Review of Machine Learning Technologies and Neural Networks in Drug Synergy Combination pharmacological research
| Autor: | Konstantin Koshechkin, Artur S. Ter-Levonian |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Deep Learning drug synergy informatics machine learning neural networks preclinical study Computer science Pharmacological research RM1-950 Machine learning computer.software_genre law.invention 03 medical and health sciences Preclinical research 0302 clinical medicine law Pharmacology (medical) Pharmacology Clinical pharmacology Artificial neural network business.industry Treatment regimen Deep learning Regimen 030104 developmental biology 030220 oncology & carcinogenesis Therapeutics. Pharmacology Artificial intelligence business computer |
| Zdroj: | Research Results in Pharmacology, Vol 6, Iss 3, Pp 27-32 (2020) |
| ISSN: | 2658-381X |
| DOI: | 10.3897/rrpharmacology.6.49591 |
| Popis: | Introduction: Nowadays an increase in the amount of information creates the need to replace and update data processing technologies. One of the tasks of clinical pharmacology is to create the right combination of drugs for the treatment of a particular disease. It takes months and even years to create a treatment regimen. Using machine learning (in silico) allows predicting how to get the right combination of drugs and skip the experimental steps in a study that take a lot of time and financial expenses. Gradual preparation is needed for the Deep Learning of Drug Synergy, starting from creating a base of drugs, their characteristics and ways of interacting. Aim: Our review aims to draw attention to the prospect of the introduction of Deep Learning technology to predict possible combinations of drugs for the treatment of various diseases. Materials and methods: Literary review of articles based on the PUBMED project and related bibliographic resources over the past 5 years (2015–2019). Results and discussion: In the analyzed articles, Machine or Deep Learning completed the assigned tasks. It was able to determine the most appropriate combinations for the treatment of certain diseases, select the necessary regimen and doses. In addition, using this technology, new combinations have been identified that may be further involved in preclinical studies. Conclusions: From the analysis of the articles, we obtained evidence of the positive effects of Deep Learning to select “key” combinations for further stages of preclinical research. |
| Databáze: | OpenAIRE |
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