2.1k citations · 6k across the 29 of their papers we have counts for
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Knowing when to trust machine-learned interatomic potentials
Shams Mehdi, Ilkwon Cho, Olexandr Isayev
Prevailing machine-learned interatomic potential (MLIP) uncertainty-quantification methods rely on ensembles of independently trained backbones. These methods scale unfavorably wit…
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…
Applications of Modular Co-Design for De Novo 3D Molecule Generation
Danny Reidenbach, Filipp Nikitin, Olexandr Isayev +1
De novo 3D molecule generation is a pivotal task in drug discovery. However, many recent geometric generative models struggle to produce high-quality 3D structures, even if they ma…
GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generation
Filipp Nikitin, Ian Dunn, David Ryan Koes +1
Deep generative models have shown significant promise in generating valid 3D molecular structures, with the GEOM-Drugs dataset serving as a key benchmark. However, current evaluati…
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…
MolecularRNN: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva +1
Designing new molecules with a set of predefined properties is a core problem in modern drug discovery and development. There is a growing need for de-novo design methods that woul…