Covalent virtual screening requires ranking compounds according to both noncovalent recognition and their ability to adopt a reaction-competent geometry with an appropriately reactive warhead. Here, we introduce BCover, a reaction-aware scoring suite that combines pre-reactive docking with quantum-chemistry-derived ligand reactivity descriptors, the electrostatic properties of the protein pocket. Quantum-chemical descriptors are aggregated using a nonlinear tree-based scoring model. BCover was evaluated retrospectively on the COValid benchmark, comprising nine targets and ten reactive sites, and compared with AutoDock, DOCK6, DOCKovalent, AlphaFold3 with Rosetta rescoring, and AlphaFold3 confidence-based ranking. BCover achieved an average adjusted LogAUC of 31% (57% max), an average ROC-AUC of 0.88 (0.96 max), an average EF1 of 18 (33 max). Its average LogAUC exceeded those of the classical docking methods and AlphaFold3-Rosetta, although AlphaFold3-mPAE provided the strongest overall enrichment. At an average runtime of about 10~s per ligand, BCover was approximately 25-fold faster than the evaluated AlphaFold3 workflows and achieved the highest average time-adjusted virtual-screening productivity index. Redocking experiments further showed that the method recovered near-native ligand conformations. These results demonstrate that combining docking-derived geometry with ligand local electronic reactivity and pocket electrostatics provides an efficient and interpretable strategy for covalent ligand prioritization. BCover is intended as a high-throughput screening method that complements more computationally demanding QM/MM and free-energy calculations during subsequent lead optimization.
Q-Score is introduced, encoding GNN-predicted orbital donor-acceptor energies into a weighted graph and scoring binding by solving a maximum-weight vertex clique problem via Digitized-Counterdiabatic QAOA, enriching for strong orbital interactions at twice the random rate.
Kangyu Zheng, Yidong Zhou, Ruihao Li et al.· 0 citations
False positives in virtual screening often arise when a single docking score or top-ranked pose is treated as sufficient evidence for binding. We extend the previously introduced ProDock software from a database-backed docking platform into a rank-resolved, multi-engine workflow for automated preparation, docking, pose analysis, and optimized re-ranking. The extended workflow combines local docking with GNINA and global docking with DiffDock with pose-level descriptors, namely binding-site occupancy, ligand localization, interaction-fingerprint similarity, and steric clash counts, together with Optuna -based threshold optimization. Across 43 DUDE-Z targets, the archived benchmark outputs reported higher enrichment values for CNN-based GNINA scores after optimization. CNNaffinity PR-AUC changed from 0.197 to 0.294 and LogAUC from 0.708 to 0.763, whereas empirical affinity ROC-AUC changed from 0.770 to 0.758. 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. The extension provides a reproducible framework for combining complementary docking engines with interpretable pose-level metrics before hit selection, thereby aiding the identification of true-positive candidates in virtual screening.
Lai Hoang Son Le, Thanh-An Pham, Ngoc Nguyen Tran et al.· bioRxiv· 0 citations
A category-stratified, statistically powered benchmark comparing pose prediction from receptor conformational ensembles against AlphaFold2, used as a matched static-structure baseline, across 29 protein–ligand systems spanning cryptic-pocket, induced-fit, water-mediated, and autoimmune-indication target classes is presented.
Ryan Varghese, Pooja Tiwary, Krishil Oswal· bioRxiv· 0 citations
The use of molecular docking in structure-based drug design is an indispensable tool in the field of computational chemistry; however, the application of molecular docking to metal complexes (such as Pt, Pd, Au, and Ag) poses unique challenges because of the variety of coordination geometries and electronic properties of metal complexes that are not represented by the typical force field. The literature (2010–2025) was reviewed and summarized in a peer-reviewed manner, concentrating on the application of inorganic complexes in the field of anticancer. The 13 representative case studies were identified by a systematic search of PubMed, Scopus and Web of Science and analysed for binding energies, RMSD values, software protocols and metal-specific parameterisation strategies. Majority of the studies used AutoDock4, AutoDock Vina or MOE with binding energies ranging from − 96.35 to − 1.48 kcal·mol⁻¹. Only three studies reported RMSD values < 2.0 Å, which is considered to be reliable pose prediction. The major drawbacks are: (i) absence of built-in metal coordination bond support in standard software; (ii) reporting of grid parameters and force field versions varies from one software to another; and (iii) very little quantum mechanical pre-optimization. Specialized tools (MetalDock, MCPB.py) increase accuracy, but are very time consuming to parameterise manually. Parameterisation, experimental validation (XRD, NMR, bioassays) and transparency of reporting are essential for reliable docking of metal complexes. Machine learning scoring functions are expected to be combined with quantum mechanical methods in the future. This review offers a practical guide and a warning for researchers who are designing metallodrugs by using docking.
Y. J. Sahar, A. Yasir, H. A. Kyhoiesh· Discover Chemistry· 0 citations
The rapid emergence of antimicrobial resistance demands the discovery of new antibacterial targets and inhibitors. Staphylococcus aureus filamenting temperature-sensitive protein (SaFtsZ), an essential cytoskeletal protein involved in bacterial cytokinesis and Z-ring formation, has gained attention as a promising target for antibacterial drug discovery. In the present study, an integrated computational strategy involving pharmacophore mapping, molecular docking, molecular dynamics (MD) simulations, and density functional theory (DFT) analysis was employed to identify potential SaFtsZ inhibitors. Initially, large compound library was screened from the Pharmit database using a structure-based pharmacophore model to identify molecules with key interaction features required for SaFtsZ inhibition. The selected 200 candidates were further evaluated through molecular docking to determine their binding affinity and interaction pattern within the active site of SaFtsZ. Among the screened molecules, compound 15 (CID 135468497) exhibited the highest binding affinity with a docking score of −10.8 kcal mol−1. Subsequent MD simulation confirmed the stability of the protein–ligand complex, while DFT analysis provided insights into the electronic characteristics and reactivity of compound 15. These findings highlight compound 15 as a computationally predicted scaffold for the development of SaFtsZ-targeted antibacterial agents. However, experimental validation is required to confirm the computational results.
Sundarrajan T., Neerugatti Dora Babu, A. K. N. et al.· RSC Advances· 0 citations