicting Hepatotoxicity of Drug Metabolites Via an Ensemble Approach Based on Support Vector Machine. Combinatorial Chemistry & High Throughput Screening 20 (2017) 839-849. https://doi.org/10.2174/1386207320666171121113255 DOI: https://doi.org/10.2174/1386207320666171121113255
[107] L.W. Chiu, Y.E. Ku, F.Y. Chan, W.N. Lie, H.J. Chao, S.Y. Wang, W.C. Shen, H.Y. Chen. Machine learning algorithms to predict colistin-induced nephrotoxicity from electronic health records in patients with multidrug-resistant gram-negative infection. International Journal of Antimicrobial Agents 64 (2024) 107175. https://doi.org/10.1016/j.ijantimicag.2024.107175 DOI: https://doi.org/10.1016/j.ijantimicag.2024.107175
[108] J.Y. Ryu, W.D. Jang, J. Jang, K.S. Oh. PredAOT: a computational framework for prediction of acute oral toxicity based on multiple random forest models. BMC Bioinformatics 24 (2023) 66. https://doi.org/10.1186/s12859-023-05176-5 DOI: https://doi.org/10.1186/s12859-023-05176-5
[109] F. Mostafa, V. Howle, M. Chen. Machine Learning to Predict Drug-Induced Liver Injury and Its Validation on Failed Drug Candidates in Development †. Toxics 12(6) (2024) 385. https://doi.org/10.3390/toxics12060385 DOI: https://doi.org/10.3390/toxics12060385
[110] C. Cai, P. Guo, Y. Zhou, J. Zhou, Q. Wang, F. Zhang, J. Fang, F. Cheng. Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity. Journal of Chemical Information and Modeling 59 (2019) 1073-1084. https://doi.org/10.1021/acs.jcim.8b00769 DOI: https://doi.org/10.1021/acs.jcim.8b00769
[111] D. Fan, H. Yang, F. Li, L. Sun, P. Di, W. Li, Y. Tang, G. Liu. In silico prediction of chemical genotoxicity using machine learning methods and structural alerts. Toxicology Research 7 (2018) 211-220. https://doi.org/10.1039/c7tx00259a DOI: https://doi.org/10.1039/C7TX00259A
[112] N.K. Shinada, N. Koyama, M. Ikemori, T. Nishioka, S. Hitaoka, A. Hakura, S. Asakura, Y. Matsuoka, S.K. Palaniappan. Optimizing machine-learning models for mutagenicity prediction through better feature selection. Mutagenesis 37 (2022) 191-202. https://doi.org/10.1093/mutage/geac010 DOI: https://doi.org/10.1093/mutage/geac010
[113] T. Li, W. Tong, R. Roberts, Z. Liu, S. Thakkar. DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation. Frontiers in Artificial Intelligence 4 (2021) 757780. https://doi.org/10.3389/frai.2021.757780 DOI: https://doi.org/10.3389/frai.2021.757780
[114] A. Talebi, A. Bitarafan-Rajabi, A. Alizadeh-asl, P. Seilani, B. Khajetash, G. Hajianfar, M. Tavakoli. Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer. Journal of Applied Clinical Medical Physics 26(4) (2024) e14614. https://doi.org/10.1002/acm2.14614 DOI: https://doi.org/10.1002/acm2.14614
[115] S. Iftkhar, A.G.C. De Sá, J.P.L. Velloso, R. Aljarf, D.E.V. Pires, D.B. Ascher. CardioToxCSM: A Web Server for Predicting Cardiotoxicity of Small Molecules. Journal of Chemical Information and Modeling 62 (2022) 4827-4836. https://doi.org/10.1021/acs.jcim.2c00822 DOI: https://doi.org/10.1021/acs.jcim.2c00822
[116] P. Trairatphisan, L. Dorsheimer, P. Monecke, J. Wenzel, R. James, A. Czich, Y. Dietz-Baum, F. Schmidt. Machine learning enhances genotoxicity assessment using MultiFlow® DNA damage assay. Environmental and Molecular Mutagenesis 66 (2024) 45-57. https://doi.org/10.1002/em.22648 DOI: https://doi.org/10.1002/em.22648
[117] C.N. Cavasotto, V. Scardino. Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point. ACS Omega 7 (2022) 47536-47546. https://doi.org/10.1021/acsomega.2c05693 DOI: https://doi.org/10.1021/acsomega.2c05693
[118] X. Ma, R. Wang, Y. Xue, Z. Li, S. Yang, Y. Wei, Y. Chen. Advances in Machine Learning Prediction of Toxicological Properties and Adverse Drug Reactions of Pharmaceutical Agents. Current Drug Safety 3 (2008) 100-114. https://doi.org/10.2174/157488608784529224 DOI: https://doi.org/10.2174/157488608784529224
[119] N. Madhukar, K. Gayvert, C. Gilvary, O. Elemento. A Machine Learning Approach Predicts Tissue-Specific Drug Adverse Events. BioRxiv (2018). https://doi.org/10.1101/288332 DOI: https://doi.org/10.1101/288332
[120] D. Naga, W. Muster, E. Musvasva, G.F. Ecker. Off-targetP ML: an open source machine learning framework for off-target panel safety assessment of small molecules. Journal of Cheminformatics 14 (2022) 27. https://doi.org/10.1186/s13321-022-00603-w DOI: https://doi.org/10.1186/s13321-022-00603-w
[121] L. Tonoyan, A.G. Siraki. Machine learning in toxicological sciences: opportunities for assessing drug toxicity. Frontiers in Drug Discovery 4 (2024) 1336025. https://doi.org/10.3389/fddsv.2024.1336025 DOI: https://doi.org/10.3389/fddsv.2024.1336025
[122] S. Okechukwuyem Ojji. Emerging Technology Integration - Artificial Intelligence (AI) and Machine Learning (ML) for Predictive Analysis for Safety and Toxicity Assessment in Environmental Toxicology. International Journal of Scientific Research and Management (IJSRM) 12 (2024) 1182-1195. https://doi.org/10.18535/ijsrm/v12i05.ec03 DOI: https://doi.org/10.18535/ijsrm/v12i05.ec03
[123] V. Blay, X. Li, J. Gerlach, F. Urbina, S. Ekins. Combining DELs and machine learning for toxicology prediction. Drug Discovery Today 27 (2022) 103351. https://doi.org/10.1016/j.drudis.2022.103351 DOI: https://doi.org/10.1016/j.drudis.2022.103351
[124] A. Lavecchia. Deep learning in drug discovery: opportunities, challenges and future prospects. Drug Discovery Today 24 (2019) 2017-2032. https://doi.org/10.1016/j.drudis.2019.07.006 DOI: https://doi.org/10.1016/j.drudis.2019.07.006
[125] H. Chen, O. Engkvist, Y. Wang, M. Olivecrona, T. Blaschke. The rise of deep learning in drug discovery. Drug Discovery Today 23 (2018) 1241-1250. https://doi.org/10.1016/j.drudis.2018.01.039 DOI: https://doi.org/10.1016/j.drudis.2018.01.039
[126] S. Min, B. Lee, S. Yoon. Deep learning in bioinformatics. Briefings in Bioinformatics 18 (2017) 851-869. https://doi.org/10.1093/bib/bbw068 DOI: https://doi.org/10.1093/bib/bbw068
[127] H. Belyadi, A. Haghighat. Supervised learning. Machine Learning Guide for Oil and Gas Using Python (2021) 169-295. https://doi.org/10.1016/b978-0-12-821929-4.00004-4 DOI: https://doi.org/10.1016/B978-0-12-821929-4.00004-4
[128] I.V.D. Srihith, P.V. Lakshmi, A.D. Donald, T. Aditya, T.A.S. Srinivas, G. Thippanna. A Forest of Possibilities: Decision Trees and Beyond. HARB Publication 6 (2023) 29-37. http://dx.doi.org/10.5281/zenodo.8372196
[129] D.S. Palmer, N.M. O’Boyle, R.C. Glen, J.B.O. Mitchell. Random forest models to predict aqueous solubility. Journal of Chemical Information and Modeling 47 (2007) 150-158. https://doi.org/10.1021/ci060164k DOI: https://doi.org/10.1021/ci060164k
[130] D.S. Cao, Y.N. Yang, J.C. Zhao, J. Yan, S. Liu, Q.N. Hu, Q.S. Xu, Y.Z. Liang. Computer-aided prediction of toxicity with substructure pattern and random forest. Journal of Chemometrics 26 (2012) 7-15. https://doi.org/10.1002/cem.1416 DOI: https://doi.org/10.1002/cem.1416
[131] D.S. Cao, Q.N. Hu, Q.S. Xu, Y.N. Yang, J.C. Zhao, H.M. Lu, L.X. Zhang, Y.Z. Liang. In silico classification of human maximum recommended daily dose based on modified random forest and substructure fingerprint. Analytica Chimica Acta 692 (2011) 50-56. https://doi.org/10.1016/j.aca.2011.02.010 DOI: https://doi.org/10.1016/j.aca.2011.02.010
[132] Y. Uesawa. Rigorous selection of random forest models for identifying compounds that activate toxicity-related pathways. Frontiers in Environmental Science 4 (2016) 9. https://doi.org/10.3389/fenvs.2016.00009 DOI: https://doi.org/10.3389/fenvs.2016.00009
[133] M.M. Suprijono, H. Sujuti, D. Kurnia, S.B. Widjanarko. Absorption, distribution, metabolism, excretion, and toxicity evaluation of Papua red fruit flavonoids through a computational study. in: IOP Conf. Ser. Earth Environ. Sci., IOP Science, Malang East Java Indonesia, (2020) https://doi.org/10.1088/1755-1315/475/1/012078 DOI: https://doi.org/10.1088/1755-1315/475/1/012078
[134] M.N. Murty, R. Raghava. Kernel-based SVM. SpringerBriefs in Computer Science 0 (2016) 57-67. https://doi.org/10.1007/978-3-319-41063-0_5 DOI: https://doi.org/10.1007/978-3-319-41063-0_5
[135] M.W.B. Trotter, S.B. Holden. Support vector machines for ADME property classification. QSAR and Combinatorial Science 22 (2003) 533-548. https://doi.org/10.1002/qsar.200310006 DOI: https://doi.org/10.1002/qsar.200310006
[136] J. Gola, O. Obrezanova, E. Champness, M. Segall. ADMET property prediction: The state of the art and current challenges. QSAR and Combinatorial Science 25 (2006) 1172-1180. https://doi.org/10.1002/qsar.200610093 DOI: https://doi.org/10.1002/qsar.200610093
[137] Y. Shi, K. Yang, Z. Yang, Y. Zhou. Primer on artificial intelligence. Mobile Edge Artificial Intelligence (2022) 7-36. https://doi.org/10.1016/b978-0-12-823817-2.00011-5 DOI: https://doi.org/10.1016/B978-0-12-823817-2.00011-5
[138] D. Gadaleta, F. Pizzo, A. Lombardo, A. Carotti, S.E. Escher, O. Nicolotti, E. Benfenati. A k-NN algorithm for predicting oral sub-chronic toxicity in the rat. Altex 31 (2014) 423-432. https://doi.org/10.14573/altex.1405091 DOI: https://doi.org/10.14573/altex.1405091
[139] F. Como, E. Carnesecchi, S. Volani, J.L. Dorne, J. Richardson, A. Bassan, M. Pavan, E. Benfenati. Predicting acute contact toxicity of pesticides in honeybees (Apis mellifera) through a k-nearest neighbor model. Chemosphere 166 (2017) 438-444. https://doi.org/10.1016/j.chemosphere.2016.09.092 DOI: https://doi.org/10.1016/j.chemosphere.2016.09.092
[140] S. Chavan, R. Friedman, I.A. Nicholls. Acute toxicity-supported chronic toxicity prediction: A k-nearest neighbor coupled read-across strategy. International Journal of Molecular Sciences 16 (2015) 11659-11677. https://doi.org/10.3390/ijms160511659 DOI: https://doi.org/10.3390/ijms160511659
