repoGitHubTrust 82 · PrimaryPublished 11d agoLive · 11d ago
rdk/p2rank
P2Rank: Protein-ligand binding site prediction from protein structure based on machine learning.
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 62%Reshaping biomolecular structure prediction through strategic conformational exploration with HelixFold-S1 →
- PossiblePossibly related (embedding) · 54%An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility →
- PossiblePossibly related (embedding) · 54%Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark →
- PossiblePossibly related (embedding) · 52%Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language →
- PossiblePossibly related (embedding) · 50%Explainable AI for Cancer Drug Response Prediction: Beyond Univariate Feature Attributions →
- PossiblePossibly related (embedding) · 45%Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations →
- PossiblePossibly related (embedding) · 52%$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning →
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Related across the graph
paperBeyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) BenchmarknewsReshaping biomolecular structure prediction through strategic conformational exploration with HelixFold-S1paperPredicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representationspaper$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learningpaperExplainable AI for Cancer Drug Response Prediction: Beyond Univariate Feature AttributionspaperAn Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous SolubilitynewsBridging three-dimensional molecular structures and artificial intelligence with a conformation description language