SpecOpt: Contact-Diff Reasoning for Agentic Molecule Optimization Toward Binding Specificity

Thao Nguyen; Heng Ji

cs.AISep 18, 2026

SpecOpt: Contact-Diff Reasoning for Agentic Molecule Optimization Toward Binding Specificity

Authors: Thao Nguyen, Heng Ji

Organizations: Siebel School of Computing and Data Science University of Illinois Urbana-Champaign

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.

Figures & tables

Figure 1: Overview of SpecOpt. Given a compound, its intended target, and known off-targets, SpecOpt docks the compound against each protein, computes per-atom contact differences with residue identities, and uses an LLM agent to propose targeted molecular edits. Candidate molecules are evaluated using structural similarity, ADMET, and re-docking criteria, and accepted candidates are iteratively refined in subsequent rounds.

Figure 2: Differential interaction analysis and molecular editing in SpecOpt. Target- and off-target-bound poses provide atom-level contact differences and growth opportunities, which are aggregated into fragment-level editing suggestions to guide candidate molecule construction.

	Baseline (original drug)	After optimization
Mean specificity gap (kcal/mol)	$-0.718$	$\mathbf{+0.472}$
Median specificity gap (kcal/mol)	$-0.600$	$+0.300$
Compounds improved	—	$776/915$ $(84.8\%)$
Mean Tanimoto to original	$1.000$	$0.720$

Table 1: Main result over the full compound pool. Gap is defined in Eq. 1 ; positive means target-preferring. The paired test compares each compound’s own baseline against its own optimized result.

Figure 3: Main result across 915 compounds. (a) The specificity-gap distribution shifts from predominantly negative (off-target-preferring) to predominantly positive. (b) Per-compound improvement. (c) Structural conservation; the spike at

1.0

is compounds returned unchanged because no candidate passed the admission gates.

Figure 5: Selected SpecOpt case studies showing the original (left) and optimized (right) structures. Highlighted atoms indicate structural changes relative to the maximum common substructure of the original compound. Specificity gaps are reported in kcal/mol.

Compound	Target / off-target	Base	Opt.	Sim.	Edit
Edoxaban	factor Xa / prothrombin	$-5.6$	$+1.5$	$0.76$	F + CF 3 on cyclohexane
Ibipinabant	CB 1 / CB 2	$-1.5$	$+5.1$	$0.73$	F on arylsulfonyl ring
Brequinar	DHODH / DHFR	$-4.9$	$+0.1$	$0.83$	F repositioned on biaryl
Repotrectinib	TRKB / ROS1, JAK2, FAK1	$-4.6$	$+0.2$	$0.73$	nitrile on fluoroaryl
Berotralstat	kallikrein / plasminogen	$-3.5$	$+1.9$	$0.78$	NH $\rightarrow$ $N$ -methyl

Table 2: Selected case studies from the main evaluation. “Edit” summarizes the net structural change relative to the original compound.

Figure 6: Ablation and signal analysis. (a) Mean change in specificity-gap improvement when each component is removed; positive values indicate that the component improves performance. Green indicates $p<0.05$ , orange $p<0.1$ , and grey no statistical significance. (b) Fragment-level interaction signals aggregated across the 915-compound evaluation. Exclusive target- or off-target-contact signals do not occur, while most informative signals arise from different residue identities across shared contacts and target-specific growth opportunities.

Relationship	$n$	Base	Opt.	$\Delta$	% improved	Wilcoxon $p$
Isoform / subtype	149	$-0.72$	$+0.48$	$+1.20$	$85.9\%$	$9{\times}10^{-23}$
Homologous (same class)	335	$-0.43$	$+0.94$	$+1.38$	$83.3\%$	$2{\times}10^{-47}$
Different (no shared class)	431	$-0.94$	$+0.10$	$+1.04$	$85.6\%$	$3{\times}10^{-62}$

Table 3: Performance grouped by the relationship between the intended target and the off-target defining the specificity gap. Performance is consistent across relationship types.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 7: Binding-score changes after optimization for FLT3-targeting compounds. Left: change in target binding, where negative values indicate stronger binding. Right: changes across five off-targets, where positive values (blue) indicate weaker binding. Five compounds weaken all five off-targets simultaneously, while the remaining compounds show mixed effects across off-targets.

Figure 8: Benchmark composition. (a) Number of off-targets per compound on a logarithmic axis, with a median of 3. (b) Clinical stage; approximately two thirds of compounds are approved drugs. (c) Target-class annotations. (d) Molecular weight versus QED for the starting compounds.