[141] H. Tian, R. Ketkar, P. Tao. ADMETboost: a web server for accurate ADMET prediction. Journal of Molecular Modeling 28 (2022) 408. https://doi.org/10.1007/s00894-022-05373-8 DOI: https://doi.org/10.1007/s00894-022-05373-8
[142] T. An, Y. Chen, Y. Chen, L. Ma, J. Wang, J. Zhao. A machine learning-based approach to ERα bioactivity and drug ADMET prediction. Frontiers in Genetics 13 (2023) 1087273. https://doi.org/10.3389/fgene.2022.1087273 DOI: https://doi.org/10.3389/fgene.2022.1087273
[143] X. Li, L. Tang, Z. Li, D. Qiu, Z. Yang, B. Li. Prediction of ADMET Properties of Anti-Breast Cancer Compounds Using Three Machine Learning Algorithms. Molecules 28 (2023) 2326. https://doi.org/10.3390/molecules28052326 DOI: https://doi.org/10.3390/molecules28052326
[144] P.M. Vassiliev, A. V. Golubeva, A.R. Koroleva, M.A. Perfilev, A.N. Kochetkov. In Silico Prediction of Toxicological and Pharmacokinetic Characteristics of Medicinal Compounds. Safety and Risk of Pharmacotherapy 11 (2023) 390-408. https://doi.org/10.30895/2312-7821-2023-11-4-390-408 DOI: https://doi.org/10.30895/2312-7821-2023-11-4-390-408
[145] J. Wenzel, H. Matter, F. Schmidt. Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets. Journal of Chemical Information and Modeling 59 (2019) 1253-1268. https://doi.org/10.1021/acs.jcim.8b00785
[146] D.A. Winkler. Neural networks in ADME and toxicity prediction. Drugs of the Future 29 (2004) 1043-1057. https://doi.org/10.1358/dof.2004.029.10.863395 DOI: https://doi.org/10.1358/dof.2004.029.10.863395
[147] I. Pantic, J. Paunovic, J. Cumic, S. Valjarevic, G.A. Petroianu, P.R. Corridon. Artificial neural networks in contemporary toxicology research. Chemico-Biological Interactions 369 (2023) 110269. https://doi.org/10.1016/j.cbi.2022.110269 DOI: https://doi.org/10.1016/j.cbi.2022.110269
[148] N. Schapin, M. Majewski, A. Varela-Rial, C. Arroniz, G. De Fabritiis. Machine learning small molecule properties in drug discovery. Artificial Intelligence Chemistry 1 (2023) 100020. https://doi.org/10.1016/j.aichem.2023.100020 DOI: https://doi.org/10.1016/j.aichem.2023.100020
[149] P. Schyman, R. Liu, V. Desai, A. Wallqvist. vNN web server for ADMET predictions. Frontiers in Pharmacology 8 (2017) 889. https://doi.org/10.3389/fphar.2017.00889 DOI: https://doi.org/10.3389/fphar.2017.00889
[150] H. González-Díaz. ADMET-Multi-Output Cheminformatics Models for Drug Delivery, Interactomics, and Nanotoxicology. Current Drug Delivery 13 (2016) 1. https://pubmed.ncbi.nlm.nih.gov/27417300/
[151] A.T. Müller, J.A. Hiss, G. Schneider. Recurrent Neural Network Model for Constructive Peptide Design. Journal of Chemical Information and Modeling 58 (2018) 472-479. https://doi.org/10.1021/acs.jcim.7b00414 DOI: https://doi.org/10.1021/acs.jcim.7b00414
[152] A. De Carlo, D. Ronchi, M. Piastra, E.M. Tosca, P. Magni. Predicting ADMET Properties from Molecule SMILE: A Bottom-Up Approach Using Attention-Based Graph Neural Networks. Pharmaceutics 16 (2024) 776. https://doi.org/10.3390/pharmaceutics16060776 DOI: https://doi.org/10.3390/pharmaceutics16060776
[153] M. Aburidi, R. Marcia. Wasserstein Distance-Based Graph Kernel for Enhancing Drug Safety and Efficacy Prediction*. Proceedings - 2024 IEEE 1st International Conference on Artificial Intelligence for Medicine, Health and Care, AIMHC (2024) 113-119. https://doi.org/10.1109/AIMHC59811.2024.00029 DOI: https://doi.org/10.1109/AIMHC59811.2024.00029
[154] L. Mescheder, S. Nowozin, A. Geiger. Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks. ArXiv (2017). http://dx.doi.org/10.48550/arXiv.1701.04722
[155] A. Genevay, G. Peyré, M. Cuturi. GAN and VAE from an Optimal Transport Point of View. (2017). http://arxiv.org/abs/1706.01807
[156] K. Tsaioun, B.J. Blaauboer, T. Hartung. Evidence-based absorption, distribution, metabolism, excretion (ADME) and its interplay with alternative toxicity methods. Altex 33 (2016) 343-358. https://doi.org/10.14573/altex.1610101 DOI: https://doi.org/10.14573/altex.1610101
[157] L.K. Vora, A.D. Gholap, K. Jetha, R.R.S. Thakur, H.K. Solanki, V.P. Chavda. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics 15 (2023) 1916. https://doi.org/10.3390/pharmaceutics15071916 DOI: https://doi.org/10.3390/pharmaceutics15071916
[158] J. Kim, W. Chang, H. Ji, I.S. Joung. Quantum-Informed Molecular Representation Learning Enhancing ADMET Property Prediction. Journal of Chemical Information and Modeling 64 (2024) 5028-5040. https://doi.org/10.1021/acs.jcim.4c00772 DOI: https://doi.org/10.1021/acs.jcim.4c00772
