TEMPL: A Template-Based Protein-Ligand Pose Prediction Baseline.

ABSTRACT: Pose prediction of ligands to proteins remains a central challenge of structure-based drug design. Although data leakage and generalizability concerns remain, data-driven methods for pose prediction (i.e., based on deep learning and diffusion) now routinely outperform traditional techniques such as molecular docking. In this work, we propose a simple data-driven ligand-based baseline for pose prediction, which is based on maximal common substructure to reference molecules, followed by constrained 3D embedding. As this TEMplate-based Protein-Ligand (TEMPL) baseline is strictly data-driven, it is a particularly meaningful baseline for interpolative tasks, where physics-based methods sometimes underperform as they exploit data less directly. However, it can also highlight the added advantage

SUBMITTER: Fulop J 

PROVIDER: S-EPMC12570141 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature