493 citations · 619 across the 7 of their papers we have counts for
9 papers
On the Selectivity of Generative Models in Structure-Based Drug Design
Ella Miray Rajaonson, Jungyoon Lee, William Chau +5
Designing small-molecule drugs that selectively bind to a target protein while avoiding unintended interactions with other proteins is essential for reducing side effects that cont…
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…
Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS
Gary Tom, Riley J. Hickman, Aniket Zinzuwadia +3
Deep learning models that leverage large datasets are often the state of the art for modelling molecular properties. When the datasets are smaller (< 2000 molecules), it is not cle…
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules
Benjamin Sanchez-Lengeling, Jennifer N. Wei, Brian K. Lee +3
Predicting the relationship between a molecule's structure and its odor remains a difficult, decades-old task. This problem, termed quantitative structure-odor relationship (QSOR)…
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…
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral +2
In unsupervised data generation tasks, besides the generation of a sample based on previous observations, one would often like to give hints to the model in order to bias the gener…