VINCENT: Validated Interaction Network for Cross-drug Explanation of Therapeutics

Fan-Sheng Chuang; Xuchen Li; Yujing Bian; Kaixiong Zhou

cs.LGAug 26, 2026

VINCENT: Validated Interaction Network for Cross-drug Explanation of Therapeutics

Authors: Fan-Sheng Chuang, Xuchen Li, Yujing Bian, Kaixiong Zhou

Organizations: North Carolina State University Computer Science Raleigh, USA · North Carolina State University Electrical and Computer Engineering Raleigh, USA

Abstract

Drug synergy prediction estimates whether two drugs produce a stronger joint effect than expected from their individual activities. For drug combination discovery, a single synergy score is often not enough: researchers also need to know which molecular regions jointly drive the prediction. We study motif-pair synergy explanation, which identifies pairs of chemically coherent regions, one from each drug, that jointly contribute to predicted synergy. Existing interpretable synergy models expose atom- or substructure-level signals, but their explanations are built into the predictor architecture, and none validates cross-drug region scores under repeated perturbations or feeds that evidence back to refine the explanation. A reliable motif-pair explanation should instead be chemically coherent, perturbation-stable, and aligned with predictor behavior. We introduce VINCENT (Validated Interaction Network for Cross-drug Explanation of Therapeutics), a post-training framework for a fixed interaction-aware synergy predictor. VINCENT extracts atom-pair evidence from attention and gradient signals, groups atoms into chemically coherent motifs, and validates candidate motif pairs through repeated local perturbations. The validated evidence is fed back to refine motif assignments, yielding explanations that satisfy these three criteria. On a 25-pair literature-annotated subset, VINCENT achieves a mean motif recall of 0.826 (95% CI: 0.78-0.87), compared with 0.49-0.66 for baselines. Across all 71 test pairs, its validated interaction scores yield a TP/TN separation of 3.36. These results show that closed-loop perturbation validation recovers literature-supported molecular regions more accurately than existing alternatives while producing cross-drug interaction scores that better reflect predictor behavior.

Figures & tables

Method	Expl. unit	Post-hoc	Rep. valid.	Feedback
SDDSynergy ( Liu et al., 2024 )	Learned substruct.	–	–	–
SynergyX ( Guo et al., 2024 )	Predef. substruct.	–	–	–
GraFSyn ( Xia et al., 2026 )	Graphlet substruct.	–	–	–
DeepSTFSynergy ( Huang et al., 2026 )	Multi-scale struct.	–	–	–
DeepDrugs ( Xin et al., 2026 )	Cross-drug attn.	–	–	–
SDCInterpreter ( Wang et al., 2026 )	Mechanism paths	–	–	–

Table 1. Comparison of interpretable and post-hoc approaches relevant to cross-drug motif-pair explanation. Post-hoc : applicable to an independently trained, fixed predictor. Rep. valid. : the same cross-drug region pair is repeatedly perturbed to estimate a stable interaction effect. Feedback : validated evidence is used to update the explanatory regions. ✓/– denote yes/no.

Figure 1. Overview of VINCENT: the fixed predictor (left) provides atom-level representations and cross-drug signals. Phases 1–2 prepare the loop’s inputs; Phases 3–4 form the assignment–validation loop with iterative feedback, yielding the top-ranked cross-drug motif pairs (right). Matrices and molecular fragments are illustrative.

Parameter	Default	Role
Target motif size $c$	6	Motif granularity
Perturbation trials $T$	16	Perturbation stability
Outer-loop iterations	3	Refinement depth
Candidate screening	top 30%, min 3, max 20	Pair-selection scope

Table 2. Key hyperparameter settings.

	Method	Recall	Precision	Jaccard	HR $\geq$ 0.7	$\rho(s_{AB},\sum r)$	TP/TN Sep.
Attribution	Integrated Gradients Saliency	0.498	0.344	0.288	28.8%	—	—
Attribution	Cross-Drug Attention Saliency	0.488	0.338	0.287	30.6%	—	—
External	GNNExplainer	0.581	0.476	0.394	39.2%	—	—
	SubgraphX	0.661	0.536	0.443	48.9%	—	—
	PGExplainer	0.552	0.451	0.371	40.8%	—	—
	CF-GNNExplainer	0.486	0.397	0.311	26.7%	—	—

Table 3. Main explanation results. Recall, precision, Jaccard, and HR

\geq

0.7 are evaluated on the 25-pair literature-annotated subset (111 reference motifs). Predictor-alignment metrics

\rho(s_{AB},\sum r)

and TP/TN separation are evaluated on all 71 test pairs. Coverage confidence intervals are from pair-level bootstrap with 2,000 resamples.

Figure 2. Literature motif coverage: (a) coverage metrics across methods; (b) distribution of best-region recall across motifs.

