97 citations · 104 across the 4 of their papers we have counts for
5 papers · 1 filter
Graph Data Modeling: Molecules, Proteins, & Chemical Processes
José Manuel Barraza-Chavez, Rana A. Barghout, Ricardo Almada-Monter +3
Graphs are central to the chemical sciences, providing a natural language to describe molecules, proteins, reactions, and industrial processes. They capture interactions and struct…
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases
Gary Tom, Cher Tian Ser, Ella M. Rajaonson +4
Olfaction -- how molecules are perceived as odors to humans -- remains poorly understood. Recently, the principal odor map (POM) was introduced to digitize the olfactory properties…
Ranking over Regression for Bayesian Optimization and Molecule Selection
Gary Tom, Stanley Lo, Samantha Corapi +2
Bayesian optimization (BO) has become an indispensable tool for autonomous decision-making across diverse applications from autonomous vehicle control to accelerated drug and mater…
Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs
Daniil A. Boiko, Thiago Reschützegger, Benjamin Sanchez-Lengeling +2
Molecular representation is a critical element in our understanding of the physical world and the foundation for modern molecular machine learning. Previous molecular machine learn…
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling +13
Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with simila…