On the ability of machine learning methods to discover novel scaffolds

Jagdev, Rishi and Finn, Paul W. and Madsen, Thomas Bruun (2022) On the ability of machine learning methods to discover novel scaffolds. Journal of Molecular Modeling, 29 (22). ISSN 0948-5023

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Abstract

The recent advances in the application of machine learning to drug discovery have made it a “hot topic” for research, with hundreds of academic groups and companies integrating machine learning into their drug discovery projects. Nevertheless, there remains great uncertainty regarding the most appropriate ways to evaluate the relative performance of these powerful methods against more traditional cheminformatics approaches, and many pitfalls remain for the unwary. In 2020, researchers at MIT [Stokes, J.M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N.M., MacNair, C.R., French, S., Carfrae, L.A., Bloom-Ackermann, Z., et al.: A deep learning approach to antibiotic discovery. Cell 180(4), 688–702(2020)] reported the discovery of a new compound with antibacterial activity, halicin, through the use of a neural network machine learning method. A robust ability to identify new active chemotypes through computational methods would be very useful.

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