Portrait of Simon Blackburn

Simon Blackburn

simon.blackburn@mila.quebec
Senior Applied Research Scientist, Applied Machine Learning Research

Publications

Generative Active Learning for the Search of Small-Molecule Protein Binders
Maksym Korablyov
Cheng-Hao Liu
Moksh Jain
Almer Van Der Sloot
Éric Jolicoeur
Edward Ruediger
Andrei Nica
Emmanuel Bengio
Kostiantyn Lapchevskyi
Daniel St-Cyr
Doris Alexandra Schuetz
Victor Ion Butoi
Jarrid Rector-Brooks
Simon Blackburn
Leo Feng
Hadi Nekoei
Saikrishna Gottipati
Priyesh Vijayan
Prateek Gupta
Ladislav Rampasek … (see 14 more)
Sasikanth Avancha
Pierre-Luc Bacon
William Hamilton
Brooks Paige
Sanchit Misra
Stanislaw Jastrzebski
Bharat Kaul
Doina Precup
José Miguel Hernández-Lobato
Marwin Segler
Michael Bronstein
Anne Marinier
Mike Tyers
Yoshua Bengio
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exh… (see more)ibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.
2023-12-31
arXiv (preprint)
doi.org
arxiv.org
Learning to Navigate the Synthetically Accessible Chemical Space Using Reinforcement Learning
Sai Krishna Gottipati
Boris Sattarov
Sufeng Niu
Yashaswi Pathak
Haoran Wei
Shengchao Liu
Karam J. Thomas
Simon Blackburn
Connor W. Coley
Jian Tang
Sarath Chandar
Yoshua Bengio
Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in deep gen… (see more)erative models. However, current generative approaches exhibit a significant challenge as they do not ensure that the proposed molecular structures can be feasibly synthesized nor do they provide the synthesis routes of the proposed small molecules, thereby seriously limiting their practical applicability. In this work, we propose a novel forward synthesis framework powered by reinforcement learning (RL) for de novo drug design, Policy Gradient for Forward Synthesis (PGFS), that addresses this challenge by embedding the concept of synthetic accessibility directly into the de novo drug design system. In this setup, the agent learns to navigate through the immense synthetically accessible chemical space by subjecting commercially available small molecule building blocks to valid chemical reactions at every time step of the iterative virtual multi-step synthesis process. The proposed environment for drug discovery provides a highly challenging test-bed for RL algorithms owing to the large state space and high-dimensional continuous action space with hierarchical actions. PGFS achieves state-of-the-art performance in generating structures with high QED and penalized clogP. Moreover, we validate PGFS in an in-silico proof-of-concept associated with three HIV targets. Finally, we describe how the end-to-end training conceptualized in this study represents an important paradigm in radically expanding the synthesizable chemical space and automating the drug discovery process.
2020-07-13
ICML (Accept)
doi.org
proceedings.mlr.press