Morph is a flexible-size 3D molecular generative model using unbalanced optimal transport on geometric graphs that matches fixed-size SOTA performance while enabling out-of-distribution generation.
Libinvent: Reaction-based generative scaffold decoration for in silico library design.Journal of Chemical Information and Modeling, 62(9):2046– 2063, 2022
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Uncertainty-aware RL for chemical language models raises true hit rate from 0.5 to 0.75 by favoring low-uncertainty regions during optimization.
citing papers explorer
-
Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport
Morph is a flexible-size 3D molecular generative model using unbalanced optimal transport on geometric graphs that matches fixed-size SOTA performance while enabling out-of-distribution generation.
-
Uncertainty-aware reinforcement learning for chemical language models
Uncertainty-aware RL for chemical language models raises true hit rate from 0.5 to 0.75 by favoring low-uncertainty regions during optimization.