Hansch, C., Maloney, P., Fujita, T. & Muir, R. Correlation of biological activity of phenoxyacetic acids with hammett substituent constants and partition coefficients. Nature 194, 178–180 (1962).
Cherkasov, A. et al. QSAR modeling: where have you been? Where are you going to? J. Med. Chem. 57, 4977–5010 (2014).
Article CAS PubMed PubMed Central Google Scholar
Muratov, E. N. et al. QSAR without borders. Chem. Soc. Rev. 49, 3525–3564 (2020).
Article CAS PubMed PubMed Central Google Scholar
Ivakhnenko, A. G. & Lapa, V. G. Cybernetics and Forecasting Techniques (American Elsevier Co, 1967).
Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E. & Svetnik, V. Deep neural nets as a method for quantitative structure-activity relationships. J. Chem. Inf. Model. 55, 263–274 (2015).
Article CAS PubMed Google Scholar
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. & Blaschke, T. The rise of deep learning in drug discovery. Drug Discov. Today 23, 1241–1250 (2018).
Yang, X., Wang, Y., Byrne, R., Schneider, G. & Yang, S. Concepts of artificial intelligence for computer-assisted drug discovery. Chem. Rev. 119, 10520–10594 (2019).
Article CAS PubMed Google Scholar
Jiménez-Luna, J., Grisoni, F. & Schneider, G. Drug discovery with explainable artificial intelligence. Nat. Mach. Intell. 2, 573–584 (2020).
Pandey, M. et al. The transformational role of GPU computing and deep learning in drug discovery. Nat. Mach. Intell. 4, 211–221 (2022).
Bengio, Y., Courville, A. & Vincent, P. Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2012).
Real, E., Aggarwal, A., Huang, Y. & Le, Q. V. Regularized evolution for image classifier architecture search. Preprint at:arXiv https://doi.org/10.48550/arXiv.1802.01548 (2018).
Elsken, T., Metzen, J. H. & Hutter, F. Neural architecture search: a survey.J. Mach. Learn. Res. 20, 1–21 (2019).
Li, X. & Fourches, D. Inductive transfer learning for molecular activity prediction: next-gen QSAR models with MolPMoFiT. J. Cheminform. 12, 27 (2020).
Article CAS PubMed PubMed Central Google Scholar
Xu, Y., Ma, J., Liaw, A., Sheridan, R. P. & Svetnik, V. Demystifying multitask deep neural networks for quantitative structure–activity relationships. J. Chem. Inf. Model. 57, 2490–2504 (2017).
Article CAS PubMed Google Scholar
Moon, C. & Kim, D. Prediction of drug-target interactions through multi-task learning. Sci. Rep. 12, 18323 (2022).
Article CAS PubMed PubMed Central Google Scholar
Fourches, D., Muratov, E. & Tropsha, A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J. Chem. Inf. Model. 50, 1189–1204 (2010).
Article CAS PubMed PubMed Central Google Scholar
Fourches, D. et al. Trust, but verify II: a practical guide to chemogenomics data curation. J. Chem. Inf. Model. 56, 1243–1252 (2016).
Article CAS PubMed PubMed Central Google Scholar
Fourches, D., Muratov, E. & Tropsha, A. Curation of chemogenomics data. Nat. Chem. Biol. 11, 535 (2015).
Article CAS PubMed Google Scholar
Alves, V. M. et al. Curated data in — trustworthy in silico models out: the impact of data quality on the reliability of artificial intelligence models as alternatives to animal testing. Altern. Lab. Anim. 49, 73–82 (2021).
Article PubMed PubMed Central Google Scholar
Tropsha, A. Best practices for QSAR model development, validation, and exploitation. Mol. Inform. 29, 476–488 (2010).
Article CAS PubMed Google Scholar
Golbraikh, A., Muratov, E., Fourches, D. & Tropsha, A. Data set modelability by QSAR. J. Chem. Inf. Model. 54, 1–4 (2014).
Article CAS PubMed PubMed Central Google Scholar
Maggiora, G. M. On outliers and activity cliffs — why QSAR often disappoints. J. Chem. Inf. Model. 46, 1535 (2006).
Article CAS PubMed Google Scholar
Aldeghi, M. et al. Roughness of molecular property landscapes and its impact on modellability. J. Chem. Inf. Model. 62, 4660–4671 (2022).
Article CAS PubMed Google Scholar
Bosc, N. et al. Large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery. J. Cheminform. 11, 4 (2019).
Article PubMed PubMed Central Google Scholar
Varnek, A. & Tropsha, A. Chemoinformatics Approaches to Virtual Screening. https://doi.org/10.1039/9781847558879 (Royal Society of Chemistry, 2008).
Schneider, G. & Fechner, U. Computer-based de novo design of drug-like molecules. Nat. Rev. Drug Discov. 4, 649–663 (2005).
Article CAS PubMed Google Scholar
Popova, M., Isayev, O. & Tropsha, A. Deep reinforcement learning for de novo drug design. Sci. Adv. 4, eaap7885 (2018).
Article CAS PubMed PubMed Central Google Scholar
Schneider, P. et al. Rethinking drug design in the artificial intelligence era. Nat. Rev. Drug Discov. 19, 353–364 (2019).
Schneider, G. Mind and machine in drug design. Nat. Mach. Intell. 1, 128–130 (2019).
Schneider, G. & Clark, D. E. Automated de novo drug design: are we nearly there yet? Angew. Chem. Int. Ed. Engl. 58, 10792–10803 (2019).
Article CAS PubMed Google Scholar
Hartenfeller, M. et al. DOGS: reaction-driven de novo design of bioactive compounds. PLoS Comput. Biol. 8, e1002380 (2012).
Article CAS PubMed PubMed Central Google Scholar
Tong, X. et al. Generative models for de novo drug design. J. Med. Chem. 64, 14011–14027 (2021).
Article CAS PubMed Google Scholar
Moret, M. et al. Leveraging molecular structure and bioactivity with chemical language models for de novo drug design. Nat. Commun. 14, 114 (2023).
Article CAS PubMed PubMed Central Google Scholar
Segler, M. H. S., Kogej, T., Tyrchan, C. & Waller, M. P. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent. Sci. 4, 120–131 (2018).
Article CAS PubMed Google Scholar
Blaschke, T., Olivecrona, M., Engkvist, O., Bajorath, J. & Chen, H. Application of generative autoencoder in de novo molecular design. Mol. Inform. 37, 1700123 (2018).
Putin, E. et al. Reinforced adversarial neural computer for de novo molecular design. J. Chem. Inf. Model. 58, 1194–1204 (2018).
Article CAS PubMed Google Scholar
Atz, K., Grisoni, F. & Schneider, G. Geometric deep learning on molecular representations. Nat. Mach. Intell. 3, 1023–1032 (2021).
Button, A., Merk, D., Hiss, J. A. & Schneider, G. Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis. Nat. Mach. Intell. 1, 307–315 (2019).
Grisoni, F. Chemical language models for de novo drug design: challenges and opportunities. Curr. Opin. Struct. Biol. 79, 102527 (2023).
Article CAS PubMed Google Scholar
Kotsias, P. C. et al. Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks. Nat. Mach. Intell. 2, 254–265 (2020).
Korshunova, M. et al. Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds. Commun. Chem. 5, 129 (2022).
Article PubMed PubMed Central Google Scholar
Baskin, I. I. Is one-shot learning a viable option in drug discovery? Expert Opin. Drug Discov. 14, 601–603 (2019).
Comments (0)