Parameter	Setting	Recall	Precision	Jaccard	HR $\geq$ 0.7	$\rho$	TP/TN
Target size $c$	4	.791	.766	.641	.69	.404	3.05
	5	.823	.788	.686	.75	.418	3.28
	6	.826	.790	.689	.77	.423	3.36
	8	.802	.741	.632	.71	.409	3.14
	10	.768	.699	.582	.63	.392	2.88
Perturbation trials $T$	4	.812	.777	.668	.73	.331	2.38

Table 4. Sensitivity to target motif size

c

, perturbation trials

T

, and outer-loop refinement. Bold marks the default

c

and

T

; the default outer-loop depth is 3.

\rho

: Pearson correlation between predicted synergy and total validated interaction strength.

Figure 3. Diagnostic case study: Nitazoxanide + Remdesivir ( $s_{AB}=0.487$ , false negative). (a,b) Motif clusters for each drug. (c) Validated interaction matrix $R$ .

Appendix figures & tables11 assets

Supplementary material from the paper’s appendix.

Appendix

Evidence Role	Identifier	Publication
Combination response	PMID:33333292; DOI:10.1016/j.ymthe.2020.12.016	Bobrowski et al., Synergistic and Antagonistic Drug Combinations against SARS-CoV-2 (2021).
Combination response	PMID:32251767; DOI:10.1016/j.antiviral.2020.104786	Choy et al., Remdesivir, lopinavir, emetine, and homoharringtonine inhibit SARS-CoV-2 replication in vitro (2020).
Combination response	PMID:34572416; DOI:10.3390/biomedicines9091230	Kongsomros et al., Anti-SARS-CoV-2 Activity of Extracellular Vesicle Inhibitors: Screening, Validation, and Combination with Remdesivir (2021).
Combination response	PMID:36190406; DOI:10.1128/spectrum.03331-22	Wagoner et al., Combinations of Host- and Virus-Targeting Antiviral Drugs Confer Synergistic Suppression of SARS-CoV-2 (2022).
Drug mechanism	PMID:33711336; DOI:10.1016/j.antiviral.2021.105056	Kumar et al., Emetine suppresses SARS-CoV-2 replication by inhibiting interaction of viral mRNA with eIF4E (2021).
Combination response / mechanism	PMID:35215969; DOI:10.3390/v14020374	Sacramento et al., Unlike Chloroquine, Mefloquine Inhibits SARS-CoV-2 Infection in Physiologically Relevant Cells (2022).

Appendix

Table 5. Key literature evidence used in the reference construction. Combination-response sources establish drug-pair behavior, whereas mechanistic and structure–activity sources support drug-specific molecular-region annotation.

Pair ID	Drugs	Literature-Grounded Context
pair_000	Amodiaquine + Nitazoxanide	Experimental combination screening reports synergy for nitazoxanide with amodiaquine. The two drugs contribute distinct antiviral pharmacological scaffolds, motivating localization of the chloroquinoline/basic-amine regions of amodiaquine and the thiazolide regions of nitazoxanide.
pair_002	Nitazoxanide + Remdesivir	Direct combination screening reports significant synergy between nitazoxanide and remdesivir. Nitazoxanide contributes a thiazolide antiviral scaffold, whereas remdesivir acts through its nucleotide-analog/RdRp axis. These mechanisms define complementary drug-specific regions for localization analysis.
pair_023	Emetine + Remdesivir	In vitro experiments report synergistic inhibition of SARS-CoV-2 by emetine and remdesivir. Emetine suppresses viral protein synthesis and has been linked to inhibition of viral mRNA interaction with eIF4E, whereas remdesivir directly inhibits viral RdRp. The two mechanisms provide distinct post-entry pharmacological contexts for molecular region annotation.
pair_061	Camostat + Remdesivir	Camostat inhibits SARS-CoV-2 entry through TMPRSS2-related serine proteases, whereas remdesivir targets viral RNA synthesis through RdRp. The entry-versus-replication distinction provides a mechanistically complementary basis for localizing relevant regions on the two component drugs.
pair_067	Mefloquine + Remdesivir	Mefloquine has been shown to reduce SARS-CoV-2 entry in physiologically relevant cells and to enhance the antiviral activity of remdesivir. Remdesivir provides the complementary nucleotide-analog/RdRp mechanism, motivating region annotations on both drug structures.

Appendix

Table 6. Representative pair-level mechanistic context used to guide drug-specific region annotation.

Pair ID	Drug	Region	Chemical Type	Annotation Role
pair_000	A	Chloroquinoline core	Heterocyclic aromatic	Principal heteroaromatic scaffold of amodiaquine
pair_000	A	Diethylamino sidechain	Basic amine	Basic side-chain region associated with the drug’s physicochemical and lysosomotropic behavior
pair_000	B	Nitrothiazole region	Heteroaromatic motif	Characteristic thiazolide heteroaromatic region of nitazoxanide
pair_000	B	Salicylamide core	Aromatic amide	Aromatic salicylamide scaffold region
pair_002	A	Nitrothiazole region	Heteroaromatic motif	Characteristic thiazolide heteroaromatic region of nitazoxanide
pair_002	A	Salicylamide core	Aromatic amide	Aromatic salicylamide scaffold region

Appendix

Table 7. Representative drug-specific literature-supported molecular regions used in the localization evaluation.