[159] C. Hu, K. V. Saboo, A.H. Ali, B.D. Juran, K.N. Lazaridis, R.K. Iyer. REMEDI: REinforcement learning-driven adaptive MEtabolism modeling of primary sclerosing cholangitis DIsease progression. Proceedings of Machine Learning Research 225 (2023) 157-189. https://doi.org/10.48550/arXiv.2310.01426
[160] R.K. Tan, Y. Liu, L. Xie. Reinforcement learning for systems pharmacology-oriented and personalized drug design. Expert Opinion on Drug Discovery 17 (2022) 849-863. https://doi.org/10.1080/17460441.2022.2072288 DOI: https://doi.org/10.1080/17460441.2022.2072288
[161] S. Park, Y.H. Ko, B. Lee, B. Shin, B.R. Beck. Abstract 35: Molecular optimization of phase III trial failed anticancer drugs using target affinity and toxicity-centered multiple properties reinforcement learning. Clinical Cancer Research 26 (2020) 35-35. https://doi.org/10.1158/1557-3265.advprecmed20-35 DOI: https://doi.org/10.1158/1557-3265.ADVPRECMED20-35
[162] D. Yuan, H. He, Y. Wu, J. Fan, Y. Cao. Physiologically Based Pharmacokinetic Modeling of Nanoparticles. Journal of Pharmaceutical Sciences 108 (2019) 58-72. https://doi.org/10.1016/j.xphs.2018.10.037 DOI: https://doi.org/10.1016/j.xphs.2018.10.037
[163] D. Deepika, V. Kumar. The Role of “Physiologically Based Pharmacokinetic Model (PBPK)” New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment. International Journal of Environmental Research and Public Health 20 (2023) 3473. https://doi.org/10.3390/ijerph20043473 DOI: https://doi.org/10.3390/ijerph20043473
[164] Y. Kamiya, K. Handa, T. Miura, M. Yanagi, K. Shigeta, S. Hina, M. Shimizu, M. Kitajima, F. Shono, K. Funatsu, H. Yamazaki. In Silico Prediction of Input Parameters for Simplified Physiologically Based Pharmacokinetic Models for Estimating Plasma, Liver, and Kidney Exposures in Rats after Oral Doses of 246 Disparate Chemicals. Chemical Research in Toxicology 34 (2021) 507-513. https://doi.org/10.1021/acs.chemrestox.0c00336 DOI: https://doi.org/10.1021/acs.chemrestox.0c00336
[165] W.C. Chou, Z. Lin. Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling. Toxicological Sciences 191 (2023) 1-14. https://doi.org/10.1093/toxsci/kfac101 DOI: https://doi.org/10.1093/toxsci/kfac101
[166] Y. Li, Z. Wang, Y. Li, J. Du, X. Gao, Y. Li, L. Lai. A Combination of Machine Learning and PBPK Modeling Approach for Pharmacokinetics Prediction of Small Molecules in Humans. Pharmaceutical Research 41 (2024) 1369-1379. https://doi.org/10.1007/s11095-024-03725-y DOI: https://doi.org/10.1007/s11095-024-03725-y
[167] D. Naga, N. Parrott, G.F. Ecker, A. Olivares-Morales. Evaluation of the Success of High-Throughput Physiologically Based Pharmacokinetic (HT-PBPK) Modeling Predictions to Inform Early Drug Discovery. Molecular Pharmaceutics 19 (2022) 2203-2216. https://doi.org/10.1021/ACS.MOLPHARMACEUT.2C00040 DOI: https://doi.org/10.1021/acs.molpharmaceut.2c00040
[168] S. Habiballah, B. Reisfeld. Adapting physiologically-based pharmacokinetic models for machine learning applications. Scientific Reports 13 (2023) 14934. https://doi.org/10.1038/s41598-023-42165-3 DOI: https://doi.org/10.1038/s41598-023-42165-3
[169] Y. Zhang, Q. Yang. An overview of multi-task learning. National Science Review 5 (2018) 30-43. https://doi.org/10.1093/nsr/nwx105 DOI: https://doi.org/10.1093/nsr/nwx105
[170] S. Zhang, X. Luo, B. Mai. Multi-task machine learning models for simultaneous prediction of tissue-to-blood partition coefficients of chemicals in mammals. Environmental Research 241 (2024) 117603. https://doi.org/10.1016/j.envres.2023.117603 DOI: https://doi.org/10.1016/j.envres.2023.117603
[171] M. Walter, J.M. Borghardt, L. Humbeck, M. Skalic. Multi-task ADME/PK Prediction at Industrial Scale: Leveraging Large and Diverse Experimental Datasets. ChemRxiv (2024) e202400079. https://doi.org/10.1002/minf.202400079 DOI: https://doi.org/10.26434/chemrxiv-2024-pf4w9
[172] Z. Zhao, L. Alzubaidi, J. Zhang, Y. Duan, Y. Gu. A comparison review of transfer learning and self-supervised learning: Definitions, applications, advantages and limitations. Expert Systems with Applications 242 (2024) 122807. https://doi.org/10.1016/j.eswa.2023.122807 DOI: https://doi.org/10.1016/j.eswa.2023.122807
[173] Z. Ye, Y. Yang, X. Li, D. Cao, D. Ouyang. An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction. Molecular Pharmaceutics 16 (2019) 533-541. https://doi.org/10.1021/acs.molpharmaceut.8b00816 DOI: https://doi.org/10.1021/acs.molpharmaceut.8b00816
[174] K. Abbasi, A. Poso, J. Ghasemi, M. Amanlou, A. Masoudi-Nejad. Deep transferable compound representation across domains and tasks for low data drug discovery. Journal of Chemical Information and Modeling (2019) 4528-4539. https://doi.org/10.1021/acs.jcim.9b00626 DOI: https://doi.org/10.1021/acs.jcim.9b00626
[175] S. Wang, Y. Guo, Y. Wang, H. Sun, J. Huang. Smiles-Bert: Large scale unsupervised pre-training for molecular property prediction. ACM-BCB 2019 - Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (2019) 429-436. https://doi.org/10.1145/3307339.3342186 DOI: https://doi.org/10.1145/3307339.3342186
