Generative Artificial Intelligence for Navigating Synthesizable Chemical Space
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 reference 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.
Code (1)
Tasks
Drug DiscoveryNavigateProperty PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture
Metal-organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential …
System of Agentic AI for the Discovery of Metal-Organic Frameworks
Generative models and machine learning promise accelerated material discovery in MOFs for CO2 capture and water harvesting but face significant challenges navigating vast chemical spaces while ensuring synthetizability. …
Language ModelingLanguage ModellingLarge Language ModelSynthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors
The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo that can be synthesized in practice. Previous works have leveraged Gener…
Contrastive LearningDrug DiscoveryExploring Synthesizable Chemical Space with Iterative Pathway Refinements
A well-known pitfall of molecular generative models is that they are not guaranteed to generate synthesizable molecules. Existing solutions for this problem often struggle to effectively navigate exponentially large comb…
Augmented Computational Design: Methodical Application of Artificial Intelligence in Generative Design
This chapter presents methodological reflections on the necessity and utility of artificial intelligence in generative design. Specifically, the chapter discusses how generative design processes can be augmented by AI to…
Decision Making