Pharmacological Mechanism Reasoning with Collaborative LLM Agents for Molecular Property Prediction

Molecular property prediction has advanced on the premise that properties follow from a sufficiently precise representation of structure. LLMs have extended this paradigm by aligning molecular and textual representations, retrieving analogous molecules, and learning step-wise reasoning over structure. In all of these, the property is still predicted directly from structure, without passing through the biological context in which it arises. A molecular property, however, is observed as the outcome of a molecule engaging targets in vivo and triggering downstream events, and a prediction that does not pass through that mechanism remains an extrapolation of structural similarity. We reformulate property prediction as mechanism-grounded evidence reasoning and propose PharmaLens, a multi-agent framework that realizes this formulation. For a given property, agents at the physicochemical, target-relation, and binding-structure levels establish the specified factors on the query molecule by tool-based measurement, and the resulting evidence is integrated over the relational logic. Across in vivo endpoints from TDC and organ-toxicity tasks from UniTox, PharmaLens achieves strong performance on both benchmarks, and it retains this margin on property cliffs, where structurally similar molecules carry opposite labels and label-trained predictors fall off most sharply. Its rationales are also consistently favored across all three evaluations. The source code is publicly available at https://github.com/anonymousqwerty350/PharmLens.