Abstract: By speeding up scientific discovery, automating experimental and computational operations, and enabling sophisticated data-driven insights, artificial intelligence (AI) is drastically changing chemistry research. AI-based methods, such as machine learning and generative models, have shown great promise in the fields of autonomous laboratories, materials discovery, molecular design, and reaction prediction. Notwithstanding these benefits, the increasing use......
Keywords: artificial intelligence, Transforming, optimization, chemistry, sustainable
[1].
Butler, K. T.; Davies, D. W.; Cartwright, H.; Isayev, O.; Walsh, A. Machine Learning For Molecular And Materials Science. Nature 2018, 559, 547–555. Https://Doi.Org/10.1038/S41586-018-0337-2.
[2].
Floridi, L.; Cowls, J.; Beltrametti, M.; Et Al. AI4People—An Ethical Framework For A Good AI Society: Opportunities, Risks, Principles, And Recommendations. Minds Mach. 2018, 28, 689–707. Https://Doi.Org/10.1007/S11023-018-9482-5.
[3].
Schneider, G.; Fechner, U. Computer-Based De Novo Design Of Drug-Like Molecules. Nat. Rev. Drug Discov. 2005, 4, 649–663.
Https://Doi.Org/10.1038/Nrd1799.
[4].
Urbina, F.; Lentzos, F.; Invernizzi, C.; Ekins, S. Dual Use Of Artificial-Intelligence-Powered Drug Discovery. Nat. Mach. Intell. 2022, 4, 189–191. Https://Doi.Org/10.1038/S42256-022-00465-9.
[5].
Jiménez-Luna, J.; Grisoni, F.; Schneider, G. Drug Discovery With Explainable Artificial Intelligence. Nat. Mach. Intell. 2020, 2, 573–584. Https://Doi.Org/10.1038/S42256-020-00236-4..
