COMPUTATIONAL CHEMISTRY FOR IMPROVED NATURAL COMPOUNDS-TARGET AFFINITY PREDICTIONS

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dc.contributor.authorSabyrbek, Aruzhan
dc.contributor.authorGole, Daria
dc.contributor.authorBolatov, Arman
dc.contributor.authorNurbayev, Zhanbolat
dc.date.accessioned2024-06-15T06:05:32Z
dc.date.available2024-06-15T06:05:32Z
dc.date.issued2024-04-19
dc.description.abstractThe 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.en_US
dc.identifier.citationSabyrbek, A.., Gole, D.., Bolatov, A.., & Nurbayev, Z. (2024). Computational chemistry for improved Natural Compounds-Target affinity predictions. Nazarbayev University School of Engineering and Digital Sciencesen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/7871
dc.language.isoenen_US
dc.publisherNazarbayev University School of Engineering and Digital Sciencesen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectType of access: Restricteden_US
dc.subjectComputational Chemistryen_US
dc.subjectNatural Compoundsen_US
dc.subjectCompounds-Target Affinity Predictionsen_US
dc.subjectMachine Learningen_US
dc.titleCOMPUTATIONAL CHEMISTRY FOR IMPROVED NATURAL COMPOUNDS-TARGET AFFINITY PREDICTIONSen_US
dc.typeBachelor's thesis, capstone projecten_US
workflow.import.sourcescience

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