[176] X. Li, D. Fourches. Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT. Journal of Cheminformatics 12 (2020) 27. https://doi.org/10.1186/s13321-020-00430-x DOI: https://doi.org/10.1186/s13321-020-00430-x
[177] S. Zhang, Z. Yan, Y. Huang, L. Liu, D. He, W. Wang, X. Fang, X. Zhang, F. Wang, H. Wu, H. Wang. HelixADMET: A robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer. Bioinformatics 38 (2022) 3444-3453. https://doi.org/10.1093/bioinformatics/btac342 DOI: https://doi.org/10.1093/bioinformatics/btac342
[178] W. Jung, S. Goo, T. Hwang, H. Lee, Y.K. Kim, J.W. Chae, H.Y. Yun, S. Jung. Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development. Pharmaceuticals 17 (2024) 382. https://doi.org/10.3390/ph17030382 DOI: https://doi.org/10.3390/ph17030382
[179] J. Wenzel, H. Matter, F. Schmidt. Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets. Journal of Chemical Information and Modeling 59 (2019) 1253-1268. https://doi.org/10.4155/fmc-2021-0138 DOI: https://doi.org/10.1021/acs.jcim.8b00785
[180] W. Saeed, C. Omlin. Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities. Knowledge-Based Systems 263 (2023) 110273. https://doi.org/10.1016/j.knosys.2023.110273 DOI: https://doi.org/10.1016/j.knosys.2023.110273
[181] R. Alizadehsani, S.S. Oyelere, S. Hussain, S.K. Jagatheesaperumal, R.R. Calixto, M. Rahouti, M. Roshanzamir, V.H.C. De Albuquerque. Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey. IEEE Access 12 (2024) 35796-35812. https://doi.org/10.1109/ACCESS.2024.3373195 DOI: https://doi.org/10.1109/ACCESS.2024.3373195
[182] A. McGrath. What is AI interpretability? IBM (2024). https://www.ibm.com/think/topics/interpretability
[183] V. Vimbi, N. Shaffi, M. Mahmud. Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer’s disease detection. Brain Informatics 11 (2024) 10. https://doi.org/10.1186/s40708-024-00222-1 DOI: https://doi.org/10.1186/s40708-024-00222-1
[184] J. Patterson, N. Tatonetti. KG-LIME: predicting individualized risk of adverse drug events for multiple sclerosis disease-modifying therapy. Journal of the American Medical Informatics Association 31 (2024) 1693-1703. https://doi.org/10.1093/jamia/ocae155 DOI: https://doi.org/10.1093/jamia/ocae155
[185] F. Gabbay, S. Bar-Lev, O. Montano, N. Hadad. A lime-based explainable machine learning model for predicting the severity level of covid-19 diagnosed patients. Applied Sciences (Switzerland) 11 (2021) 10417. https://doi.org/10.3390/app112110417 DOI: https://doi.org/10.3390/app112110417
[186] S. Singh, R. Kumar, S. Payra, S.K. Singh. Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery. Cureus 15(8) (2023) e44359. https://doi.org/10.7759/cureus.44359 DOI: https://doi.org/10.7759/cureus.44359
[187] T.Z. Long, D.J. Jiang, S.H. Shi, Y.C. Deng, W.X. Wang, D.S. Cao. Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence. Journal of Chemical Information and Modeling 64 (2024) 3222-3236. https://doi.org/10.1021/acs.jcim.4c00159 DOI: https://doi.org/10.1021/acs.jcim.4c00159
[188] C. König, A. Vellido. Understanding predictions of drug profiles using explainable machine learning models. BioData Mining 17 (2024) 25. https://doi.org/10.1186/s13040-024-00378-w DOI: https://doi.org/10.1186/s13040-024-00378-w
[189] R. Rodríguez-Pérez, J. Bajorath. Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. Journal of Computer-Aided Molecular Design 34 (2020) 1013-1026. https://doi.org/10.1007/s10822-020-00314-0 DOI: https://doi.org/10.1007/s10822-020-00314-0
[190] S. Marshall, R. Madabushi, E. Manolis, K. Krudys, A. Staab, K. Dykstra, S.A.G. Visser. Model-Informed Drug Discovery and Development: Current Industry Good Practice and Regulatory Expectations and Future Perspectives. CPT: Pharmacometrics and Systems Pharmacology 8 (2019) 87-96. https://doi.org/10.1002/psp4.12372 DOI: https://doi.org/10.1002/psp4.12372
[191] N. Terranova, D. Renard, M.H. Shahin, S. Menon, Y. Cao, C.E.C.A. Hop, S. Hayes, K. Madrasi, S. Stodtmann, T. Tensfeldt, P. Vaddady, N. Ellinwood, J. Lu. Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices. Clinical Pharmacology and Therapeutics 115 (2024) 658-672. https://doi.org/10.1002/cpt.3053 DOI: https://doi.org/10.1002/cpt.3053
[192] S.H. Kang, M.R. Poynton, K.M. Kim, H. Lee, D.H. Kim, S.H. Lee, K.S. Bae, O. Linares, S.E. Kern, G.J. Noh. Population pharmacokinetic and pharmacodynamic models of remifentanil in healthy volunteers using artificial neural network analysis. British Journal of Clinical Pharmacology 64 (2007) 3-13. https://doi.org/10.1111/j.1365-2125.2007.02845.x DOI: https://doi.org/10.1111/j.1365-2125.2007.02845.x
