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Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Review Article

Application of Deep Learning Neural Networks in Computer-aided Drug Discovery: A Review

In Press, (this is not the final "Version of Record"). Available online 23 January, 2024
Author(s): Jay Shree Mathivanan, Victor Violet Dhayabaran, Mary Rajathei David, Muthugobal Bagayalakshmi Karuna Nidhi, Karuppasamy Muthuvel Prasath and Suvaiyarasan Suvaithenamudhan*
Published on: 23 January, 2024

DOI: 10.2174/0115748936276510231123121404

Price: $95

Abstract

Computer-aided drug design has an important role in drug development and design. It has become a thriving area of research in the pharmaceutical industry to accelerate the drug discovery process. Deep learning, a subdivision of artificial intelligence, is widely applied to advance new drug development and design opportunities. This article reviews the recent technology that uses deep learning techniques to ameliorate the understanding of drug-target interactions in computer-aided drug discovery based on the prior knowledge acquired from various literature. In general, deep learning models can be trained to predict the binding affinity between the protein-ligand complexes and protein structures or generate protein-ligand complexes in structure-based drug discovery. In other words, artificial neural networks and deep learning algorithms, especially graph convolutional neural networks and generative adversarial networks, can be applied to drug discovery. Graph convolutional neural network effectively captures the interactions and structural information between atoms and molecules, which can be enforced to predict the binding affinity between protein and ligand. Also, the ligand molecules with the desired properties can be generated using generative adversarial networks.

Keywords: Deep learning, artificial intelligence, artificial neural networks, machine learning, convolutional neural network, graph convolutional neural networks.


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