97 citations · 101 across the 2 of their papers we have counts for
3 papers
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…