330 citations · 2.7k across the 74 of their papers we have counts for
32 papers · 1 filter
GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem
Rui-Xi Wang, Runzhong Wang, Connor W. Coley
Predicting tandem mass spectra (MS/MS) from molecular structures represents a central task in analytical chemistry with direct relevance to clinical metabolomics, systems biology,…
MassSpecGym in the Wild: Uncovering and Correcting Evaluation Pitfalls in AI-Driven Molecule Discovery
Hongxuan Liu, Roman Bushuiev, Ivy Lightheart +12
Reliable benchmarking is critical for developing machine learning models for tandem mass spectrometry (MS/MS) based molecule discovery. Subtle issues in experimental design and mod…
FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time
Montgomery Bohde, Hongxuan Liu, Mrunali Manjrekar +4
Tandem mass spectrometry is prominent in scientific discovery workflows for identifying unknown small molecules, yet high-throughput structural elucidation remains challenging. Whi…
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…
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…
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…
Generative Artificial Intelligence for Navigating Synthesizable Chemical Space
Wenhao Gao, Shitong Luo, Connor W. Coley
We introduce SynFormer, a generative modeling framework designed to efficiently explore and navigate synthesizable chemical space. Unlike traditional molecular generation approache…
Batched Bayesian optimization by maximizing the probability of including the optimum
Jenna Fromer, Runzhong Wang, Mrunali Manjrekar +3
Batched Bayesian optimization (BO) can accelerate molecular design by efficiently identifying top-performing compounds from a large chemical library. Existing acquisition strategie…
OpenChemIE: An Information Extraction Toolkit For Chemistry Literature
Vincent Fan, Yujie Qian, Alex Wang +3
Information extraction from chemistry literature is vital for constructing up-to-date reaction databases for data-driven chemistry. Complete extraction requires combining informati…
Beyond Major Product Prediction: Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset
Joonyoung F. Joung, Mun Hong Fong, Jihye Roh +3
Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery. While several machine learning models…
Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks
Yanqiao Zhu, Jeehyun Hwang, Keir Adams +10
Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design. While Graph Neural Networks (GNNs) are e…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
A graph representation of molecular ensembles for polymer property prediction
Matteo Aldeghi, Connor W. Coley
Synthetic polymers are versatile and widely used materials. Similar to small organic molecules, a large chemical space of such materials is hypothetically accessible. Computational…
Bringing Atomistic Deep Learning to Prime Time
Nathan C. Frey, Siddharth Samsi, Bharath Ramsundar +2
Artificial intelligence has not yet revolutionized the design of materials and molecules. In this perspective, we identify four barriers preventing the integration of atomistic dee…
Scalable Geometric Deep Learning on Molecular Graphs
Nathan C. Frey, Siddharth Samsi, Joseph McDonald +3
Deep learning in molecular and materials sciences is limited by the lack of integration between applied science, artificial intelligence, and high-performance computing. Bottleneck…
Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction
Zhengkai Tu, Connor W. Coley
Synthesis planning and reaction outcome prediction are two fundamental problems in computer-aided organic chemistry for which a variety of data-driven approaches have emerged. Natu…
Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations
Keir Adams, Lagnajit Pattanaik, Connor W. Coley
Molecular chirality, a form of stereochemistry most often describing relative spatial arrangements of bonded neighbors around tetrahedral carbon centers, influences the set of 3D c…
Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design
Wenhao Gao, Rocío Mercado, Connor W. Coley
Molecular design and synthesis planning are two critical steps in the process of molecular discovery that we propose to formulate as a single shared task of conditional synthetic p…
Differentiable Scaffolding Tree for Molecular Optimization
Tianfan Fu, Wenhao Gao, Cao Xiao +3
The structural design of functional molecules, also called molecular optimization, is an essential chemical science and engineering task with important applications, such as drug d…