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Related Experiment Video

Updated: Jan 15, 2026

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

Generative AI for navigating synthesizable chemical space.

Wenhao Gao¹, Shitong Luo², Connor W Coley^1,2

¹Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.

Proceedings of the National Academy of Sciences of the United States of America

|October 6, 2025

Summary

This summary is machine-generated.

SynFormer generates novel molecules with viable synthetic routes, ensuring chemical designs are practical. This generative framework enhances both local and global chemical space exploration for drug discovery and materials science.

Keywords:

deep learning generative AI molecular design synthetic accessibility

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Area of Science:

Computational chemistry
Machine learning in chemistry

Background:

Generative models are crucial for exploring chemical space.
Ensuring synthetic tractability is a key challenge in molecular design.

Purpose of the Study:

Introduce SynFormer, a novel generative framework for synthesizable chemical space exploration.
Develop a model that generates molecules with feasible synthetic pathways.

Main Methods:

Utilized a scalable transformer architecture.
Incorporated a diffusion module for building block selection.
Applied the framework to local and global chemical space exploration tasks.

Main Results:

SynFormer successfully generates synthesizable molecular analogs.
The model demonstrates effectiveness in identifying optimal molecules via a property prediction oracle.
Performance scales with increased computational resources.

Conclusions:

SynFormer offers an efficient approach to synthesizable molecular design.
The framework has broad applications in drug discovery and materials science.
Openly available code and models facilitate further research.