[193] B. Canault, S. Bourg, P. Vayer, P. Bonnet. Comprehensive Network Map of ADME-Tox Databases. Molecular Informatics 36 (2017) 1700029. https://doi.org/10.1002/minf.201700029 DOI: https://doi.org/10.1002/minf.201700029
[194] S. Ekins, A.J. Williams. Precompetitive preclinical ADME/Tox data: Set it free on the web to facilitate computational model building and assist drug development. Lab on a Chip 10 (2010) 13-22. https://doi.org/10.1039/b917760b DOI: https://doi.org/10.1039/B917760B
[195] G. Pawar, J.C. Madden, D. Ebbrell, J.W. Firman, M.T.D. Cronin. In silico toxicology data resources to support read-across and (Q)SAR. Frontiers in Pharmacology 10 (2019) 561. https://doi.org/10.3389/fphar.2019.00561 DOI: https://doi.org/10.3389/fphar.2019.00561
[196] M.A. Miteva, S. Violas, M. Montes, D. Gomez, P. Tuffery, B.O. Villoutreix. FAF-Drugs: Free ADME/tox filtering of compound collections. Nucleic Acids Research 34 (2006) W738-W744. https://doi.org/10.1093/nar/gkl065 DOI: https://doi.org/10.1093/nar/gkl065
[197] D. Rao, V.N. Gudivada, V. V. Raghavan. Data quality issues in big data. Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data (2015) 2654-2660. https://doi.org/10.1109/BigData.2015.7364065 DOI: https://doi.org/10.1109/BigData.2015.7364065
[198] W. Fan, F. Geerts. Foundations of Data Quality Management, 1st ed., Springer Nature Link, (2012) https://doi.org/10.1007/978-3-031-01892-3 DOI: https://doi.org/10.1007/978-3-031-01892-3
[199] K. Natarajan, J. Li, A. Koronios. Data mining techniques for data cleaning. Engineering Asset Lifecycle Management - Proceedings of the 4th World Congress on Engineering Asset Management, WCEAM (2009) 796-804. https://doi.org/10.1007/978-0-85729-320-6_91 DOI: https://doi.org/10.1007/978-0-85729-320-6_91
[200] C. Batini, M. Scannapieco. Data Quality: Concepts, Methodologies and Techniques. Springer 1 (2006) 161-200. http://dx.doi.org/10.1007/3-540-33173-5 DOI: https://doi.org/10.1007/3-540-33173-5
[201] M. Picard, M.P. Scott-Boyer, A. Bodein, O. Périn, A. Droit. Integration strategies of multi-omics data for machine learning analysis. Computational and Structural Biotechnology Journal 19 (2021) 3735-3746. https://doi.org/10.1016/j.csbj.2021.06.030 DOI: https://doi.org/10.1016/j.csbj.2021.06.030
[202] H. Bansal, H. Luthra, S.R. Raghuram. A Review on Machine Learning Aided Multi-omics Data Integration Techniques for Healthcare. Studies in Big Data 132 (2023) 211-239. https://doi.org/10.1007/978-3-031-38325-0_10 DOI: https://doi.org/10.1007/978-3-031-38325-0_10
[203] M. Akhmedov, A. Arribas, R. Montemanni, F. Bertoni, K. Ivo. OmicsNet: Integration of Multi-Omics Data using Path Analysis in Multilayer Networks. BioRxiv (2017). https://doi.org/10.1101/238766 DOI: https://doi.org/10.1101/238766
[204] I. Subramanian, S. Verma, S. Kumar, A. Jere, K. Anamika. Multi-omics Data Integration, Interpretation, and Its Application. Bioinformatics and Biology Insights 14 (2020) 16-21. https://doi.org/10.1177/1177932219899051 DOI: https://doi.org/10.1177/1177932219899051
[205] P.K. Davis, A. O’Mahony, J. Pfautz. Social-behavioral modeling for complex systems. Social-Behavioral Modeling for Complex Systems (2019) 1-947. https://doi.org/10.1002/9781119485001 DOI: https://doi.org/10.1002/9781119485001
[206] A. Pardo, G. Siemens. Ethical and privacy principles for learning analytics. British Journal of Educational Technology 45 (2014) 438-450. https://doi.org/10.1111/bjet.12152 DOI: https://doi.org/10.1111/bjet.12152
[207] J. Francom. Rfid: a Survey of Ethical and Privacy Concerns. Issues In Information Systems 8(2) (2007) 336-340. https://doi.org/10.48009/2_iis_2007_336-340 DOI: https://doi.org/10.48009/2_iis_2007_336-340
[208] P. Wisniewski, J. Vitak, X. Page, B. Knijnenburg, Y. Wang, C. Fiesler. In whose best interest? Exploring the real, potential, and imagined ethical concerns in privacy-focused agenda. CSCW 2017 - Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (2017) 377-382. https://doi.org/10.1145/3022198.3022660 DOI: https://doi.org/10.1145/3022198.3022660
[209] A. Nayarisseri, R. Khandelwal, P. Tanwar, M. Madhavi, D. Sharma, G. Thakur, A. Speck-Planche, S.K. Singh. Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery. Current Drug Targets 22 (2021) 631-655. https://doi.org/10.2174/1389450122999210104205732 DOI: https://doi.org/10.2174/1389450122999210104205732
[210] V.G. Maltarollo, J.C. Gertrudes, P.R. Oliveira, K.M. Honorio. Applying machine learning techniques for ADME-Tox prediction: A review. Expert Opinion on Drug Metabolism and Toxicology 11 (2015) 259-271. https://doi.org/10.1517/17425255.2015.980814 DOI: https://doi.org/10.1517/17425255.2015.980814
[211] Y. Bidault. A flexible approach for optimising in silico ADME/Tox characterisation of lead candidates. Expert Opinion on Drug Metabolism and Toxicology 2 (2006) 157-168. https://doi.org/10.1517/17425255.2.1.157 DOI: https://doi.org/10.1517/17425255.2.1.157
