A DEEP LEARNING APPROACH FOR DRUG-TARGET AFFINITY PREDICTIONCOMPREHENSIVE TRANSACTIVE ENERGY MANAGEMENT SYSTEMS
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Date
2021-05
Authors
Li, Albina
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
The identification of drug-target interaction (DTI) is a crucial part of the drug discovery
and development process. In vitro and in vivo experiments for drug target
validation and screening are, however, very expensive and take a lot of time to complete.
There experiment on large scale are unfeasible, thus there is a huge demand for
the development of computational in silico alternatives for DTI prediction. Several
statistical and machine learning-based methods have been developed over time that
focused on the binary classification of DTI. However, these interactions are very complex,
as there is a dynamic fluctuation present between the protein and the bound
compound and a continuous mutually flexible adjustment, which needs to be simplified
by reaching an equilibrium state characterised by well established binding affinity
descriptor. The exact estimation of the binding affinity in the DTI still remains a
challenge to this day. Various machine and deep learning methodologies have been
developed that utilize different feature representation approaches for both compounds
and proteins. These algorithms generally utilize as input limited chemical information,
which may not be meaningful and intuitive enough to be used as an effective
descriptor.
In this work I am addressing the limitation of current methods by introducing
a deep learning-based model that makes use of chemical representations of the
molecules. Results of experiments on two benchmark datasets demonstrate that the
proposed model outperforms the baseline model, which is one of the state-of-the-art
methods in the drug-target affinity (DTA) prediction field.
Description
Keywords
Type of access: Open Access, Research Subject Categories::TECHNOLOGY, drug-target interaction, DTI, DeepDTA, convolutional neural networks, CNNs
Citation
Li, A. (2021). A Deep Learning Approach for Drug-Target Affinity Predictioncomprehensive Transactive Energy Management Systems (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstan