Version 1
: Received: 23 August 2024 / Approved: 23 August 2024 / Online: 23 August 2024 (10:10:20 CEST)
How to cite:
Li, W. Inter-molecular Binding Affinity Synthetic Data Augmentation Transforms the Landscape of Computational Biomolecule Design and Discovery. Preprints2024, 2024081713. https://doi.org/10.20944/preprints202408.1713.v1
Li, W. Inter-molecular Binding Affinity Synthetic Data Augmentation Transforms the Landscape of Computational Biomolecule Design and Discovery. Preprints 2024, 2024081713. https://doi.org/10.20944/preprints202408.1713.v1
Li, W. Inter-molecular Binding Affinity Synthetic Data Augmentation Transforms the Landscape of Computational Biomolecule Design and Discovery. Preprints2024, 2024081713. https://doi.org/10.20944/preprints202408.1713.v1
APA Style
Li, W. (2024). Inter-molecular Binding Affinity Synthetic Data Augmentation Transforms the Landscape of Computational Biomolecule Design and Discovery. Preprints. https://doi.org/10.20944/preprints202408.1713.v1
Chicago/Turabian Style
Li, W. 2024 "Inter-molecular Binding Affinity Synthetic Data Augmentation Transforms the Landscape of Computational Biomolecule Design and Discovery" Preprints. https://doi.org/10.20944/preprints202408.1713.v1
Abstract
The advancement of computational drug discovery and design necessitates continuous innovation to enhance the accuracy and scope of predictive models for early-stage drug research and development. This article introduces a novel workflow for in silico generation of structural and intermolecular binding affinity data with reasonable accuracy, combining two computational tools: Modeller and Prodigy. By leveraging synthetic structural and biophysical data, this approach addresses the limitations of existing experimental datasets, generating extensive, high-quantity binding affinity data with reasonable accuracy for biomolecular binding pairs, which broadens the horizon of computational biomolecule design and discovery by enabling extensive exploration of the sequence space of biomolecular binding pairs, and narrows the gap between experimental binding affinity data and its unexplored territories. Overall, this article presents a methodological advance to enhance the accuracy and scope of computational biomolecule discovery and design, paves the way for the development of preclinical candidates with improved efficacy and specificity, and holds transformative potential for further advancements in artificial intelligence-enabled biomolecule discovery and design in the future.
Keywords
Inter-molecular Binding Affinity; Synthetic Data Augmentation; Computational Biomolecule Design and Discovery; Site-specific mutation; Structural biophysics
Subject
Biology and Life Sciences, Biophysics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
