This article outlines practical applications of deep learning on drug discovery data. It introduces some of the research behind our Cerella technology.
Summary
When looking to apply artificial intelligence in drug discovery, one of the main challenges is around the lack of abundant quality data. This is due to the time and cost of experiments, alongside potential experimental errors. This paper describes Alchemite™, the cutting-edge deep learning method underpinning our Cerella platform, and its success in making predictions even faced with sparse, noisy data. It can impute data across a range of drug discovery projects, from activity to ADME endpoints with excellent accuracy, even when combining data across uncorrelated endpoints and projects with different chemical spaces.
Citation details
B. W. J. Irwin, J. Levell, T. M. Whitehead, M. D. Segall, G. J. Conduit, J. Chem. Inf. Model. 2020, 60, 6, 2848–2857
DOI: 10.1021/acs.jcim.0c00443
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