activity
20172026
most citedPredicting Organic Reaction Outcomes with Weisfeiler-Lehman Network

191 citations · 1.1k across the 63 of their papers we have counts for

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Showing physics.chem-phShow all

8 papers · 1 filter

physics.chem-ph2026

Harnessing AtomisticSkills for Agentic Atomistic Research

Bowen Deng, Bohan Li, Matthew Cox +20

Computational materials science and chemistry span vast knowledge domains and fractured software ecosystems. Although large language models (LLMs) have demonstrated research capabi…

physics.chem-ph2024

Revealing the Relationship Between Publication Bias and Chemical Reactivity with Contrastive Learning

Wenhao Gao, Priyanka Raghavan, Ron Shprints +1

A synthetic method's substrate tolerance and generality are often showcased in a "substrate scope" table. However, substrate selection exhibits a frequently discussed publication b…

physics.chem-ph2022

Machine learning-guided computational screening of new bio-orthogonal click reactions

Thijs Stuyver, Connor Coley

Bio-orthogonal click chemistry has become an indispensable part of the biochemist's toolbox. Despite the wide variety of applications that have been developed in recent years, only…

physics.chem-ph2022★ 5 cited

Reaction profiles for quantum chemistry-computed [3 + 2] cycloaddition reactions

Thijs Stuyver, Kjell Jorner, Connor Coley

Bio-orthogonal click chemistry based on [3 + 2] dipolar cycloadditions has had a profound impact on the field of biochemistry and significant effort has been devoted to identify pr…

physics.chem-ph2022★ 8 cited

Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design

Keir Adams, Connor W. Coley

Shape-based virtual screening is widely employed in ligand-based drug design to search chemical libraries for molecules with similar 3D shapes yet novel 2D chemical structures comp…

physics.chem-ph2021

Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability and interpretability

Thijs Stuyver, Connor W. Coley

There is a perceived dichotomy between structure-based and descriptor-based molecular representations used for predictive chemistry tasks. Here, we study the performance, generaliz…