[212] J.G.M. Bessems, L. Geraets. Proper knowledge on toxicokinetics improves human hazard testing and subsequent health risk characterisation. A case study approach. Regulatory Toxicology and Pharmacology 67 (2013) 325-334. https://doi.org/10.1016/j.yrtph.2013.08.010 DOI: https://doi.org/10.1016/j.yrtph.2013.08.010
[213] S.N. Deftereos, C. Andronis, E.J. Friedla, A. Persidis, A. Persidis. Drug repurposing and adverse event prediction using high-throughput literature analysis. Wiley Interdisciplinary Reviews: Systems Biology and Medicine 3 (2011) 323-334. https://doi.org/10.1002/wsbm.147 DOI: https://doi.org/10.1002/wsbm.147
[214] L. Mucke. EC‐03‐04: Tau‐Dependent Signaling and Neuronal Network Hyperexcitability. Alzheimer’s & Dementia 12 (2016) P269. https://doi.org/10.1016/j.jalz.2016.06.2382 DOI: https://doi.org/10.1016/j.jalz.2016.06.2382
[215] F. Farouk, R. Shamma. Chemical structure modifications and nano-technology applications for improving ADME-Tox properties, a review. Archiv Der Pharmazie 352 (2019) 1800213. https://doi.org/10.1002/ardp.201800213 DOI: https://doi.org/10.1002/ardp.201800213
[216] G. Szakács, A. Váradi, C. Özvegy-Laczka, B. Sarkadi. The role of ABC transporters in drug absorption, distribution, metabolism, excretion and toxicity (ADME-Tox). Drug Discovery Today 13 (2008) 379-393. https://doi.org/10.1016/j.drudis.2007.12.010 DOI: https://doi.org/10.1016/j.drudis.2007.12.010
[217] C. Kim, J. Jeong, J. Choi. Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data. Chemical Research in Toxicology 35 (2022) 2219-2226. https://doi.org/10.1021/acs.chemrestox.2c00189 DOI: https://doi.org/10.1021/acs.chemrestox.2c00189
[218] Y.O. Lee, Y.J. Kim. The Effect of Resampling on Data-imbalanced Conditions for Prediction towards Nuclear Receptor Profiling Using Deep Learning. Molecular Informatics 39 (2020) 1900131. https://doi.org/10.1002/minf.201900131 DOI: https://doi.org/10.1002/minf.201900131
[219] L. Tao, P. Zhang, C. Qin, S.Y. Chen, C. Zhang, Z. Chen, F. Zhu, S.Y. Yang, Y.Q. Wei, Y.Z. Chen. Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools. Advanced Drug Delivery Reviews 86 (2015) 83-100. https://doi.org/10.1016/j.addr.2015.03.014 DOI: https://doi.org/10.1016/j.addr.2015.03.014
[220] X. Sun, J. Zhu, B. Chen, H. You, H. Xu. A feature transferring workflow between data-poor compounds in various tasks. PLoS ONE 17 (2022) e0266088. https://doi.org/10.1371/journal.pone.0266088 DOI: https://doi.org/10.1371/journal.pone.0266088
[221] L. Alzubaidi, J. Bai, A. Al-Sabaawi, J. Santamaría, A.S. Albahri, B.S.N. Al-dabbagh, M.A. Fadhel, M. Manoufali, J. Zhang, A.H. Al-Timemy, Y. Duan, A. Abdullah, L. Farhan, Y. Lu, A. Gupta, F. Albu, A. Abbosh, Y. Gu. A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications. Journal of Big Data 10 (2023) 46. https://doi.org/10.1186/s40537-023-00727-2 DOI: https://doi.org/10.1186/s40537-023-00727-2
[222] D.A. Dablain, C. Bellinger, B. Krawczyk, D.W. Aha, N. V. Chawla. Interpretable ML for Imbalanced Data. ArXiv (2022). http://arxiv.org/abs/2212.07743
[223] Y. Chen, R. Calabrese, B. Martin-Barragan. Interpretable machine learning for imbalanced credit scoring datasets. European Journal of Operational Research 312 (2024) 357-372. https://doi.org/10.1016/j.ejor.2023.06.036 DOI: https://doi.org/10.1016/j.ejor.2023.06.036
[224] C.H. Chang, S. Tan, B. Lengerich, A. Goldenberg, R. Caruana. How Interpretable and Trustworthy are GAMs? Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2021) 95-105. https://doi.org/10.1145/3447548.3467453 DOI: https://doi.org/10.1145/3447548.3467453
[225] Y. Liu, X. Yu. Farthest Point Sampling in Property Designated Chemical Feature Space as a General Strategy for Enhancing the Machine Learning Model Performance for Small Scale Chemical Dataset. (2024). http://arxiv.org/abs/2404.11348 DOI: https://doi.org/10.20517/jmi.2025.10
[226] N.C. Iovanac, B.M. Savoie. Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment. Journal of Physical Chemistry A 123 (2019) 4295-4302. https://doi.org/10.1021/acs.jpca.9b01398 DOI: https://doi.org/10.1021/acs.jpca.9b01398
[227] D. Lemm, G.F. von Rudorff, O.A. von Lilienfeld. Improved decision making with similarity based machine learning. (2022). http://arxiv.org/abs/2205.05633
[228] D. Wade, A. Wilson, A. Reddy, R. Bharadwaj. Validating machine-learned diagnostic classifiers in safety critical applications with imbalanced populations. Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM (2018). https://doi.org/10.36001/phmconf.2018.v10i1.192 DOI: https://doi.org/10.36001/phmconf.2018.v10i1.192
