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Found 9 papers, 5 papers with code
SE(3)-Stochastic Flow Matching for Protein Backbone Generation
The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly.
Thompson sampling for improved exploration in GFlowNets
Generative flow networks (GFlowNets) are amortized variational inference algorithms that treat sampling from a distribution over compositional objects as a sequential decision-making problem with a learnable action policy.
Learning GFlowNets from partial episodes for improved convergence and stability
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 successfully used for various probabilistic modeling tasks.
RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro
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.
Properties of Minimizing Entropy
Compact data representations are one approach for improving generalization of learned functions.
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation
Using insights from Temporal Difference learning, we propose GFlowNet, based on a view of the generative process as a flow network, making it possible to handle the tricky case where different trajectories can yield the same final state, e. g., there are many ways to sequentially add atoms to generate some molecular graph.
DEUP: Direct Epistemic Uncertainty Prediction
Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes with more evidence.
RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design
A natural idea to mitigate this problem is to bias the search process towards more easily synthesizable molecules using a proxy for synthetic accessibility.
Capsule networks for low-data transfer learning
We propose a capsule network-based architecture for generalizing learning to new data with few examples.
