@inproceedings{
fox2023targeting,
title={Targeting tissues via dynamic human systems modeling in generative design},
author={Zachary Fox and Nolan English and Belinda Akpa},
booktitle={NeurIPS 2023 Generative AI and Biology (GenBio) Workshop},
year={2023},
url={https://openreview.net/forum?id=lOIKlYR3vX}
}
Drug discovery is a complex, costly process with high failure rates. A successful drug should bind to a target, be deliverable to an intended site of activity, and promote a desired pharmacological effect without causing toxicity. Typically, these factors are evaluated in series over the course of a pipeline where the number of candidates is reduced from a large initial pool. One promise of AI-driven discovery is the opportunity to evaluate multiple facets of drug performance in parallel. However, despite ML-driven advancements, current models for pharmacological property prediction are exclusively trained to predict molecular properties, ignoring important, dynamic biodistribution and bioactivity effects.
Targeting tissues via dynamic human systems modeling in generative design
Zachary Fox Nolan English Belinda Akpa