[Submitted on 18 Sep 2026]
Title:SpecOpt: Contact-Diff Reasoning for Agentic Molecule Optimization Toward Binding Specificity
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Abstract:Off-target protein binding is a major source of adverse effects for small-molecule drugs, yet most structure-based molecular design methods focus on generating selective compounds de novo rather than improving the selectivity of existing, well- characterized drugs. We introduce specificity optimization (SpecOpt), a molecular design task that seeks constrained structural modifications to an existing compound that increase its binding preference for an intended target over known off-targets while preserving its structural identity and drug-like properties. To enable systematic evaluation, we construct a ChEMBL-derived benchmark from compound-target interaction data, identifying intended targets through curated drug-mechanism annotations and off- targets through measured activities. We then develop an agentic framework that docks each compound against its intended target and off-targets, compares the resulting poses through residue-aware atom-protein contacts, and provides these differential interactions to a large language model to propose targeted structural modifications. Candidates are retained only if they satisfy molecular similarity, ADMET, and target-off-target docking selectivity criteria. On 915 compounds, the agent improves the target- off-target binding gap for 84.8% of compounds, shifting the mean gap from -0.72 to +0.47 kcal/mol while maintaining a mean Tanimoto similarity of 0.72 to the starting compounds. Ablation studies identify residue-specific contact information as the critical optimization signal: replacing residue identities with binary contact indicators eliminates improvement on all 29 ablation compounds. These results establish SpecOpt as a distinct molecular design problem and demonstrate residue-aware differential interactions as an effective signal for improving the specificity of existing compounds.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.21165 [cs.AI]
(or arXiv:2609.21165v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.21165
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Thao Nguyen [view email] [v1] Fri, 18 Sep 2026 00:16:35 UTC (703 KB)
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