CompassDock: Comprehensive Accurate Assessment Approach for Deep Learning-Based Molecular Docking in Inference and Fine-Tuning
Ahmet Sarigun, Vedran Franke, Bora Uyar, Altuna Akalin

TL;DR
This paper introduces Compass, a comprehensive assessment framework for molecular docking that evaluates physical, chemical, and bioactivity properties, and demonstrates how fine-tuning with Compass scores improves ligand conformation quality and favorability.
Contribution
The paper presents Compass, a novel assessment approach combining physical, chemical, and bioactivity evaluations, and integrates it with deep learning docking methods for improved accuracy and model fine-tuning.
Findings
Compass reveals substantial noise in PDBBind data.
Fine-tuning with Compass improves ligand favorability.
Compass-based fine-tuning enhances docking quality without sacrificing physical properties.
Abstract
Datasets used for molecular docking, such as PDBBind, contain technical variability - they are noisy. Although the origins of the noise have been discussed, a comprehensive analysis of the physical, chemical, and bioactivity characteristics of the datasets is still lacking. To address this gap, we introduce the Comprehensive Accurate Assessment (Compass). Compass integrates two key components: PoseCheck, which examines ligand strain energy, protein-ligand steric clashes, and interactions, and AA-Score, a new empirical scoring function for calculating binding affinity energy. Together, these form a unified workflow that assesses both the physical/chemical properties and bioactivity favorability of ligands and protein-ligand interactions. Our analysis of the PDBBind dataset using Compass reveals substantial noise in the ground truth data. Additionally, we propose CompassDock, which…
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Taxonomy
TopicsMolecular Communication and Nanonetworks · Advanced biosensing and bioanalysis techniques · Quantum-Dot Cellular Automata
