COMPUTATIONAL CHEMISTRY FOR IMPROVED NATURAL COMPOUNDS-TARGET AFFINITY PREDICTIONS
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Date
2024-04-19
Authors
Sabyrbek, Aruzhan
Gole, Daria
Bolatov, Arman
Nurbayev, Zhanbolat
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
The rapid evolution of pathogens underscores an urgent need for accelerated therapeutic development strategies. With an emphasis on natural compounds, this work expands the field of drug repositioning by employing machine learning(ML) techniques to forecast compound-protein interactions that may have therapeutic consequences. Our method makes use of several pre-trained Drug-Target Affinity (DTA) models, such as GraphDTA, MLT-LE, and DeepDTA, to predict binding affinities between protein targets listed in BindingDB and natural products sourced from the COCONUT database. This integration aims to create a robust database facilitating the repurposing of naturally occurring compounds, which are often overlooked in traditional synthetic drug pipelines.
Description
Keywords
Type of access: Restricted, Computational Chemistry, Natural Compounds, Compounds-Target Affinity Predictions, Machine Learning
Citation
Sabyrbek, A.., Gole, D.., Bolatov, A.., & Nurbayev, Z. (2024). Computational chemistry for improved Natural Compounds-Target affinity predictions. Nazarbayev University School of Engineering and Digital Sciences