An Open-Source Reproducible Workflow for Pocket-Oriented Virtual Screening and ADME-Integrated Chemoinformatics: A Multi-Target Flavivirus Case Study

Abstract

Zika virus (ZIKV), yellow fever virus (YFV), West Nile virus (WNV), Usutu virus (USUV), and Saint Louis encephalitis virus (SLEV) remain major public health concerns, yet broad-spectrum antiviral options are limited. Here, we present an open-source, reproducible software workflow for pocket-oriented virtual screening and ADME-integrated chemoinformatics, designed to support standardized multi-target compound prioritization. As a case study, the workflow was applied to structural and nonstructural proteins from clinically relevant flaviviruses. Automated pocket detection using Concavity reduces site-selection bias by generating docking boxes from surface concavity clusters, while standardized downstream scripts parse docking logs, convert docking-derived binding energies into Kd-related metrics, integrate SwissADME descriptors, and compute LE, LLE, FQ, and drug-likeness rules. The framework also supports retrospective validation and comparative benchmarking using literature-supported reference compounds and target-specific plausibility checks. Rather than proposing experimentally validated antiviral candidates, this study provides a reusable computational framework for hypothesis generation, benchmarking, and downstream experimental prioritization in structure-based drug discovery. The workflow is modular and adaptable to other multi-target screening campaigns where integrated ranking across binding, physicochemical, and ADME dimensions is required. SUMMARYWe describe an open-source, reproducible software workflow that integrates pocket-oriented docking, ligand efficiency scoring, ADME descriptor integration, and multivariate chemoinformatics to standardize compound prioritization across multiple protein targets. The workflow combines open-source tools with auditable Bash, R, and Python scripts and is demonstrated through a multi-target flavivirus case study. Rather than claiming experimentally validated antiviral activity, the framework is intended to support hypothesis generation, retrospective benchmarking, transparent reporting, and downstream experimental prioritization.