ArticleProceedings of the National Academy of Sciences of the United States of America2025
Generative AI for navigating synthesizable chemical space.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Computational data as the fuel for AI in chemistry.Chemical science · 2026Review
- Chemical Distance-Based Acceleration of Large Library Docking with ChemSTEP.ACS central science · 2026Article
- Circumventing the Synthesizability Problem in Generative Molecular Design.Journal of chemical information and modeling · 2026Article
- Lessons From Drug Discovery for Cryoprotective Agent Design: An AI-Oriented Perspective.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The (r)evolution of chemical space and molecular modeling: a time-resolved perspective.Journal of computer-aided molecular design · 2026Review
- SyntheMol-RL: a flexible reinforcement learning framework for designing easily synthesizable antibiotics.Molecular systems biology · 2026Article
- A Reinforcement Learning-Guided Genetic Algorithm Integrating Medicinal Chemistry-Inspired Molecular Transformations.Journal of chemical information and modeling · 2026Article
- A Comparative Review of Artificial Intelligence Applications in Small Molecule Versus Peptide Drug Discovery.International journal of molecular sciences · 2026Review
- TANGO: direct optimization of constrained synthesizability for generative molecular design.Nature computational science · 2026Article
- Redefining drug discovery: AI-driven strategies for efficient cancer therapeutics.Frontiers in oncology · 2026Review
- SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models.ArXiv · 2025Article
- Machine learning in chemistry.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We introduce SynFormer, a generative modeling framework designed to efficiently explore and navigate synthesizable chemical space. Unlike traditional molecular generation approaches, we generate synthetic pathways for molecules to ensure that designs are synthetically tractable. By incorporating a scalable transformer architecture and a diffusion module for building block selection, SynFormer surpasses existing models in synthesizable molecular design. We demonstrate SynFormer's effectiveness in two key applications: 1) local chemical space exploration, where the model generates synthesizable analogs of a query molecule, and 2) global chemical space exploration, where the model aims to identify optimal molecules according to a black-box property prediction oracle. Additionally, we demonstrate the scalability of our approach via the improvement in performance as more computational resources become available. With our code and trained models openly available, we hope that SynFormer will find use across applications in drug discovery and materials science.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.