Advances in Activity/Property Prediction from Chemical Structures

References

• Mak, K. K.; Pichika, M. R. Artificial Intelligence in Drug Development: Present Status and Future Prospects. Drug. Discov. Today 2019, 24, 773–780. DOI: 10.1016/j.drudis.2018.11.014.
   PubMed Web of Science ®Google Scholar
• Staszak, M.; Staszak, K.; Wieszczycka, K.; Bajek, A.; Roszkowski, K.; Tylkowski, B. Machine Learning in Drug Design: Use of Artificial Intelligence to Explore the Chemical Structure–Biological Activity Relationship. Wiley Interdisciplinary Reviews: Computational Molecular Science; Wiley: Hoboken, 2021. DOI: 10.1002/wcms.1568.
   Google Scholar
• Nantasenamat, C.; Isarankura-Na-Ayudhya, C.; Naenna, T.; Prachayasittikul, V. A Practical Overview of Quantitative Structure-Activity Relationship. EXCLI J. 2009, 8, 74–88. DOI: 10.17877/DE290R-690.
   Web of Science ®Google Scholar
• Svetnik, V.; Liaw, A.; Tong, C.; Christopher Culberson, J.; Sheridan, R. P.; Feuston, B. P. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. J. Chem. Inf. Comput. Sci. 2003, 43, 1947–1958. DOI: 10.1021/ci034160g.
   PubMedGoogle Scholar
• Baderna, D.; Gadaleta, D.; Lostaglio, E.; Selvestrel, G.; Raitano, G.; Golbamaki, A.; Lombardo, A.; Benfenati, E. New in Silico Models to Predict in Vitro Micronucleus Induction as Marker of Genotoxicity. J. Hazard. Mater. 2020, 385, 121638. DOI: 10.1016/j.jhazmat.2019.121638.
   PubMed Web of Science ®Google Scholar
• Guerra, L. R.; de Souza, A. M. T.; Côrtes, J. A.; Lione, V.; de, O. F.; Castro, H. C.; Alves, G. G. Assessment of Predictivity of Volatile Organic Compounds Carcinogenicity and Mutagenicity by Freeware in Silico Models. Regul. Toxicol. Pharmacol. 2017, 91, 1–8. DOI: 10.1016/j.yrtph.2017.09.030.
   PubMed Web of Science ®Google Scholar
• Lavecchia, A. Machine-Learning Approaches in Drug Discovery: Methods and Applications. Drug. Discov. Today 2015, 20, 318–331. DOI: 10.1016/j.drudis.2014.10.012.
   PubMed Web of Science ®Google Scholar
• Vamathevan, J.; Clark, D.; Czodrowski, P.; Dunham, I.; Ferran, E.; Lee, G.; Li, B.; Madabhushi, A.; Shah, P.; Spitzer, M.; Zhao, S. Applications of Machine Learning in Drug Discovery and Development. Nat. Rev. Drug. Discov. 2019, 18, 463–477. DOI: 10.1038/s41573-019-0024-5.
   PubMed Web of Science ®Google Scholar
• O'Boyle, N. M.; Banck, M.; James, C. A.; Morley, C.; Vandermeersch, T.; Hutchison, G. R. Open Babel: An Open Chemical Toolbox. J. Cheminform. 2011, 3, 33. DOI: 10.1186/1758-2946-3-33.
   PubMed Web of Science ®Google Scholar
• Harrington, P. Statistical Validation of Classification and Calibration Models Using Bootstrapped Latin Partitions. Trends in Analytical Chemistry 2006, 25, 1112–1124.
   Web of Science ®Google Scholar
• Harrington, P. Multiple versus Single Set Validation to Avoid Mistakes. CRC Critical Reviews in Analytical Chemistry 2018, 48, 33–46.
   PubMed Web of Science ®Google Scholar
• Valsecchi, C.; Grisoni, F.; Consonni, V.; Ballabio, D. Consensus versus Individual QSARs in Classification: Comparison on a Large-Scale Case Study. J. Chem. Inf. Model. 2020, 60, 1215–1223. DOI: 10.1021/acs.jcim.9b01057.
