Cloud-based ligand-guided virtual screening with deep-learning-enhanced docking identifies a micromolar CXCR4 antagonist.

SurroDock: A Deep Learning Surrogate for Accelerated Pre-Docking Ligand Prioritization in Structure-Based Virtual Screening

Results indicate that 2D-based docking-score surrogate modeling can provide a reproducible and retrainable strategy for large-scale structure-based virtual screening by concentrating docking resources on a smaller, enriched subset of compounds.

Rank-Resolved Multi-Engine Docking and Optuna-Optimized Re-Ranking with ProDock for Virtual Screening

Structural investigation of re-docked actives showed that re-ranked poses were more native-like, with improved binding-site occupancy, reduced centroid displacement, and greater recovery of co-crystal interactions.

Harnessing machine learning, docking and molecular dynamics for the virtual screening of compounds as CDK4/6 dual inhibitors.

Development and validation of an attention-based cGAS-specific deep learning scoring function for structure-based virtual screening

DeepCGASPred is a cyclic GMP-AMP synthase (cGAS)-specific deep learning scoring function that integrates three-dimensional convolutional neural networks with multi-head attention mechanisms and composite structural descriptors, including Structural Protein–Ligand Interaction Fingerprints (SPLIF), hydrogen bond features, and extended connectivity fingerprints (ECFP).

Free energy perturbation and machine learning-assisted identification of potential MAP3K8 hit molecules: a comprehensive structure- and ligand-based studies.

The convergence of docking, dynamics, and free-energy results prioritized PM2, PM3, and PM4 as promising MAP3K8 hit candidates, which require further experimental validation and lead optimization.

Beyond the Score: Fixed-Budget Benchmarking of Virtual Screening Integration Strategies for Decision-Centric Drug Discovery

Virtual screening (VS) workflows often combine structure- and ligand-based methods; however, their value depends on the number of compounds that can be tested. We benchmarked 20 fixed-budget strategies derived from molecular docking (GNINA CNN score), maximum common substructure (MCS) similarity, and a calibrated machine-learning (ML)-QSAR classifier across five pharmacologically diverse targets. Individual methods, best-rank and worst-rank fusion, mean-rank consensus, and sequential funnels were evaluated at 1%, 5%, and 10% library fractions, with every strategy selecting the same number of compounds. ML-QSAR was the strongest standalone method, recovering 47.6%, 81.6%, and 84.4% of actives at the three cutoffs. At the 1% budget, ML-QSAR achieved the highest mean hit recovery (47.6% recall; 99.2% precision). At 5% and 10%, best-rank fusion of QSAR and MCS produced the highest mean recall (83.2% and 86.4%). Among the sequential workflows, QSAR → MCS achieved the highest 1% hit recovery (45.2 ± 3.3% recall), whereas docking-first funnels consistently underperformed under the default, non-optimized conditions evaluated in this study. Target-level results showed substantial variability in MCS-containing workflows and limited benefits from adding docking without target-specific optimization. Under matched assay budgets, a validated ligand-based predictor or a simple two-method rank-fusion scheme provided the highest observed mean hit recovery without requiring elaborate integration.