A review on machine learning approaches and trends in drug discovery

[Display omitted] •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...

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Vydané v:	Computational and structural biotechnology journal Ročník 19; s. 4538 - 4558
Hlavní autori:	Carracedo-Reboredo, Paula, Liñares-Blanco, Jose, Rodríguez-Fernández, Nereida, Cedrón, Francisco, Novoa, Francisco J., Carballal, Adrian, Maojo, Victor, Pazos, Alejandro, Fernandez-Lozano, Carlos
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.01.2021 Research Network of Computational and Structural Biotechnology Elsevier
Predmet:	biotechnology Cheminformatics chemoinformatics computer science Deep Learning Drug Discovery drugs Machine Learning Molecular Descriptors precision medicine QSAR Review ANN Cheminformatics CNN FDA BBB RNA MACCS GNN FNN ECFP DL t-SNE CNS FP Molecular Descriptors CDK SVM Machine Learning FS ADMET ADR AUC MD KEGG ML WHO OOB APFP AI GO SMILES MCC Drug Discovery Deep Learning TCGA PCA MKL GEO CV GCN NB RF QSAR DNA CPI
ISSN:	2001-0370, 2001-0370
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