Calibration and Generalizability of Probabilistic Models on Low-Data Chemical Datasets With DIONYSUS | Gary Tom
Description
[DISCLAIMER] - For the full visual experience, we recommend you tune in through our YouTube channel to see the presented slides.
If you enjoyed this talk, consider joining the Molecular Modeling and Drug Discovery (M2D2) talks live.
Also, consider joining the M2D2 Slack.
Abstract: Deep learning models that leverage large datasets are often the state of the art for modelling molecular properties. When the datasets are smaller (less than 2000 molecules), it is not clear that deep learning approaches are the right modelling tool. In this work we perform an extensive study of the calibration and generalizability of probabilistic machine learning models on small chemical datasets. Using different molecular representations and models, we analyse the quality of their predictions and uncertainties in a variety of tasks (binary, regression) and datasets. We also introduce two simulated experiments that evaluate their performance: (1) Bayesian optimization guided molecular design, (2) inference on out-of-distribution data via ablated cluster splits. We offer practical insights into model and feature choice for modelling small chemical datasets, a common scenario in new chemical experiments. We have packaged our analysis into the DIONYSUS repository, which is open sourced to aid in reproducibility and extension to new datasets.
Speaker: Gary Tom
Twitter - Prudencio
Twitter - Therence
Twitter - Jonny
Twitter - Valence Discovery
United States
