# Fine-Tuning DiffDock‑L for Allosteric Kinase Docking

## 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.

## Key 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 model
is found to markedly improve pose-recovery metrics for Type III/IV
allosteric binders while preserving the performance on ATP-site ligands.
Binding-mode-resolved evaluations and comparisons with cofolding models
such as AlphaFold3 and Boltz-2 highlight how targeted retraining reshapes
the generative model’s sampling distribution, offering practical
guidance for adapting AI-driven docking to challenging, low-data binding
modes in kinase structure-based drug design.