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Titolo:
Quantum neural networks can predict binding free energies for enzymatic inhibitors
Autore:
Braunheim, BB; Bagdassarian, CK; Schramm, VL; Schwartz, SD;
Indirizzi:
Yeshiva Univ Albert Einstein Coll Med, Dept Biochem, Bronx, NY 10461 USA Yeshiva Univ Albert Einstein Coll Med Bronx NY USA 10461 nx, NY 10461 USA Yeshiva Univ Albert Einstein Coll Med, Dept Physiol, Bronx, NY 10461 USA Yeshiva Univ Albert Einstein Coll Med Bronx NY USA 10461 nx, NY 10461 USA Yeshiva Univ Albert Einstein Coll Med, Dept Biophys, Bronx, NY 10461 USA Yeshiva Univ Albert Einstein Coll Med Bronx NY USA 10461 nx, NY 10461 USA
Titolo Testata:
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
fascicolo: 3, volume: 78, anno: 2000,
pagine: 195 - 204
SICI:
0020-7608(20000605)78:3<195:QNNCPB>2.0.ZU;2-C
Fonte:
ISI
Lingua:
ENG
Soggetto:
TRANSITION-STATE INHIBITOR; CYTIDINE DEAMINASE; AMP NUCLEOSIDASE; DESIGN; SITE;
Keywords:
inhibitor; binding energy; neural network;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Citazioni:
25
Recensione:
Indirizzi per estratti:
Indirizzo: Schramm, VL Yeshiva Univ Albert Einstein Coll Med, Dept Biochem, 1300 Morris Pk Ave, Bronx, NY 10461 USA Yeshiva Univ Albert Einstein Coll Med 1300 Morris Pk Ave Bronx NY USA 10461
Citazione:
B.B. Braunheim et al., "Quantum neural networks can predict binding free energies for enzymatic inhibitors", INT J QUANT, 78(3), 2000, pp. 195-204

Abstract

Quantum mechanical molecular electrostatic potential surfaces and neural networks are combined to predict the binding energy for bioactive molecules with enzyme targets. Computational neural networks are employed to identifythe quantum mechanical features of inhibitory molecules that contribute tobinding. This approach generates relationships between the quantum mechanical structure of inhibitory molecules and the strength of binding. Feed-forward neural networks with back-propagation of error are trained to recognize the quantum mechanical electrostatic potential at the entire van der Waals surface of a group of training molecules and to predict the strength of interactions between the enzyme and novel inhibitors. Three enzyme systems are used as examples in this work: AMP (adenosine mono phosphate) nucleosidase, adenosine deaminase, and cytidine deaminase. Quantum neural networks identify critical areas on inhibitor potential surfaces involved in binding and predict with quantitative accuracy the binding strength of new inhibitors. The method is able to predict the binding free energy of the transition state, when trained with less tightly bound inhibitors. The application of this approach to the study of enzyme inhibitors and receptor agonists wouldpermit evaluation of chemical libraries of potential bioactive agents. (C)2000 John Wiley & Sons, Inc.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 18/09/20 alle ore 16:54:31