Machine-learning approaches in drug discovery: Methods and applications

Drug Discovery Today

Volumn 20, Issue 3, 2015, Pages 318-331

  Lavecchia, Antonio ^a  

^a UNIVERSITY OF NAPLES FEDERICO II   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ANTICONVULSIVE AGENT; ANTIINFLAMMATORY AGENT; ANTINEOPLASTIC AGENT; CANNABINOID DERIVATIVE; CYTOCHROME P450 INHIBITOR; DOPAMINE 1 RECEPTOR BLOCKING AGENT; ESTROGEN RECEPTOR AGONIST; HORMONE RECEPTOR STIMULATING AGENT; LIGAND; PROTEIN TYROSINE KINASE INHIBITOR; STEROID; UNCLASSIFIED DRUG;

ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; BAYESIAN LEARNING; BLOOD BRAIN BARRIER; CLASSIFICATION; CLASSIFICATION ALGORITHM; CLASSIFIER; COMPUTER MODEL; CRYSTAL STRUCTURE; DATA MINING; DECISION TREE; DRUG ABSORPTION; DRUG DESIGN; DRUG DISTRIBUTION; DRUG METABOLISM; DRUG PENETRATION; DRUG SOLUBILITY; HIGH THROUGHPUT SCREENING; HUMAN; HYBRID; K NEAREST NEIGHBOR; LIGAND BASED VIRTUAL SCREENING; MACHINE LEARNING; NAIVE BAYESIAN CLASSIFIER; PHYSICAL CHEMISTRY; PUBLICATION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RANDOM FOREST; REVIEW; SUPPORT VECTOR MACHINE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DRUG DEVELOPMENT;

ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DECISION TREES; DRUG DISCOVERY; NEURAL NETWORKS (COMPUTER);

EID: 84925400066     PISSN: 13596446     EISSN: 18785832     Source Type: Journal    
DOI: 10.1016/j.drudis.2014.10.012     Document Type: Review

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