Development of machine learning models of β-cyclodextrin and sulfobutylether-β-cyclodextrin complexation free energies

International Journal of Pharmaceutics

Volumn 418, Issue 2, 2011, Pages 207-216

  Merzlikine, Alexei ^a   Abramov, Yuriy A ^a   Kowsz, Stacy J ^a,b,d   Thomas, V Hayden ^a   Mano, Takashi ^c  

^a PFIZER INC   (United States)

^b ROCHESTER INSTITUTE OF TECHNOLOGY   (United States)

^c PFIZER GLOBAL RESEARCH AND DEVELOPMENT   (United Kingdom)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Cyclodextrin; Complexation constant; Cubist; Machine learning methods; Random Forest; Sulfobutylether cyclodextrin

Indexed keywords

1 NAPHTHYLAMINE; 1,2,3 TRICHLOROBENZENE; 2 NAPHTHYLAMINE; 2 PHENYLQUINOLINE DERIVATIVE; 3,5 DICHLOROPHENOL; ACETAZOLAMIDE; ACRIDINE; ADENINE; ADENOSINE; ATROPINE; AZATHIOPRINE; BENDROFLUMETHIAZIDE; BENZAMIDE; BENZOCAINE; BENZOIC ACID; BETA CYCLODEXTRIN SULFOBUTYL ETHER; BETAMETHASONE; BIPHENYL; BUTYL PARABEN; BUTYLCAINE; CAFFEINE; CHLORAMPHENICOL; CHLORTALIDONE; CHLORZOXAZONE; FLUCYTOSINE; FLUOROURACIL; MESALAZINE; SULPIRIDE; UNINDEXED DRUG;

ARTICLE; COMPLEX FORMATION; CUBIST METHOD; ENERGY; MACHINE LEARNING; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE PROPERTY RELATION; RANDOM FOREST METHOD; SOLUBILIZATION;

ARTIFICIAL INTELLIGENCE; BETA-CYCLODEXTRINS; COMPUTER SIMULATION; DATABASES, FACTUAL; DRUG DISCOVERY; ENTROPY; EXCIPIENTS; MOLECULAR STRUCTURE; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SOLUBILITY; THERMODYNAMICS;

EID: 80052964320     PISSN: 03785173     EISSN: 18733476     Source Type: Journal    
DOI: 10.1016/j.ijpharm.2011.03.065     Document Type: Article

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