Fine-Tuning DiffDock‑L for Allosteric Kinase Docking
Eric Chen, Justin Green, Yingkai Zhang
TL;DR
This paper improves a docking model to better predict the correct binding positions of allosteric kinase inhibitors.
Contribution
The study introduces a fine-tuned version of DiffDock-L, optimized for allosteric kinase ligand prediction.
Findings
Fine-tuning strategies significantly improved pose recovery for Type III/IV allosteric binders.
The model preserved performance on ATP-site ligands while enhancing allosteric predictions.
Comparisons with cofolding models showed how retraining reshapes the model's sampling distribution.
Abstract
Allosteric kinase inhibitors are an important modality for overcoming resistance and achieving selectivity, yet most structure-based docking and deep generative models are trained predominantly on orthosteric protein–ligand complexes. As a result, current methods often misplace allosteric kinase ligands into the adenosine triphosphate (ATP)-binding site and fail to recover the correct binding mode. Here we curate AlloSet, a kinome-wide, time-split data set of kinase–ligand complexes annotated by binding mode, to systematically evaluate and fine-tune the diffusion-based docking model DiffDock-L for allosteric pose prediction. We explore several fine-tuning strategies, including increased dropout, freezing of torsion parameters with translation/rotation-only fine-tuning, and molecular dynamics-based supersampling of receptor conformations and ligand poses. The resulting DiffDock-L-Allo…
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Taxonomy
TopicsComputational Drug Discovery Methods · Protein Structure and Dynamics · Cell Image Analysis Techniques