Evolutionary‑Driven Bayesian Optimization for Automated Molecular Docking with AutoDock Vina
TL;DR
This work introduces Evolutionary Driven Bayesian Optimization (EA-BO), a surrogate-based framework designed for efficient exploration under strict evaluation budgets and demonstrates that EA-BO provides data-efficient strategy for locating promising docking regions when computational cost limit traditional approaches.
Abstract
Defining the docking search space is a critical yet often overlooked step in molecular docking, especially for receptors that lack clear or well-structured binding pockets. We address this challenge by formulating grid box placement as a global, expensive black-box optimization problem and introduce Evolutionary Driven Bayesian Optimization (EA-BO), a surrogate-based framework designed for efficient exploration under strict evaluation budgets. EA-BO integrates Gaussian Process models with a Matérn 5/2 kernel, LBFGS-B hyperparameter tuning, and CMA-ES-driven acquisition maximization to balance exploration and exploitation in a computationally demanding setting. We evaluate EA-BO on the interleukin-6 receptor, where manual grid selection and standard heuristics frequently fail. Across a panel of resveratrol-like ligands selected through ECFP4-based similarity screening, EA-BO consistently identifies interaction hotspots and converges faster than Optuna, Gaussian Process Bayesian Optimization, and Scikit-Optimize, while also outperforming grid centers reported in previous IL-6R docking studies. As a second contribution, we leverage the optimized docking region to rank and select the best-performing ligand from the resveratrol analogue set based on binding affinity with the receptor, demonstrating how automated grid optimization directly facilitates ligand prioritization. These results demonstrate that EA-BO provides data-efficient strategy for locating promising docking regions when computational cost limit traditional approaches.