QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest

Biology Direct

Volumn 10, Issue 1, 2015, Pages

  Singh, Harinder ^a   Singh, Sandeep ^a   Singla, Deepak ^a   Agarwal, Subhash M ^b   Raghava, Gajendra P ^a  

^a INSTITUTE OF MICROBIAL TECHNOLOGY   (India)

^b INSTITUTE OF CYTOLOGY AND PREVENTIVE ONCOLOGY   (India)

Author keywords

Active functional groups; Active substructure; Classification of EGFR inhibitors and non inhibitors; EGFR inhibitors; PubChem fingerprint; QSAR; Random forest

Indexed keywords

EGFR PROTEIN, HUMAN; EPIDERMAL GROWTH FACTOR RECEPTOR; INDOLE DERIVATIVE; PYRIMIDINE DERIVATIVE; QUINAZOLINE DERIVATIVE; QUINOLINE DERIVATIVE;

ALGORITHM; ANTAGONISTS AND INHIBITORS; BIOLOGY; CHEMISTRY; DRUG DESIGN; FACTUAL DATABASE; HUMAN; IC50; PROCEDURES; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REPRODUCIBILITY; SOFTWARE; THEORETICAL MODEL; VALIDATION STUDY;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; DRUG DESIGN; HUMANS; INDOLES; INHIBITORY CONCENTRATION 50; MODELS, THEORETICAL; PYRIMIDINES; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; QUINAZOLINES; QUINOLINES; RECEPTOR, EPIDERMAL GROWTH FACTOR; REPRODUCIBILITY OF RESULTS; SOFTWARE;

EID: 84925295087     PISSN: None     EISSN: 17456150     Source Type: Journal    
DOI: 10.1186/s13062-015-0046-9     Document Type: Article

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