Unified multi-target approach for the rational in silico design of anti-bladder cancer agents

Anti-Cancer Agents in Medicinal Chemistry

Volumn 13, Issue 5, 2013, Pages 791-800

  Speck Planche, Alejandro ^a   Kleandrova, Valeria V ^a,b   Luan, Feng ^a,c   Cordeiro, M N D S ^a  

^a UNIVERSITY OF PORTO   (Portugal)

^b MOSCOW STATE UNIVERSITY OF FOOD PRODUCTION   (Russian Federation)

^c YANTAI UNIVERSITY   (China)

Author keywords

Artificial neural networks; Bladder cancer; Fragments; In silico design; Linear discriminant analysis; Molecular descriptors; Mt QSAR; Quantitative contributions

Indexed keywords

ANTINEOPLASTIC AGENT; BLCI 1; BLCI 2; BLCI 3; BLCI 4; BLCI 5; BLCI 6; BLCI 7; BLCI 8; UNCLASSIFIED DRUG;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL ACTIVITY; BLADDER CANCER; CHEMICAL DATABASE; CHEMICAL STRUCTURE; COMPUTER AIDED DESIGN; DISCRIMINANT ANALYSIS; HUMAN; HUMAN CELL; HYDROPHOBICITY; PHYSICAL CHEMISTRY; PREDICTION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RECEIVER OPERATING CHARACTERISTIC; TRAINING;

ANTINEOPLASTIC AGENTS; CELL LINE, TUMOR; COMPUTER SIMULATION; DRUG DELIVERY SYSTEMS; DRUG DESIGN; HUMANS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; URINARY BLADDER NEOPLASMS;

EID: 84880168945     PISSN: 18715206     EISSN: 18755992     Source Type: Journal    
DOI: 10.2174/1871520611313050013     Document Type: Article

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