Use mk_prepare_receptor to create AutoDock Vina receptor PDBQT and box files while documenting parsers, residue decisions, outputs, validation, and provenance.
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Evidence-led articles on scientific methods, reproducible workflows, and software decisions.
What Is the PDBQT Format? Charges, Atom Types, and Torsions in AutoDock Vina
Learn what a PDBQT file stores, how ligand torsion trees differ from rigid receptor records, why bond orders can be lost, and what to inspect before an AutoDock Vina run.
Molecular Docking with Waters, Metals, and Cofactors: What Should Stay in the Receptor?
Decide which waters, metals, ions, and cofactors belong in a docking receptor by biological role, structural evidence, ligand mechanism, force-field compatibility, and controlled validation.
Local vs Cloud Molecular Docking Software: How to Choose the Right Workflow
Compare local, cloud, and hybrid molecular docking by real workload, data boundaries, runtime continuity, reproducibility, collaboration, governance, and total cost - not by deployment label alone.
Can Your Molecular Docking Study Be Re-Executed? An AutoDock Vina Reporting Checklist
A practical AutoDock Vina reporting checklist for preserving prepared inputs, software versions, search-box geometry, parameters, seeds, raw poses, failures, and the evidence needed to re-execute a docking study.
How to Choose a Protein Structure for Molecular Docking: Holo, Apo, AlphaFold, or an Ensemble?
Choose a receptor by target state, local pocket evidence, ligand context, and validation. Compare when a holo, apo, AlphaFold, or receptor ensemble best fits a reproducible protein-small-molecule docking campaign.
How to Select Compounds After Molecular Docking: From Ranked Results to an Experimental Shortlist
A docking screen ends with a ranked list, but an assay needs a defensible shortlist. Learn how to combine rank, pose quality, chemical diversity, artifact risk, availability, and controls before experimental testing.
In Silico Drug Discovery Today: How Structure Prediction, Virtual Screening, AI, and Experiments Work Together
Modern in silico drug discovery is not one algorithm. Learn how target evidence, structure prediction, molecular docking, virtual screening, AI, physics-based refinement, and experiments work as one traceable decision system.
AutoDock Vina Batch Docking: How to Screen a Ligand Library Without Losing Traceability
Screen multiple ligands without losing the scientific record. Preserve stable identities, one frozen Vina protocol, failures, ranked poses, exports, and local job history across the batch.
Why Some Ligands Fail Before AutoDock Vina: A Practical SDF Preflight Checklist
An SDF file can load and still be unfit for docking. Diagnose valence, aromaticity, fragments, stereochemistry, hydrogens, 3D geometry, minimization, and PDBQT failures before AutoDock Vina.