AI3SD Video: Machines Learning Chemistry

Reinvigorated by algorithmic developments, faster hardware and large data sets, machine learning is pervading many aspects of chemistry. We present two examples from our recent studies, one in the area of drug discovery, the other focused on protein spectroscopy. In the first, we consider pharmaceutical lead discovery as active search in a space of labelled graphs [1]. We extend a recent data-driven adaptive Markov chain approach, and evaluate it on a focused drug design problem, where we search for an antagonist of an 훂v integrin, the target protein that belongs to a group of Arg-Gly-Asp integrin receptors. In the second example, we present a novel machine learning protocol that uses a few key structural descriptors to predict amide I IR spectra of proteins and agrees well with experiment [2]. Its transferability enabled us to distinguish protein secondary structures, probe atomic structure variations with temperature, and monitor protein folding.[1] Oglic, D., Oatley, S.A., Macdonald, S.J.F., McInally, T., Garnett, R., Hirst, J.D. & Gartner, T. Active search for computer-aided drug design. Mol. Inf., 2018, 37, 1700130. https://doi.org/10.1002/minf.201700130[2] Ye, S., Zhong, K., Zhang, J., Hu, W., Hirst, J.D., Zhang, G., Mukamel, S., Jiang, J. A transferable machine learning protocol for predicting protein amide-I infrared spectra. J. Am. Chem. Soc., 2020, 142, 19071. https://doi.org/10.1021/jacs.0c06530