Publications and Presentations

Practical Applications of Deep Learning to Impute Heterogeneous Drug Discovery Data

Practical Applications of Deep Learning to Impute Heterogeneous Drug Discovery Data

Apr 29, 2020

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

This article outlines practical applications of deep learning on drug discovery data. It introduces some of the research behind our Cerella technology.

practical applications of deep learning


Contemporary deep learning approaches still struggle to bring a useful improvement in the field of drug discovery due to the challenges of sparse, noisy and heterogeneous data that are typically encountered in this context. We use a state-of-the-art deep learning method, Alchemite™, to impute data from drug discovery projects, including multi-target biochemical activities, phenotypic activities in cell-based assays, and a variety of absorption, distribution, metabolism, and excretion (ADME) endpoints. The resulting model gives excellent predictions for activity and ADME endpoints, offering an average increase in R² of 0.22 versus quantitative structure-activity relationship methods. The model accuracy is robust to combining data across uncorrelated endpoints and projects with different chemical spaces, enabling a single model to be trained for all compounds and endpoints. We demonstrate improvements in accuracy on the latest chemistry and data when updating models with new data as an ongoing medicinal chemistry project progresses.

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Cerella™ is a unique artificial intelligence platform which supports medicinal chemists and other discovery scientists, escalating the success rate and advances small molecule drug discovery from working with early hits to nominating preclinical candidates. 

Cerella’s AI platform is proven to overcome limitations in drug discovery data, confidently deliver results, seamlessly integrate with your med chem software platforms and help you and your colleagues increase the success rate of your projects.