March 27, 2024, 4:43 a.m. | Ilia Igashov, Arne Schneuing, Marwin Segler, Michael Bronstein, Bruno Correia

cs.LG updates on arXiv.org arxiv.org

arXiv:2308.16212v2 Announce Type: replace-cross
Abstract: Retrosynthesis planning is a fundamental challenge in chemistry which aims at designing reaction pathways from commercially available starting materials to a target molecule. Each step in multi-step retrosynthesis planning requires accurate prediction of possible precursor molecules given the target molecule and confidence estimates to guide heuristic search algorithms. We model single-step retrosynthesis planning as a distribution learning problem in a discrete state space. First, we introduce the Markov Bridge Model, a generative framework aimed to …

abstract algorithms arxiv challenge chemistry confidence cs.lg designing guide markov materials modeling molecules planning prediction q-bio.bm q-bio.qm search search algorithms type

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Codec Avatars Research Engineer

@ Meta | Pittsburgh, PA