Agentic orchestration of drug discovery ML tools under partial observability

Sara Masarone, Katie Victoria Beckwith, Matthew Mason, Thomas Clelford, Layla Hosseini-Gerami

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Track: tiny / short paper (2-4 pages excluding references; extended abstract format)

Keywords: drug discovery, bioinformatics, cheminformatics, agentic workflows

TL;DR: We present an AI orchestrating agent that dynamically coordinates heterogeneous machine learning tools to maintain drug discovery workflows even when critical data is missing or incomplete.

Abstract:

Over 90\% of drugs fail in clinical trials, often due to unanticipated safety issues, resulting in substantial financial losses and missed therapeutic opportunities. We develop models that uncover mechanistic bases of observed safety liabilities and support rational drug modification, including an asset-sourcing agent, a cheminformatics module predicting off-target interactions, and a bioinformatics module linking off-targets to toxicity pathways.

Individually, these tools are performant but outputs are fragmented, and not all input data are always available, limiting rapid decision-making. To address this, we introduce an orchestrating agent that dynamically coordinates tool execution based on data availability, task context, and uncertainty. The agent selectively invokes, sequences, or defers modules to enable adaptive analysis under partial information. We present its architecture and early testing, illustrating a framework to unify a fragmented AI ecosystem into a coherent, agent-driven system.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Submission Number: 20