Agent improves drug binding specificity while keeping original structure
SpecOpt: Contact-Diff Reasoning for Agentic Molecule Optimization Toward Binding Specificity
Artificial Intelligence
Summary
Medications sometimes attach to unintended proteins, causing side effects. The authors introduce a method called SpecOpt that slightly changes existing drugs to stick better to their intended target and less to others. They test this on nearly a thousand compounds and show it usually improves selectivity while keeping the drug very similar to the original. Their system uses detailed interaction information between molecules and proteins to guide changes.
What this means in practice
- •For drug discovery teams: Improve existing drug candidates' selectivity to reduce side effects by targeted molecular modifications guided by binding differences to proteins.
- •For chemical informatics developers: Develop tools combining docking and language models to propose precise molecular edits improving drug-target interaction specificity.
Authors
Thao Nguyen, Heng Ji
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.