Pair	Drug	Region	Atom Indices	$\|\mathrm{ref}\|$	$\|\mathrm{pred}\|$	Recall	Precision	Jaccard
pair_000	A	Chloroquinoline core	{10–20}	11	11	1.000	1.000	1.000
pair_000	A	Diethylamino sidechain	{0–5}	6	8	1.000	0.750	0.750
pair_000	B	Nitrothiazole region	{13–20}	8	8	0.875	0.875	0.778
pair_000	B	Salicylamide core	{4–12}	9	13	0.889	0.615	0.571
pair_002	A	Nitrothiazole region	{13–20}	8	7	0.875	1.000	0.875
pair_002	A	Salicylamide core	{4–12}	9	9	0.889	0.889	0.800

Appendix

Table 8. Representative atom-level reference mappings and region-aware matching results. Atom indices are zero-based RDKit indices.

|\mathrm{ref}|

and

|\mathrm{pred}|

denote the reference and matched predicted region sizes, respectively.

Statistic	Value
Annotated benchmark entries	25
Literature-supported reference regions	111
Mean reference regions per entry	4.44
Reference regions on Drug A	57
Reference regions on Drug B	54
T1 entries: direct combination-response evidence	10

Appendix

Table 9. Summary statistics of the literature-supported molecular-region reference.

Figure 4. Case study of Amodiaquine + Nitazoxanide (true positive; predicted synergy score 0.690). (a) Predicted substructure segmentation of Amodiaquine. (b) Predicted substructure segmentation of Nitazoxanide. (c) Region-pair interaction matrix $R$ after local perturbation validation, where $r_{kl}$ denotes the validated interaction score between predicted regions $k$ and $l$ .

Parameter	Default	Role
Target motif size $c$	6	Atoms per motif (determines $K_{d}=\mathrm{clip}(\lfloor N_{d}/c\rceil,2,8)$ )
Perturbation trials $T$	16	Trials per retained motif pair
Outer-loop iterations	3	Default refinement depth
Candidate screening $\theta_{\mathrm{screen}}$	top 30%, min 3, max 20	Pair-selection scope
$(\lambda_{\mathrm{struct}},\lambda_{\mathrm{pattern}},\lambda_{\mathrm{fb}})$	$(1.0,\;0.7,\;0.3)$	Affinity-view weights
$\lambda_{H}$	0.03	Entropy regularization weight

Appendix

Table 10. Complete hyperparameter settings for the explanation framework.

Parameter	Setting	Recall	Precision	Jaccard	HR ${\geq}$ 0.7	$\rho$	TP/TN
$K_{d}$ ( $c$ )	4	0.791	0.766	0.641	0.69	0.404	3.05
	5	0.823	0.788	0.686	0.75	0.418	3.28
	6	0.826	0.790	0.689	0.77	0.423	3.36
	8	0.802	0.741	0.632	0.71	0.409	3.14
	10	0.768	0.699	0.582	0.63	0.392	2.88
$T$	4	0.812	0.777	0.668	0.73	0.331	2.38

Appendix

Table 11. Complete sensitivity results across five hyperparameter sweeps and an early outer-loop convergence analysis. Bold rows indicate defaults for the five swept hyperparameters and the preselected outer-loop depth of 3. The iteration-10 row is included only to characterize convergence beyond the default.

Tier	#Pairs	Recall	Precision	HR $\geq$ 0.7
T1	10	0.842 [0.767–0.898]	0.814 [0.749–0.868]	0.858 [0.738–0.958]
T2	3	0.809 [0.753–0.838]	0.830 [0.709–0.906]	0.700 [0.500–0.800]
T3m	12	0.824 [0.757–0.890]	0.768 [0.709–0.829]	0.718 [0.546–0.875]
All	25	0.829 [0.785–0.871]	0.794 [0.751–0.835]	0.771 [0.672–0.861]

Appendix

Table 12. Motif coverage stratified by evidence tier. 95% CIs from pair-level bootstrap (2,000 resamples within each tier). The “All” row reports pair-level means; the motif-level mean in Section 5.3 (0.826) weights each motif equally regardless of pair.

$k$ removed	Pairs remaining	95% range of mean recall
1	24	[0.823–0.841]
2	23	[0.819–0.843]
3	22	[0.815–0.847]
5	20	[0.809–0.852]

Appendix

Table 13. Pair-selection stability of mean recall under leave-

k

-out resampling (2,000 random subsets per

k

Model	Test ROC-AUC
Random Forest	0.62
DeepSynergy	0.68
DeepDDS	0.80
ComboNet (original)	0.82
Our predictor (fixed target)	0.85

Appendix

Table 14. Predictor adequacy: test ROC-AUC on the SARS-CoV-2 combination benchmark.

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