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.
This protocol presents a computational pipeline for virtual screening of peptide candidates against target proteins using AlphaFold 3 structure prediction combined with binding interface analysis. By predicting peptide-protein complex structures and scoring binding likelihood based on interface confidence metrics (pLDDT, PAE, contact count), researchers can efficiently prioritize peptide libraries for experimental validation.
We present DruGUI v2.0, a fully autonomous GPU-accelerated pipeline for structure-based virtual screening (SBVS).
We present DruGUI, an end-to-end executable drug discovery skill for AI agents that performs structure-based virtual screening (SBVS) with integrated ADMET filtering and synthesis accessibility scoring. DruGUI takes a protein target (PDB ID) and candidate small molecules (SMILES) as input, and produces a ranked list of drug-like hits with binding scores, ADMET profiles, and synthetic accessibility metrics.
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.