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
On-demand
In this webinar, Jeff Blaney (Senior Director of Discovery Chemistry, Genentech), Darren Green (Head of Cheminformatics & Data Science, GlaxoSmithKline), Julian Levell (Head of Discovery, New Equilibrium Biosciences), Matthew Segall (CEO, Optibrium) discuss the state of AI in early drug discovery from hit to preclinical candidate and share their experiences with and expectations of AI, including predictive modelling, synthesis prediction, and generative chemistry. Hear about the successes of AI drug discovery and an outlook on what AI needs to achieve to really transform the industry.
On-demand
In this webinar, we demonstrate how Cerella™ (AI drug discovery software) highlights new opportunities and guides more efficient compound optimisation.
