Targeting tissues via dynamic human systems modeling in generative design
Keywords: Drug discovery, language models, physiological modeling, genetic algorithms
TL;DR: We built a framework for LM-based molecular generation scored by dynamic physiological models (PBPK) and retrosynthetic analysis.
Abstract: 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.
Submission Number: 89
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