Harnessing angular geometry in deep learning for protein–ligand binding affinity prediction

The angular features are effective, lightweight, and robust descriptors for binding affinity prediction. These results highlight angular geometry as a valuable direction for future structure-based drug discovery. The program and data of AAP are publicly available at https://github.com/juliacse06/AAP.

Authors

Md Khaled Ben Islam (ORCID: https://orcid.org/0000-0003-4841-9604)
Jiffriya Mohamed Abdul Cader
Julia Rahman (ORCID: https://orcid.org/0000-0001-5005-9922)
Mohammed Eunus Ali
M.A. Hakim Newton
Abdul Sattar

Institutions

Griffith University (AU)
Monash University (AU)
University of Newcastle Australia (AU)

Publication Details

Journal: Computer Methods and Programs in Biomedicine
Published: 2026-02-12
DOI: https://doi.org/10.1016/j.cmpb.2026.109282
Primary Topic: Protein Structure and Dynamics
Type: article
Field-Weighted Citation Impact: 0.00

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article

Harnessing angular geometry in deep learning for protein–ligand binding affinity prediction

Md Khaled Ben Islam, Jiffriya Mohamed Abdul Cader, Julia Rahman, Mohammed Eunus Ali et al.

Feb 12, 2026

Computer Methods and Programs in Biomedicine

Protein Structure and Dynamics

article

Harnessing angular geometry in deep learning for protein–ligand binding affinity prediction

Md Khaled Ben Islam, Jiffriya Mohamed Abdul Cader, Julia Rahman, Mohammed Eunus Ali, M.A. Hakim Newton, Abdul Sattar

February 12, 2026article en

Abstract

The angular features are effective, lightweight, and robust descriptors for binding affinity prediction. These results highlight angular geometry as a valuable direction for future structure-based drug discovery. The program and data of AAP are publicly available at https://github.com/juliacse06/AAP.

Computer Methods and Programs in BiomedicineVol. 278

Griffith University (AU), Monash University (AU), University of Newcastle Australia (AU)

Openalex Percentile: Top 58%

Protein Structure and Dynamics

DOI Open Access PDF OpenAlex

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