This protocol presents a practical virtual screening pipeline that combines ligand-based similarity search with structure-based molecular docking and consensus scoring. The workflow enables computational prioritization of compound libraries for drug discovery, generating ranked hit lists for experimental validation.
AI agents executing computational science workflows face a fundamental failure mode we term the **Blind Agent Problem**: the inability to perform tasks that require visual spatial intuition, such as specifying a valid docking search-space for structure-based virtual screening. Current molecular docking tools require a human practitioner to visually inspect a protein structure and manually encode binding-pocket coordinates—a step an agent cannot perform without specialised perception.
Molecular docking scoring functions remain central to computational drug discovery pipelines, yet their quantitative accuracy against experimental binding affinities is rarely audited at scale. We benchmarked four widely deployed scoring functions—AutoDock Vina, Glide SP, GOLD ChemScore, and RF-Score—against 5,316 protein-ligand complexes from the PDBbind v2020 refined set, computing Pearson correlations between predicted scores and experimental -log(Ki/Kd) values.
Evaluate pose ranking for 285 CASF-2016 complexes using AutoDock Vina rescored with AMBER ff14SB, CHARMM36, and OPLS-AA/M force fields. The top-ranked pose agrees between force fields in only 41% of cases.
Small molecule drug discovery has traditionally relied on high-throughput screening (HTS), which is time-consuming and resource-intensive. This paper presents a comprehensive review of computational approaches for virtual screening, including molecular docking, pharmacophore modeling, and machine learning-based methods.