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VINCENT is a post‑training framework that provides validated, chemically coherent explanations for drug synergy predictions by extracting atom‑pair evidence from attention and gradient signals, grouping them into motifs, and refining these motifs through repeated local perturbations. On a literature‑annotated subset of 25 drug pairs, VINCENT achieves a mean motif recall of 0.826, outperforming baselines (0.49–0.66). Across 71 test pairs, its validated interaction scores yield a TP/TN separation of 3.36, indicating more accurate recovery of literature‑supported molecular regions and better alignment with predictor behavior.
ProbeMatchDTI is a new framework for drug‑target interaction prediction that uses probe‑driven pattern matching to preserve weak biochemical signals. It introduces IterProbe, which retains contextual states across refinement depths and selects them with learnable probes, and BindingProbe, which models drug‑protein complementarity at both local and whole‑pair levels. Experiments show that ProbeMatchDTI outperforms existing methods, improving AUC‑ROC by 2.0% on BindingDB and 0.5% on DrugBank, and its predictions can be integrated into downstream drug‑discovery workflows.
ProbeMatchDTI introduces a probe-driven framework for drug‑target interaction prediction that preserves weak biochemical signals by using IterProbe to retain contextual states and BindingProbe to model cross‑entity complementarity at multiple scales. The method improves AUC‑ROC by 2.0% on BindingDB and 0.5% on DrugBank compared to prior biochemical representation learning approaches. Feature‑level analyses confirm the effectiveness of the probe-driven pattern matching, and the predictions are linked to an evidence‑guided downstream drug‑discovery workflow for candidate refinement and validation planning.
arXiv:2606. 07698v1 Announce Type: cross Abstract: Graph neural networks (GNNs) applied to drug-drug interaction (DDI) prediction rely exclusively on molecular structure encoded as SMILES-derived graphs.
arXiv:2609.37384v1 Announce Type: new Abstract: Molecular representation learning is central to computer-aided drug discovery. Molecular graphs, SMILES strings, and 3D conformations provide complemen...
arXiv:2511. 19264v2 Announce Type: replace-cross Abstract: Generative Flow Networks (GFlowNets) construct molecules through sequential decisions, but their internal policies remain opaque, limiting adoption in drug discovery, where chemists need interpretable rationales for proposed structures.