   PubMed Web of Science ®Google Scholar
• Meyers, J.; Fabian, B.; Brown, N. De Novo Molecular Design and Generative Models. Drug Discov. Today. 2021, 26, 2707–2715. DOI: 10.1016/j.drudis.2021.05.019This.
   PubMed Web of Science ®Google Scholar
• Lorenz, S.; Amsel, A. K.; Puhlmann, N.; Reich, M.; Olsson, O.; Kümmerer, K. Toward Application and Implementation of in Silico Tools and Workflows within Benign by Design Approaches. ACS Sustain. Chem. Eng. 2021, 9, 12461–12475. DOI: 10.1021/acssuschemeng.1c03070.
   Web of Science ®Google Scholar
• Zhavoronkov, A.; Ivanenkov, Y. A.; Aliper, A.; Veselov, M. S.; Aladinskiy, V. A.; Aladinskaya, A. v.; Terentiev, V. A.; Polykovskiy, D. A.; Kuznetsov, M. D.; Asadulaev, A.; et al. Aspuru-Guzik, A. Deep Learning Enables Rapid Identification of Potent DDR1 Kinase Inhibitors. Nat. Biotechnol. 2019, 37, 1038–1040. DOI: 10.1038/s41587-019-0224-x.
   PubMed Web of Science ®Google Scholar
• Blaschke, T.; Arús-Pous, J.; Chen, H.; Margreitter, C.; Tyrchan, C.; Engkvist, O.; Papadopoulos, K.; Patronov, A. REINVENT 2.0: An AI Tool for de Novo Drug Design. J. Chem. Inf. Model. 2020, 60, 5918–5922. DOI: 10.1021/acs.jcim.0c00915.
   PubMed Web of Science ®Google Scholar
• Bush, J. T.; Pogany, P.; Pickett, S. D.; Barker, M.; Baxter, A.; Campos, S.; Cooper, A. W. J.; Hirst, D.; Inglis, G.; Nadin, A.; et al. A Turing Test for Molecular Generators. J. Med. Chem. 2020, 63, 11964–11971. DOI: 10.1021/acs.jmedchem.0c01148.
   PubMed Web of Science ®Google Scholar
• Segler, M. H. S.; Preuss, M.; Waller, M. P. Planning Chemical Syntheses with Deep Neural Networks and Symbolic AI. Nature 2018, 555, 604–610. DOI: 10.1038/nature25978.
   PubMed Web of Science ®Google Scholar
• Ferrero, E.; Dunham, I.; Sanseau, P. In Silico Prediction of Novel Therapeutic Targets Using Gene-Disease Association Data. J. Transl. Med. 2017, 15, 182. DOI: 10.1186/s12967-017-1285-6.
   PubMed Web of Science ®Google Scholar
• Ng, Y. L.; Salim, C. K.; Chu, J. J. H. Drug Repurposing for COVID-19: Approaches, Challenges and Promising Candidates. Pharmacol. Ther. 2021, 228, 107930. DOI: 10.1016/j.pharmthera.2021.107930.
   PubMed Web of Science ®Google Scholar
• Güngör, T.; Ozleyen, A.; Yılmaz, Y. B.; Siyah, P.; Ay, M.; Durdağı, S.; Tumer, T. B. New Nimesulide Derivatives with Amide/Sulfonamide Moieties: Selective COX-2 Inhibition and Antitumor Effects. Eur. J. Med. Chem. 2021, 221, 113566. DOI: 10.1016/j.ejmech.2021.113566.
   PubMed Web of Science ®Google Scholar
• Catalani, V.; Botha, M.; Corkery, J. M.; Guirguis, A.; Vento, A.; Scherbaum, N.; Schifano, F. The Psychonauts’ Benzodiazepines; Quantitative Structure-Activity Relationship (Qsar) Analysis and Docking Prediction of Their Biological Activity. Pharmaceuticals 2021, 14, 720. DOI: 10.3390/ph14080720.
