Active Causal Machine Learning for Molecular Property Prediction

Abstract

Predicting properties from molecular structures is paramount to design tasks in medicine, materials science, and environmental management. However, design rules derived from the structure-property relationships using correlative data-driven methods fail to elucidate underlying causal mechanisms controlling chemical phenomena. This preliminary work proposes a workflow to actively learn robust cause-effect relations between structural features and molecular property for a broad chemical space utilizing smaller subsets, entailing partial information.

PDF NeurIPSW OpenReview Semantic Scholar

Cite

Text

Fox and Ghosh. "Active Causal Machine Learning for Molecular Property Prediction." NeurIPS 2023 Workshops: AI4Mat, 2023.

Markdown

[Fox and Ghosh. "Active Causal Machine Learning for Molecular Property Prediction." NeurIPS 2023 Workshops: AI4Mat, 2023.](https://mlanthology.org/neuripsw/2023/fox2023neuripsw-active/)

BibTeX

@inproceedings{fox2023neuripsw-active,
  title     = {{Active Causal Machine Learning for Molecular Property Prediction}},
  author    = {Fox, Zachary R and Ghosh, Ayana},
  booktitle = {NeurIPS 2023 Workshops: AI4Mat},
  year      = {2023},
  url       = {https://mlanthology.org/neuripsw/2023/fox2023neuripsw-active/}
}