1 code implementation • 23 May 2022 • Paul Scherer, Thomas Gaudelet, Alison Pouplin, Alice Del Vecchio, Suraj M S, Oliver Bolton, Jyothish Soman, Jake P. Taylor-King, Lindsay Edwards
Active learning (AL) is a sub-field of ML focused on the development of methods to iteratively and economically acquire data through strategically querying new data points that are the most useful for a particular task.
1 code implementation • 7 Feb 2022 • Paul Bertin, Jarrid Rector-Brooks, Deepak Sharma, Thomas Gaudelet, Andrew Anighoro, Torsten Gross, Francisco Martinez-Pena, Eileen L. Tang, Suraj M S, Cristian Regep, Jeremy Hayter, Maksym Korablyov, Nicholas Valiante, Almer van der Sloot, Mike Tyers, Charles Roberts, Michael M. Bronstein, Luke L. Lairson, Jake P. Taylor-King, Yoshua Bengio
For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges.
no code implementations • 9 Dec 2020 • Thomas Gaudelet, Ben Day, Arian R. Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy B. R. Hayter, Richard Vickers, Charles Roberts, Jian Tang, David Roblin, Tom L. Blundell, Michael M. Bronstein, Jake P. Taylor-King
Graph Machine Learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types.
1 code implementation • 9 Jun 2020 • Jake P. Taylor-King, Cristian Regep, Jyothish Soman, Flawnson Tong, Catalina Cangea, Charlie Roberts
Dynamic Distribution Decomposition (DDD) was introduced in Taylor-King et.