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Status: Unchecked ×Keyword: absolute binding free energy ×Clear all

2 claims from 1 paper

1. Computer Science › Computational Drug Discovery Methods

   Integrating Conformational Sampling and Siamese Learning to Predict Mutation-Induced Binding Affinity Changes in Abelson Tyrosine Kinase and Its Ligands

   Xie, Ma, Lu et al. · Journal of Chemical Theory and Computation · 2026

   The authors combined AlphaFold 2 conformational sampling with a Siamese neural network (SIGMA-Net) to predict how mutations change drug binding affinity in Abelson tyrosine kinase.

   Unchecked2 claims
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   1. UncheckedA new method predicts mutation-driven binding changes better than docking and TriG-Net for five of six kinase inhibitors across 31 ABL mutants.“Benchmarking on the tyrosine kinase inhibitors (TKI) data set and the refined set of PDBbind, our proposed approach achieves higher correlation coefficients for five of six TKI molecules across 31 ABL mutants, outperforming molecular docking and trichannel graph network (TriG-Net).”
   2. UncheckedThe authors' method predicts absolute binding free energy about as well as Boltz-2, but Boltz-2 does better on relative binding free energy in the systems tested.“It achieves absolute binding free energy (ABFE) prediction performance comparable to that of state-of-the-art models such as Boltz-2, whereas Boltz-2 demonstrates better performance in relative binding free energy (RBFE) prediction in the evaluated systems.”