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Connor Coley

5 accepted papers

2026

FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time

Montgomery Bohde, Hongxuan Liu, Mrunali Manjrekar, Magdalena Lederbauer, Shuiwang Ji, Runzhong Wang +1
ICML 2026poster

Tandem mass spectrometry is prominent in scientific discovery workflows for identifying unknown small molecules, yet high-throughput structural elucidation remains challenging. While recent autoregressive and graph diffusion models have shown promise in *de novo* elucidation, performance remains lim…

Cited by 0Source ↗Scholar ↗
2021

Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction

Hangrui Bi, Hengyi Wang, Chence Shi, Connor Coley, Jian Tang, Hongyu Guo
ICML 2021spotlight

Reliably predicting the products of chemical reactions presents a fundamental challenge in synthetic chemistry. Existing machine learning approaches typically produce a reaction product by sequentially forming its subparts or intermediate molecules. Such autoregressive methods, however, not only req…

Cited by 32Source ↗PDF ↗Scholar ↗
2020

Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning

Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu +6
ICML 2020poster

Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in generative modeling of novel chemical structures. However, current generative approaches exhibit a significant challenge: they do not ensure that the proposed molecular…

Cited by 175Source ↗PDF ↗code ↗Scholar ↗
2019

Retrosynthesis Prediction with Conditional Graph Logic Network

Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, Le Song
NeurIPS 2019poster

Retrosynthesis is one of the fundamental problems in organic chemistry. The task is to identify reactants that can be used to synthesize a specified product molecule. Recently, computer-aided retrosynthesis is finding renewed interest from both chemistry and computer science communities. Most existi…

Cited by 252Source ↗PDF ↗code ↗Scholar ↗
2017

Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network

Wengong Jin, Connor Coley, Regina Barzilay, Tommi Jaakkola
NeurIPS 2017poster

The prediction of organic reaction outcomes is a fundamental problem in computational chemistry. Since a reaction may involve hundreds of atoms, fully exploring the space of possible transformations is intractable. The current solution utilizes reaction templates to limit the space, but it suffers f…

Cited by 388Source ↗PDF ↗code ↗Scholar ↗