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.
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...
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.
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...
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: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.
arXiv:2407. 07357v3 Announce Type: replace Abstract: Predicting signed interactions in biological networks is crucial for understanding drug mechanisms and facilitating drug repurposing.
arXiv:2606. 14245v1 Announce Type: new Abstract: Drug-target interaction (DTI) and affinity (DTA) predictors increasingly achieve strong benchmark scores, yet their internal use of sequence, fingerprint, and graph features often remains opaque.
arXiv:2607. 08404v1 Announce Type: cross Abstract: Current computational approaches for drug design typically focus on generating molecules conditioned on specific targets or general molecular properties, often neglecting the influence of disease context on target behavior and therapeutic outcomes.
SpecOpt is a new molecular design task that optimizes the binding specificity of existing drugs by making constrained structural modifications. The method uses an agentic framework that docks a compound against its intended target and known off‑targets, compares residue‑aware atom‑protein contacts, and feeds the differential interactions to a large language model to propose changes. On a benchmark of 915 compounds, SpecOpt increased the target‑off‑target binding gap for 84.8% of cases while preserving drug‑like properties and structural similarity.
arXiv:2608.30674v1 Announce Type: cross Abstract: Accurate molecular property prediction requires both statistical reliability and chemical reasoning. Graph neural networks can be calibrated directly...
The paper introduces ReGeoDTA, a framework that preserves chemical heterogeneity and continuous geometric relationships in drug and protein representations to improve drug–target affinity prediction. Experiments on three benchmark datasets show that maintaining representation fidelity consistently enhances predictive accuracy across various DTA architectures, while degrading representations harms performance and cannot be recovered by more complex downstream models. The study highlights representation fidelity as a key upstream design principle for accurate and generalizable affinity prediction.