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
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