T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction With Uncertainty-Aware Self-Learning for Protein-Specific Alignment DOI Creative Commons
Gregory W. Kyro, Anthony M. Smaldone, Yu Shee

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 20, 2024

Abstract There is significant interest in targeting disease-causing proteins with small molecule inhibitors to restore healthy cellular states. The ability accurately predict the binding affinity of molecules a protein target silico enables rapid identification candidate and facilitates optimization on-target potency. In this work, we present T-ALPHA, novel deep learning model that enhances protein-ligand prediction by integrating multimodal feature representations within hierarchical transformer framework capture information critical predicting affinity. T-ALPHA outperforms all existing models reported literature on multiple benchmarks designed evaluate scoring functions. Remarkably, maintains state-of-the-art performance when utilizing predicted structures rather than crystal structures, powerful capability real-world drug discovery applications where experimentally determined are often unavailable or incomplete. Additionally, an uncertainty-aware self-learning method for protein-specific alignment does not require additional experimental data, demonstrate it improves T-ALPHA’s rank compounds biologically targets such as SARS-CoV-2 main protease epidermal growth factor receptor. To facilitate implementation reproducibility results presented paper, have made our software available at https://github.com/gregory-kyro/T-ALPHA .

Language: Английский

T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein–Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment DOI
Gregory W. Kyro, Anthony M. Smaldone, Yu Shee

et al.

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 18, 2025

There is significant interest in targeting disease-causing proteins with small molecule inhibitors to restore healthy cellular states. The ability accurately predict the binding affinity of molecules a protein target silico enables rapid identification candidate and facilitates optimization on-target potency. In this work, we present T-ALPHA, novel deep learning model that enhances protein–ligand prediction by integrating multimodal feature representations within hierarchical transformer framework capture information critical predicting affinity. T-ALPHA outperforms all existing models reported literature on multiple benchmarks designed evaluate scoring functions. Remarkably, maintains state-of-the-art performance when utilizing predicted structures rather than crystal structures, powerful capability real-world drug discovery applications where experimentally determined are often unavailable or incomplete. Additionally, an uncertainty-aware self-learning method for protein-specific alignment does not require additional experimental data demonstrate it improves T-ALPHA's rank compounds biologically targets such as SARS-CoV-2 main protease epidermal growth factor receptor. To facilitate implementation reproducibility results presented paper, made our software available at https://github.com/gregory-kyro/T-ALPHA.

Language: Английский

Citations

1
Emerging Artificial Intelligence Methodologies in Computational Biology DOI
Nguyen Quoc Khanh Le, Binh Nguyen

Journal of Molecular Biology, Journal Year: 2025, Volume and Issue: unknown, P. 169002 - 169002

Published: Feb. 1, 2025

Language: Английский

Citations

0
Enhanced inhibitor–kinase affinity prediction via integrated multimodal analysis of drug molecule and protein sequence features DOI
Zhenxing Li,

Kaitai Han,

Zijun Wang

et al.

International Journal of Biological Macromolecules, Journal Year: 2025, Volume and Issue: unknown, P. 142871 - 142871

Published: April 1, 2025

Language: Английский

Citations

0
T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction With Uncertainty-Aware Self-Learning for Protein-Specific Alignment DOI Creative Commons
Gregory W. Kyro, Anthony M. Smaldone, Yu Shee

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 20, 2024

Abstract There is significant interest in targeting disease-causing proteins with small molecule inhibitors to restore healthy cellular states. The ability accurately predict the binding affinity of molecules a protein target silico enables rapid identification candidate and facilitates optimization on-target potency. In this work, we present T-ALPHA, novel deep learning model that enhances protein-ligand prediction by integrating multimodal feature representations within hierarchical transformer framework capture information critical predicting affinity. T-ALPHA outperforms all existing models reported literature on multiple benchmarks designed evaluate scoring functions. Remarkably, maintains state-of-the-art performance when utilizing predicted structures rather than crystal structures, powerful capability real-world drug discovery applications where experimentally determined are often unavailable or incomplete. Additionally, an uncertainty-aware self-learning method for protein-specific alignment does not require additional experimental data, demonstrate it improves T-ALPHA’s rank compounds biologically targets such as SARS-CoV-2 main protease epidermal growth factor receptor. To facilitate implementation reproducibility results presented paper, have made our software available at https://github.com/gregory-kyro/T-ALPHA .

Language: Английский

Citations

0