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- Title
Deep learning in structural bioinformatics: current applications and future perspectives.
- Authors
Kumar, Niranjan; Srivastava, Rakesh
- Abstract
In this review article, we explore the transformative impact of deep learning (DL) on structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by extensive data, accessible toolkits and robust computing resources. As big data continue to advance, DL is poised to become an integral component in healthcare and biology, revolutionizing analytical processes. Our comprehensive review provides detailed insights into DL, featuring specific demonstrations of its notable applications in bioinformatics. We address challenges tailored for DL, spotlight recent successes in structural bioinformatics and present a clear exposition of DL—from basic shallow neural networks to advanced models such as convolution, recurrent, artificial and transformer neural networks. This paper discusses the emerging use of DL for understanding biomolecular structures, anticipating ongoing developments and applications in the realm of structural bioinformatics.
- Subjects
STRUCTURAL bioinformatics; DEEP learning; SCIENTIFIC Revolution; DRUG discovery
- Publication
Briefings in Bioinformatics, 2024, Vol 25, Issue 3, p1
- ISSN
1467-5463
- Publication type
Article
- DOI
10.1093/bib/bbae042