Protein	Endpoint	$n$	Spearman $\rho$	$p$
Muscarinic acetylcholine receptor M2	AC50	132	$-0.574$	$6.3\times 10^{-13}$
Mu-type opioid receptor	AC50	119	$-0.316$	$4.7\times 10^{-4}$
5-HT receptor 2B	Ki	99	$-0.258$	$0.010$
KCNH2 (hERG)	IC50	124	$-0.237$	$0.008$
5-HT receptor 2A	AC50	117	$-0.229$	$0.013$

Appendix

Table 4: The five proteins with the strongest docking–affinity agreement, after filtering to a single assay type and unit. Negative

\rho

is the expected direction (a more negative Vina score should accompany a higher

p

-activity). These are the strongest correlations among the 19 proteins tested; the median over all 19 is

-0.075

Figure 9: Docking scores pooled into two sets over the whole benchmark: compound–protein pairs where the protein is the compound’s intended target (

n=1{,}670

) and bindings where it is an off-target (

n=11{,}823

). Density-normalized; dashed lines mark medians. The comparison shows no statistically significant difference and a small effect size: medians

-8.20

vs.

-8.30

kcal/mol, Mann–Whitney

p=0.21

, Cohen’s

d=0.02

, with quantiles agreeing to within

0.1

–

0.4

kcal/mol at the 5th, 25th, 75th and 95th percentiles.

		Docking score		Experimental (pChEMBL)
Category	Vina / $K_{d}$	target	off-target	target	off-target
Strong	$\leq-9.0$ ( $\lesssim$ 260 nM)	34.7%	34.5%	81.5%	27.7%
Moderate	$-9.0$ to $-7.0$ (260 nM–7.5 µM)	37.6%	40.2%	15.4%	64.1%
Weak	$-7.0$ to $-5.0$ (7.5–220 µM)	22.6%	20.4%	0 3.1%	0 8.2%
Negligible	$>-5.0$ ( $\gtrsim$ 220 µM)	0 5.1%	0 4.9%	0 0.0%	0 0.0%
Strong or moderate		72.3%	74.7%	96.9%	91.8%

Appendix

Table 5: Binding-strength categories. Left: our docking scores. Right: the experimental affinities binned at the pChEMBL equivalents of the same cut points (

-9.0\to 6.59

-7.0\to 5.13

-5.0\to 3.66

). Docking places intended targets and off-targets in nearly identical proportions; the experimental labels differ by approximately 54 percentage points in the strong category. The experimental “negligible” row is empty because off-targets enter the benchmark only at pChEMBL

\geq 5.0

Component removed	$n$	full	ablated	$\Delta$	Wilcoxon $p$
Five-way contact diff (naive 3-way instead)	29	$+0.903$	$+0.000$	$\mathbf{+0.903}$	$<0.001$
Within-compound memory (8-round budget)	29	$+1.390$	$+0.786$	$+0.603$	$0.091$
Combo mechanism	29	$+0.903$	$+0.824$	$+0.079$	$0.082$
Cross-compound memory	29	$+0.903$	$+0.852$	$+0.052$	$0.648$
Pose-consistency caution	17	$+1.147$	$+1.141$	$+0.006$	$0.893$
Within-compound memory (4-round budget)	28	$+0.936$	$+1.046$	$-0.111$	$0.972$

Appendix

Table 6: Paired ablation results.

\Delta

is mean(full

-

ablated); positive means the component contributes.

Endpoint	Generated (top-20)	Real drugs (baseline)	Ours (optimized)
AMES (mutagenicity) $\downarrow$	$0.539$	$0.288$	$\mathbf{0.178}$
hERG (cardiotoxicity) $\downarrow$	$\mathbf{0.691}$	$0.696$	$0.702$
P-gp (efflux) $\downarrow$	$0.349$	$0.315$	$\mathbf{0.290}$
DILI (liver injury) $\downarrow$	$0.787$	$0.753$	$\mathbf{0.745}$
HIA (absorption) $\uparrow$	$0.442$	$0.857$	$\mathbf{0.876}$
BBBP (BBB penetration)	$0.387$	$0.680$	$0.619$

Appendix

Table 7: ADMET comparison (mean predicted probability). Arrows indicate the preferred direction:

\downarrow

lower is better;

\uparrow

higher is better.

Molecular Optimization

Molecular optimization computationally designs or edits chemical structures to improve desired properties while respecting chemical and practical constraints.

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Lead optimization in structure-based drug design aims to improve target binding while avoiding unintended interactions with off-target pockets. However, existing affinity-driven methods do not explicitly control specificity, whereas current specificity-aware approaches commonly require prior knowledge of off-target structures. We address off-target-agnostic specificity-aware lead optimization by analyzing the geometric mismatch between a ligand and the target pocket. We provide a conservative specificity lower bound for geometrically separated off-targets without requiring access to off-target structures. By metricizing pocket--ligand mismatch, the triangle inequality shows that reducing target--ligand mismatch improves a conservative lower bound on mismatch to a separated off-target class, which can be translated into a specificity lower bound through an empirical geometry--affinity calibration. Motivated by this analysis, we introduce SurfSpec, an off-target-agnostic lead optimization framework that iteratively grows ligands toward under-occupied regions of the target pocket surface. SurfSpec alternates between linker generation toward selected target-surface patches, which provides geometric pseudo-labels, and refinement under a pocket-conditioned ligand prior, which restores these pseudo-labels into valid ligands. On the CrossDocked2020 test set, SurfSpec reduces geometric mismatch and outperforms evaluated off-target-agnostic lead optimization baselines in empirical specificity, while maintaining competitive target-affinity improvement.

Abstract

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