Fragflex generates valid and unique molecules by adding chemical fragments
FragFlex: Fragment-Based Flexible Molecular Generation for Exploring Vast Chemical Space
Machine Learning
Summary
Discovering new drugs is hard because there are so many possible molecules to try. The authors created FragFlex, a method that builds molecules by adding and removing parts called fragments instead of single atoms. This method learns how to join fragments correctly and makes sure the molecules make chemical sense. Their tests show FragFlex can create many unique and valid molecules, helping explore new chemical possibilities efficiently.
What this means in practice
- •For pharmaceutical chemists: Generate new candidate molecules with diverse structures and high chemical validity for drug development projects.
- •For chemical informatics teams: Create large libraries of unique and chemically valid molecules for virtual screening and property prediction workflows.
Authors
Sihyun Park, David Ryan Koes
Abstract
Drug discovery requires efficient exploration of an astronomically large chemical space. Molecular graph diffusion models offer a promising approach to this task, but many require graph size to be fixed before generation. Although atom-level insertion and deletion can enable flexible-size generation, jointly modeling molecular size, atom types, and bonds while maintaining chemical validity remains challenging. We introduce FragFlex, a flexible-length discrete diffusion model that performs node insertion and deletion at the fragment level. During denoising, FragFlex jointly learns the size, fragment identities, and inter-fragment connectivity of the fragment graph. A neural attachment-site predictor and valence-aware assembler then convert the generated fragment graph into an atom-level molecule. In large-scale experiments generating 10 million molecules, FragFlex achieves near-perfect chemical validity while maintaining high uniqueness, demonstrating a strong balance between the two.