Abstract
Computer-aided drug design (CADD) is the framework in which the huge amount of data accumulated by high-throughput experimental methods used in drug design is quantitatively studied. Its objectives include pattern recognition, biomarker identification and/or classification, etc. In order to achieve these objectives, machine learning algorithms and especially artificial neural networks (ANNs) have been used over ADMET factor testing and QSAR modeling evaluation. This paper provides an overview of the current trends in CADD-applied ANNs, since their use was re-boosted over a decade ago.
Keywords
- Artificial neural networks (ANNs)
- Computer-aided drug design (CADD)
- Quantitative structure-activity relationship (QSAR) models
- Molecular predictors
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The author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter overviewed in this manuscript. Royalties, employment, consultancies, honoraria, stock ownership or options, expert testimony, and grants or patents received or pending included.
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Cheirdaris, D.G. (2020). Artificial Neural Networks in Computer-Aided Drug Design: An Overview of Recent Advances. In: Vlamos, P. (eds) GeNeDis 2018. Advances in Experimental Medicine and Biology, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-32622-7_10
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DOI: https://doi.org/10.1007/978-3-030-32622-7_10
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