   Web of Science ®Google Scholar
• Jayasekara, P. S.; Skanchy, S. K.; Kim, M. T.; Kumaran, G.; Mugabe, B. E.; Woodard, L. E.; Yang, J.; Zych, A. J.; Kruhlak, N. L. Assessing the Impact of Expert Knowledge on ICH M7 (Q)SAR Predictions. Is Expert Review Still Needed? Regul. Toxicol. Pharmacol. 2021, 125, 105006. DOI: 10.1016/j.yrtph.2021.105006.
   PubMed Web of Science ®Google Scholar
• Ding, Y. L.; Lyu, Y. C.; Leong, M. K. In Silico Prediction of the Mutagenicity of Nitroaromatic Compounds Using a Novel Two-QSAR Approach. Toxicol. In Vitro 2017, 40, 102–114. DOI: 10.1016/j.tiv.2016.12.013.
   PubMed Web of Science ®Google Scholar
• Food and Drug Administration (FDA). Guidance for Industry: Nonclinical Safety Evaluation of Drug or Biologic Combinations. https://www.fda.gov/media/119657/download (accessed Oct. 8, 2021).
   Google Scholar
• Hasselgren, C.; Bercu, J.; Cayley, A.; Cross, K.; Glowienke, S.; Kruhlak, N.; Muster, W.; Nicolette, J.; Reddy, M. V.; Saiakhov, R.; Dobo, K. Management of Pharmaceutical ICH M7 (Q)SAR Predictions – The Impact of Model Updates. Regul. Toxicol. Pharm. 2020, 118, 104807. DOI: 10.1016/j.yrtph.2020.104807.
   PubMed Web of Science ®Google Scholar
• Food and Drug Administration (FDA). Guidance for Industry: Control of Nitrosamine Impurities in Human Drugs https://www.fda.gov/media/141720/download (accessed Oct. 8, 2021).
   Google Scholar
• Hua, Y.; Shi, Y.; Cui, X.; Li, X. In Silico Prediction of Chemical-Induced Hematotoxicity with Machine Learning and Deep Learning Methods. Mol. Divers. 2021, 25, 1585–1596. DOI: 10.1007/s11030-021-10255-x.
   PubMed Web of Science ®Google Scholar
• Chikowe, I.; Phiri, A. C.; Mbewe, K. P.; Matekenya, D. In-Silico Evaluation of Malawi Essential Medicines and Reactive Metabolites for Potential Drug-Induced Toxicities. BMC Pharmacol. Toxicol. 2021, 22, 36. DOI: 10.1186/s40360-021-00499-6.
   PubMed Web of Science ®Google Scholar
• Food and Drug Administration (FDA). Guidance for Industry: M7(R1) Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk. https://www.fda.gov/media/85885/download (accessed Oct. 8, 2021).
   Google Scholar
• Amberg, A.; Beilke, L.; Bercu, J.; Bower, D.; Brigo, A.; Cross, K. P.; Custer, L.; Dobo, K.; Dowdy, E.; Ford, K. A.; et al. Principles and Procedures for Implementation of ICH M7 Recommended (Q)SAR Analyses. Regul. Toxicol. Pharmacol. 2016, 77, 13–24. DOI: 10.1016/j.yrtph.2016.02.004.
   PubMed Web of Science ®Google Scholar
• Pradeep, P.; Povinelli, R. J.; White, S.; Merrill, S. J. An Ensemble Model of QSAR Tools for Regulatory Risk Assessment. J. Cheminform. 2016, 8, 1–9. DOI: 10.1186/s13321-016-0164-0.
   PubMed Web of Science ®Google Scholar
• Landry, C.; Kim, M. T.; Kruhlak, N. L.; Cross, K. P.; Saiakhov, R.; Chakravarti, S.; Stavitskaya, L. Transitioning to Composite Bacterial Mutagenicity Models in ICH M7 (Q)SAR Analyses. Regul. Toxicol. Pharmacol. 2019, 109, 104488. DOI: 10.1016/j.yrtph.2019.104488.
