Deep Learning in Drug Discovery
Parinita Das*
ICAR-Indian Agricultural Statistics Research Institute, New Delhi, Delhi (110 012), India
Mamatha Y.S.
ICAR-Indian Agricultural Statistics Research Institute, New Delhi, Delhi (110 012), India
DOI: NIL
Keywords: Artificial Intelligence, Deep learning, Drug discovery, Machine learning
Abstract
Deep learning (DL) techniques have been very effective and widely employed to build artificial intelligence (AI) in practically every sector over the past ten years, particularly after they acquired their proud record on computational Go. In comparison to conventional machine learning (ML) techniques, deep learning (DL) methods still have a long way to go before they are widely accepted in the discovery and development of small molecule drugs. Additionally, there is still much effort to be done in order to popularise and apply DL for research purposes, such as for the development and investigation of small molecule drugs. In this article, we focused on a few of the most popular DL strategies and how they were applied to the drug development process.
Downloads
not found
Reference
Gómez-Bombarelli, R., Wei, J.N., Duvenaud, D., Hernández-Lobato, J.M., Sánchez-Lengeling, B., Sheberla, D., Aspuru-Guzik, A., 2018. Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Science 4(2), 268-276. DOI: 10.1021/acscentsci.7b00572.
Kwon, S., Yoon, S., 2017. Deepcci: End-to-end deep learning for chemical-chemical interaction prediction. In: Proceedings of the 8 th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, August. pp. 203-212. DOI: 10.1145/3107411.3107451.
Liu, P., Leung, K.S., 2018. & quot; Accelerating Drug Discovery Using Convolution Neural Network Based Active Learning, & quot; TENCON 2018 - 2018 IEEE Region 10 Conference, pp. 2005-2010. DOI: 10.1109/TENCON.2018.8650298.
Wang, Y.B., You, Z.H., Yang, S., Yi, H.C., Chen, Z.H., Zheng, K., 2020. A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network. BMC Medical Informatics and Decision Making 20(2), 1-9. DOI: 10.1186/s12911-020-1052-0.
Zhang, C., Lu, Y., Zang, T., 2022. CNN-DDI: a learning-based method for predicting drug–drug interactions using convolution neural networks. BMC Bioinformatics 23, 88. DOI: 10.1186/s12859-022-04612-2.