@inproceedings{eec45cad9b8b45bbaa445b6cd704436b,
title = "Probabilistic and Dynamic Molecule-Disease Interaction Modeling for Drug Discovery",
abstract = "Drug discovery aims at finding promising drug molecules for treating target diseases. Existing computational drug discovery methods mainly depend on molecule databases, ignoring valuable data collected from clinical trials. In this work, we propose PRIME to leverage high-quality drug molecules and drug-disease relations in historical clinical trials to narrow down the molecular search space in drug discovery. PRIME also introduces time dependency constraints to model evolving drug-disease relations using a probabilistic deep learning model that can quantify model uncertainty. We evaluated PRIME against leading models on both de novo design and drug repurposing tasks. Results show that compared with the best baselines, PRIME achieves 25.9% relative improvement (i.e., reduction) in average hit-ranking on drug repurposing and 47.6% relative improvement in success rate on de novo design.",
keywords = "drug discovery, probabilistic deep learning",
author = "Tianfan Fu and Cao Xiao and Cheng Qian and Glass, {Lucas M.} and Jimeng Sun",
note = "Funding Information: This work was supported by NSF award SCH-2014438, PPoSS 2028839, IIS-1838042, NIH award R01 1R01NS107291-01 and OSF Healthcare. Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 ; Conference date: 14-08-2021 Through 18-08-2021",
year = "2021",
month = aug,
day = "14",
doi = "10.1145/3447548.3467286",
language = "English (US)",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "404--414",
booktitle = "KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining",
address = "United States",
}