A review on machine learning approaches and trends in drug discovery
| Autor: | Victor Maojo, Nereida Rodriguez-Fernandez, Adrian Carballal, Carlos Fernandez-Lozano, Francisco J. Novoa, Alejandro Pazos, Paula Carracedo-Reboredo, Francisco Cedrón, Jose Liñares-Blanco |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
GNN
Graph Neural Networks Computer science CPI Compound-protein interaction Review computer.software_genre NB Naive Bayes FNN Fully Connected Neural Networks Biochemistry Field (computer science) AUC Area under the Curve PCA Principal Component Analyisis Machine Learning DNA Deoxyribonucleic acid ADMET Absorption distribution metabolism elimination and toxicity Structural Biology Drug Discovery MCC Matthews correlation coefficient GEO Gene Expression Omnibus t-SNE t-Distributed Stochastic Neighbor Embedding Drug discovery QSAR Cheminformatics BBB Blood–Brain barrier OOB Out of Bag Molecular Descriptors Computer Science Applications CNS Central Nervous System MKL Multiple Kernel Learning ML Machine Learning CNN Convolutional Neural Networks RF Random Forest Biotechnology CV Cross Validation SVM Support Vector Machines Quantitative structure–activity relationship GCN Graph Convolutional Networks MACCS Molecular ACCess System Biophysics QSAR Quantitative structure–activity relationship FP Fringerprints Machine learning SMILES simplified molecular-input line-entry system ADR Adverse Drug Reaction KEGG Kyoto Encyclopedia of Genes and Genomes WHO World Health Organization MD Molecular Descriptors AI Artificial Intelligence Deep Learning Component (UML) GO Gene Ontology ECFP Extended Connectivity Fingerprints Genetics Set (psychology) ComputingMethodologies_COMPUTERGRAPHICS APFP Atom Pairs 2d FingerPrint business.industry DL Deep Learning Deep learning CDK Chemical Development Kit RNA Ribonucleic Acid FDA Food and Drug Administration ANN Artificial Neural Networks FS Feature Selection TCGA The Cancer Genome Atlas State (computer science) Artificial intelligence business computer TP248.13-248.65 |
| Zdroj: | Computational and Structural Biotechnology Journal, Vol 19, Iss, Pp 4538-4558 (2021) RUC. Repositorio da Universidade da Coruña instname Computational and Structural Biotechnology Journal |
| ISSN: | 2001-0370 |
| Popis: | Graphical abstract Highlights • Machine Learning in drug discovery has greatly benefited the pharmaceutical industry. • Application of machine algorithms must entail a robust design in real clinical tasks. • Trending machine learning algorithms in drug design: NB, SVM, RF and ANN. Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years. |
| Databáze: | OpenAIRE |
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