QSAR study on estrogenic activity of structurally diverse compounds using generalized regression neural network

Science in China, Series B: Chemistry

Volumn 51, Issue 7, 2008, Pages 677-683

  Ji, Li ^a   Wang, Xiaodong ^a   Luo, Si ^a   Qin, Liang ^a   Yang, Xvshu ^a   Liu, Shushen ^b   Wang, Liansheng ^a  

^a NANJING UNIVERSITY   (China)

^b Tongji University   (China)

Author keywords

Endocrine disruptors; Estrogen receptor; Generalized regression neural network; Quantitative structure activity relationship

Indexed keywords

ARCHITECTURAL DESIGN; CHEMOTHERAPY; COMPUTER NETWORKS; DRUG DELIVERY; DRUG DOSAGE; DRUG THERAPY; ELECTRIC RESISTANCE; ERBIUM; FOOD PROCESSING; IMAGE CLASSIFICATION; METROPOLITAN AREA NETWORKS; MIXTURES; MODEL STRUCTURES; MOLECULAR GRAPHICS; NETWORK PROTOCOLS; ORGANIC COMPOUNDS; PHOTORESISTS; SULFUR COMPOUNDS; TELLURIUM COMPOUNDS;

DESCRIPTORS; ENDOCRINE DISRUPTORS; ESTROGEN RECEPTOR (ER); ESTROGENIC ACTIVITIES; FEATURE SELECTION (FS); GENERALIZED REGRESSION NEURAL NETWORK (GRMM); NETWORK TECHNIQUES; POW ERFUL TOOL; PREDICTIVE POWER; QSAR MODELING; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP (QSAS); RAPID SCREENING; SPRINGER (CO); STRUCTURAL REQUIREMENTS; TRADITIONAL TECHNIQUES; TRAINING SETS;

NEURAL NETWORKS;

EID: 45749155619     PISSN: 10069291     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11426-008-0070-z     Document Type: Article

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