   PubMed Web of Science ®Google Scholar
• Benigni, R. In Silico Assessment of Genotoxicity. Combinations of Sensitive Structural Alerts Minimize False Negative Predictions for All Genotoxicity Endpoints and Can Single out Chemicals for Which Experimentation Can Be Avoided. Regul. Toxicol. Pharmacol. 2021, 126, 105042. DOI: 10.1016/j.yrtph.2021.105042.
   PubMed Web of Science ®Google Scholar
• Guan, D.; Fan, K.; Spence, I.; Matthews, S. Combining Machine Learning Models of in Vitro and in Vivo Bioassays Improves Rat Carcinogenicity Prediction. Regul. Toxicol. Pharmacol. 2018, 94, 8–15. DOI: 10.1016/j.yrtph.2018.01.008.
   PubMed Web of Science ®Google Scholar
• Pradeep, P.; Judson, R.; DeMarini, D. M.; Keshava, N.; Martin, T. M.; Dean, J.; Gibbons, C. F.; Simha, A.; Warren, S. H.; Gwinn, M. R.; Patlewicz, G. An Evaluation of Existing QSAR Models and Structural Alerts and Development of New Ensemble Models for Genotoxicity Using a Newly Compiled Experimental Dataset. Comput. Toxicol. 2021, 18, 100167. DOI: 10.1016/j.comtox.2021.100167.
   Google Scholar
• Barber, C.; Amberg, A.; Custer, L.; Dobo, K. L.; Glowienke, S.; van Gompel, J.; Gutsell, S.; Harvey, J.; Honma, M.; Kenyon, M. O.; et al. Establishing Best Practise in the Application of Expert Review of Mutagenicity under ICH M7. Regul. Toxicol. Pharmacol. 2015, 73, 367–377. DOI: 10.1016/j.yrtph.2015.07.018.
   PubMed Web of Science ®Google Scholar
• Williams, R. V.; Amberg, A.; Brigo, A.; Coquin, L.; Giddings, A.; Glowienke, S.; Greene, N.; Jolly, R.; Kemper, R.; O'Leary-Steele, C.; et al. It’s Difficult, but Important, to Make Negative Predictions. Regul. Toxicol. Pharmacol. 2016, 76, 79–86. DOI: 10.1016/j.yrtph.2016.01.008.
   PubMed Web of Science ®Google Scholar
• Jolly, R.; Riaz Ahmed, K. B.; Zwickl, C.; Watson, I.; Gombar, V. An Evaluation of In-House and off-the-Shelf In Silico Models: Implications on Guidance for Mutagenicity Assessment. Regul. Toxicol. Pharmacol. 2015, 71, 388–397. DOI: 10.1016/j.yrtph.2015.01.010.
   PubMed Web of Science ®Google Scholar
• Patel, M.; Kranz, M.; Munoz-Muriedas, J.; Harvey, J. S.; Giddings, A.; Swallow, S.; Fellows, M.; Naven, R.; Werner, A. L.; Yeo, D. J.; et al. A Pharma-Wide Approach to Address the Genotoxicity Prediction of Primary Aromatic Amines. Comput. Toxicol. 2018, 7, 27–35. DOI: 10.1016/j.comtox.2018.06.002.
   Google Scholar
• Foster, R. S.; Fowkes, A.; Cayley, A.; Thresher, A.; Werner, A. L. D.; Barber, C. G.; Kocks, G.; Tennant, R. E.; Williams, R. v.; Kane, S.; Stalford, S. A. The Importance of Expert Review to Clarify Ambiguous Situations for (Q)SAR Predictions under ICH M7. Genes Environ. 2020, 42, 27. DOI: 10.1186/s41021-020-00166-y.
   PubMed Web of Science ®Google Scholar
• Amberg, A.; Andaya, R. v.; Anger, L. T.; Barber, C.; Beilke, L.; Bercu, J.; Bower, D.; Brigo, A.; Cammerer, Z.; Cross.; et al. Principles and Procedures for Handling out-of-Domain and Indeterminate Results as Part of ICH M7 Recommended (Q)SAR Analyses. Regul. Toxicol. Pharmacol. 2019, 102, 53–64. DOI: 10.1016/j.yrtph.2018.12.007.
   PubMed Web of Science ®Google Scholar