[229] J.D. Walker, L. Carlsen, J. Jaworska. Improving opportunities for regulatory acceptance of QSARs: The importance of model domain, uncertainty, validity and predictability. QSAR and Combinatorial Science 22 (2003) 346-350. https://doi.org/10.1002/qsar.200390024 DOI: https://doi.org/10.1002/qsar.200390024
[230] Y. Wang, K. Wang, C. Zhang. Applications of artificial intelligence/machine learning to high-performance composites. Composites Part B: Engineering 285 (2024) 111740. https://doi.org/10.1016/j.compositesb.2024.111740 DOI: https://doi.org/10.1016/j.compositesb.2024.111740
[231] J.P. Janet, L. Mervin, O. Engkvist. Artificial intelligence in molecular de novo design: Integration with experiment. Current Opinion in Structural Biology 80 (2023). https://doi.org/10.1016/j.sbi.2023.102575 DOI: https://doi.org/10.1016/j.sbi.2023.102575
[232] S. Capponi, K.G. Daniels. Harnessing the power of artificial intelligence to advance cell therapy. Immunological Reviews 320 (2023) 147-165. https://doi.org/10.1111/imr.13236 DOI: https://doi.org/10.1111/imr.13236
[233] Y. Hu, W. Li, D. Wright, O. Aydin, D. Wilson, O. Maher, M. Raad. Artificial Intelligence Approaches. Geographic Information Science & Technology Body of Knowledge 2019 (2019) 102575. https://doi.org/10.22224/gistbok/2019.3.4 DOI: https://doi.org/10.22224/gistbok/2019.3.4
[234] J.E.T. Taylor, G.W. Taylor. Artificial cognition: How experimental psychology can help generate explainable artificial intelligence. Psychonomic Bulletin and Review 28 (2021) 454-475. https://doi.org/10.3758/s13423-020-01825-5 DOI: https://doi.org/10.3758/s13423-020-01825-5
[235] F. Gossen, T. Margaria, B. Steffen. Formal Methods Boost Experimental Performance for Explainable AI. IT Professional 23 (2021) 8-12. https://doi.org/10.1109/MITP.2021.3123495 DOI: https://doi.org/10.1109/MITP.2021.3123495
[236] D. Hemment, D. Murray-Rust, V. Belle, R. Aylett, ... Experiential AI: A transdisciplinary framework for legibility and agency in AI. ArXiv Preprint ArXiv … (2023). https://doi.org/10.48550/arXiv.2306.00635
[237] M.T. Hosain, J.R. Jim, M.F. Mridha, M.M. Kabir. Explainable AI approaches in deep learning: Advancements, applications and challenges. Computers and Electrical Engineering 117 (2024) 109246. https://doi.org/10.1016/j.compeleceng.2024.109246 DOI: https://doi.org/10.1016/j.compeleceng.2024.109246
[238] Y. Cao, J. Romero, A. Aspuru-Guzik. Potential of quantum computing for drug discovery. IBM Journal of Research and Development 62 (2018) 6:1-6:20. https://doi.org/10.1147/JRD.2018.2888987 DOI: https://doi.org/10.1147/JRD.2018.2888987
[239] M. Avramouli, I.K. Savvas, A. Vasilaki, G. Garani. Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery. Electronics (Switzerland) 12 (2023) 2402. https://doi.org/10.3390/electronics12112402 DOI: https://doi.org/10.3390/electronics12112402
[240] S.K. Kandula, N. Katam, P.R. Kangari, A. Hijmal, R. Gurrala, M. Mahmoud. Quantum Computing Potentials for Drug Discovery. Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023 (2023) 1467-1473. https://doi.org/10.1109/CSCI62032.2023.00240 DOI: https://doi.org/10.1109/CSCI62032.2023.00240
[241] P.H. Wang, J.H. Chen, Y.Y. Yang, C. Lee, Y.J. Tseng. Recent Advances in Quantum Computing for Drug Discovery and Development. IEEE Nanotechnology Magazine 17 (2023) 26-30. https://doi.org/10.1109/MNANO.2023.3249499 DOI: https://doi.org/10.1109/MNANO.2023.3249499
[242] K. Louhichi, I. Abdelghani, H. Jdidi, Y. Boukhris, B. Roch, C. François, M. Toumi, A. Bakhutashvili. MSR8 Revolutionizing Drug Discovery and Preclinical Research Via Artificial Intelligence: A Targeted Literature Review. Value in Health 25 (2022) S518-S519. https://doi.org/10.1016/j.jval.2022.04.1215 DOI: https://doi.org/10.1016/j.jval.2022.04.1215
[243] N. Stephenson, E. Shane, J. Chase, J. Rowland, D. Ries, N. Justice, J. Zhang, L. Chan, R. Cao. Survey of Machine Learning Techniques in Drug Discovery. Current Drug Metabolism 20 (2018) 185-193. https://doi.org/10.2174/1389200219666180820112457 DOI: https://doi.org/10.2174/1389200219666180820112457
[244] M. Baranwal, A. Magner, P. Elvati, J. Saldinger, A. Violi, A. Violi, A.O. Hero. A deep learning architecture for metabolic pathway prediction. Bioinformatics 36 (2020) 2547-2553. https://doi.org/10.1093/bioinformatics/btz954 DOI: https://doi.org/10.1093/bioinformatics/btz954
[245] K. Sasahara, M. Shibata, H. Sasabe, T. Suzuki, K. Takeuchi, K. Umehara, E. Kashiyama. Predicting drug metabolism and pharmacokinetics features of in-house compounds by a hybrid machine-learning model. Drug Metabolism and Pharmacokinetics 39 (2021) 100395. https://doi.org/10.1016/j.dmpk.2021.100395 DOI: https://doi.org/10.1016/j.dmpk.2021.100395
Published
Issue
Section
License
Copyright (c) 2025 Magesh Venkataraman, Gopi Chand Rao, Jeevan Karthik Madavareddi , Srinivas Rao Maddi
This work is licensed under a Creative Commons Attribution 4.0 International License.
