Title:	Improving binding affinity prediction by using a rule-based model with physical-chemical and structural descriptors of the nano-environment for protein-ligand interactions.
Authors:	BORRO, L. C. SALIM, J. A. MAZONI, I. YANO, I. JARDINE, J. G. NESHICH, G.
Affiliation:	IB/Unicamp; FEEC/Unicamp; IVAN MAZONI, CNPTIA; INACIO HENRIQUE YANO, CNPTIA; JOSÉ GILBERTO JARDINE, CNPTIA; GORAN NESHICH, CNPTIA.
Date Issued:	2015
Citation:	In: CONGRESS OF THE INTERNATIONAL UNION FOR BIOCHEMISTRY AND MOLECULAR BIOLOGY, 23.; ANNUAL MEETING OF THE BRAZILIAN SOCIETY FOR BIOCHEMISTRY AND MOLECULAR BIOLOGY, 44., 2015, Foz do Iguaçu. Biochemistry for a better world: abstracts book. [Foz do Iguaçu]: SBBq, 2015.
Pages:	p. 153.
Description:	In order to improve binding affinity prediction, we developed a new scoring function, named STINGSF, derived from physical-chemical and structural features that describe the protein-ligand interaction nano-environment of experimentally determined structures.
NAL Thesaurus:	Artificial intelligence
Keywords:	Interação proteína-ligante Aprendizado de máquina Inteligência artificial Protein-ligand interaction Scoring functions Machine learning
Notes:	C.047.
Type of Material:	Resumo em anais e proceedings
Access:	openAccess
Appears in Collections:	Resumo em anais de congresso (CNPTIA)