330 citations · 2.7k across the 74 of their papers we have counts for
12 papers · 1 filter
Efficient and Programmable Exploration of Synthesizable Chemical Space
Shitong Luo, Connor W. Coley
The constrained nature of synthesizable chemical space poses a significant challenge for sampling molecules that are both synthetically accessible and possess desired properties. I…
A Genetic Algorithm for Navigating Synthesizable Molecular Spaces
Alston Lo, Connor W. Coley, Wojciech Matusik
Inspired by the effectiveness of genetic algorithms and the importance of synthesizability in molecular design, we present SynGA, a simple genetic algorithm that operates directly…
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…
Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials
Nicholas Casetti, Dylan Anstine, Olexandr Isayev +1
Reaction mechanism search tools have demonstrated the ability to provide insights into likely products and rate-limiting steps of reacting systems. However, reactions involving sev…
Optimization of Robotic Liquid Handling as a Capacitated Vehicle Routing Problem
Guangqi Wu, Runzhong Wang, Connor W. Coley
We present an optimization strategy to reduce the execution time of liquid handling operations in the context of an automated chemical laboratory. By formulating the task as a capa…
Optimal compound downselection to promote diversity and parallel chemistry
Jenna C. Fromer, Alexandra D. Volkova, Connor W. Coley
Early stage drug discovery and molecular design projects often follow iterative design-make-test cycles. The selection of which compounds to synthesize from all possible candidate…
The impact of conformer quality on learned representations of molecular conformer ensembles
Keir Adams, Connor W. Coley
Training machine learning models to predict properties of molecular conformer ensembles is an increasingly popular strategy to accelerate the conformational analysis of drug-like s…
Electron flow matching for generative reaction mechanism prediction obeying conservation laws
Joonyoung F. Joung, Mun Hong Fong, Nicholas Casetti +3
Central to our understanding of chemical reactivity is the principle of mass conservation, which is fundamental for ensuring physical consistency, balancing equations, and guiding…
Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning
Runzhong Wang, Rui-Xi Wang, Mrunali Manjrekar +1
Molecular machine learning has gained popularity with the advancements of geometric deep learning. In parallel, retrieval-augmented generation has become a principled approach comm…
DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra
Montgomery Bohde, Mrunali Manjrekar, Runzhong Wang +2
Mass spectrometry plays a fundamental role in elucidating the structures of unknown molecules and subsequent scientific discoveries. One formulation of the structure elucidation ta…
Challenging reaction prediction models to generalize to novel chemistry
John Bradshaw, Anji Zhang, Babak Mahjour +3
Deep learning models for anticipating the products of organic reactions have found many use cases, including validating retrosynthetic pathways and constraining synthesis-based mol…
ASKCOS: an open source software suite for synthesis planning
Zhengkai Tu, Sourabh J. Choure, Mun Hong Fong +16
The advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planni…