Agentic AI for Computational Pharmacology: Orchestrating Molecular Search, Evidence Appraisal, Mechanistic Reasoning, and Candidate Prioritization

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Agentic artificial intelligence is emerging as a potential approach for coordinating complex computational research workflows that require multiple searches, tools, reasoning stages, and review checkpoints. In computational pharmacology, such coordination may support the integration of molecular search, literature retrieval, evidence appraisal, target and pathway interpretation, safety assessment, and candidate prioritization. This article proposes a conceptual agentic AI framework designed to organize these activities while preserving source grounding, pharmacological caution, auditability, and human decision authority. The framework separates molecular and evidence-retrieval functions from source-quality appraisal, mechanistic reasoning, safety review, and multi-criteria prioritization. It further introduces explicit controls for tool permissions, evidence provenance, citation traceability, conflict detection, uncertainty communication, expert escalation, and validation planning. Molecular search outputs are treated as research hypotheses rather than validated candidates, while mechanistic interpretations are framed as evidence-linked plausibility assessments rather than confirmation of biological mechanism. Candidate rankings are similarly restricted to provisional decision-support outputs that require pharmacological, medicinal-chemistry, toxicological, and experimental review. The principal contribution is a structured architecture in which specialized agents can assist task decomposition and evidence organization without independently establishing biological truth, safety, efficacy, translational readiness, or clinical utility. By embedding human oversight and validation boundaries throughout the workflow, the proposed framework positions agentic AI as a research-orchestration approach rather than an autonomous drug discovery or clinical decision-making system.