28 citations · 106 across the 10 of their papers we have counts for
10 papers
Generative Active Learning for the Search of Small-molecule Protein Binders
Maksym Korablyov, Cheng-Hao Liu, Moksh Jain +31
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a si…
SE(3)-Stochastic Flow Matching for Protein Backbone Generation
Avishek Joey Bose, Tara Akhound-Sadegh, Guillaume Huguet +7
The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly. Toward this goal, we introduce FoldFlow, a series of novel…
Thompson sampling for improved exploration in GFlowNets
Jarrid Rector-Brooks, Kanika Madan, Moksh Jain +5
Generative flow networks (GFlowNets) are amortized variational inference algorithms that treat sampling from a distribution over compositional objects as a sequential decision-maki…
Learning GFlowNets from partial episodes for improved convergence and stability
Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov +6
Generative flow networks (GFlowNets) are a family of algorithms for training a sequential sampler of discrete objects under an unnormalized target density and have been successfull…
RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro
Paul Bertin, Jarrid Rector-Brooks, Deepak Sharma +17
For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dos…
Properties of Minimizing Entropy
Xu Ji, Lena Nehale-Ezzine, Maksym Korablyov
Compact data representations are one approach for improving generalization of learned functions. We explicitly illustrate the relationship between entropy and cardinality, both mea…
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation
Emmanuel Bengio, Moksh Jain, Maksym Korablyov +2
This paper is about the problem of learning a stochastic policy for generating an object (like a molecular graph) from a sequence of actions, such that the probability of generatin…
DEUP: Direct Epistemic Uncertainty Prediction
Salem Lahlou, Moksh Jain, Hadi Nekoei +5
Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes with more evidence. While existing work focuses on using the variance of the Bayesian post…
RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design
Cheng-Hao Liu, Maksym Korablyov, Stanisław Jastrzębski +3
De novo molecule generation often results in chemically unfeasible molecules. A natural idea to mitigate this problem is to bias the search process towards more easily synthesizabl…
Capsule networks for low-data transfer learning
Andrew Gritsevskiy, Maksym Korablyov
We propose a capsule network-based architecture for generalizing learning to new data with few examples. Using both generative and non-generative capsule networks with intermediate…