Beyond SMILES: Evaluating Agentic Systems for Drug Discovery

2026-02-10

AI Summary

Current AI agents for drug discovery exhibit limited generalization in peptide therapeutics, in vivo pharmacology, and resource-constrained settings, revealing critical capability gaps. This work systematically evaluates six leading agent frameworks across fifteen task categories, combining task taxonomy with knowledge probing experiments to demonstrate that their performance bottlenecks stem primarily from architectural limitations rather than insufficient knowledge—particularly in peptide handling, integration of in vitro and in vivo data, and adaptability under resource constraints. Building on these insights, the study proposes a design specification and capability matrix for next-generation drug discovery agents, emphasizing the integration of protein language models, support for multi-objective optimization, and efficient computational collaboration under realistic operational constraints.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Agentic systems for drug discovery have demonstrated autonomous synthesis planning, literature mining, and molecular design. We ask how well they generalize. Evaluating six frameworks against 15 task classes drawn from peptide therapeutics, in vivo pharmacology, and resource-constrained settings, we find five capability gaps: no support for protein language models or peptide-specific prediction, no bridges between in vivo and in silico data, reliance on LLM inference with no pathway to ML training or reinforcement learning, assumptions tied to large-pharma resources, and single-objective optimization that ignores safety-efficacy-stability trade-offs. A paired knowledge-probing experiment suggests the bottleneck is architectural rather than epistemic: four frontier LLMs reason about peptides at levels comparable to small molecules, yet no framework exposes this capability. We propose design requirements and a capability matrix for next-generation frameworks that function as computational partners under realistic constraints.

Problem

• agentic systems
• drug discovery
• peptide therapeutics
• in vivo pharmacology
• resource-constrained settings

Innovation

• Agentic systems
• Drug discovery
• Peptide therapeutics
• Capability gaps
• Computational partners