Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications
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Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.
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Machine learning has the capacity to extract complex and abstract features in pharmacology and bioinformatics applications.
“due to its capacity of extracting complex and abstract features in pharmacology and bioinformatics applications [160]”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [160]
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- Authors
- Pastur-Romay, L., Cedrón, F., Pazos, A., & Porto-Pazos, A.
- Affiliations
- Universidade da Coruña (ES)
- Corresponding author
- Porto-Pazos, Ana
- Venue
- International Journal of Molecular Sciences
- Publisher
- MDPI AG
- Published
- 2016-08-11
- Volume
- 17
- Issue
- 8
- Pages
- 1313
- ISSN
- 1422-0067 (ISSN-L)
- PMC
- PMC5000710
- OpenAlex
- W2517582793
- Cited by
- 109
- References
- 127
- Citations by year
- 2026: 2 · 2025: 11 · 2024: 10 · 2023: 15 · 2022: 12 · 2021: 11 · 2020: 9 · 2019: 11 · 2018: 17 · 2017: 6 · 2016: 5
- FWCI
- 1.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Molecular Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
- RUC (Universidade Da Coruña) (submittedVersion, cc-by)
- Funders
- European Commission (2013-2016)
- Xunta de Galicia (R2014/039, GRC2014/049)
- Instituto de Salud Carlos III (2013-2016, PI13/00280)
- Spanish National Plan for Scientific and Technical Research and Innovation
- Center for Innovative Medicine
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Keywords
- deep neural network, neuromorphic hardware, drug design, virtual screening, QSAR, protein structure prediction, SpiNNaker, astrocytes, glial cells, bioinformatics
- MeSH terms
- Algorithms, Animals, Humans, Neural Networks, Computer, Computational Biology, Quantitative Structure-Activity Relationship
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