01388nam a2200277 a 450000100080000000500110000800800410001910000170006024502000007726002740027730000120055150000110056352002630057465000280083765300280086565300290089365300340092265300210095665300310097765300220100870000170103070000150104770000130106270000190107570000160109420322602020-01-21       2015    bl uuuu     u00u1 u    #d1 aBORRO, L. C.  aImproving binding affinity prediction by using a rule-based model with physical-chemical and structural descriptors of the nano-environment for protein-ligand interactions.h[electronic resource]  aIn: 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]: SBBqc2015  ap. 153.  aC.047.  aIn 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.  aArtificial intelligence  aAprendizado de máquina  aInteligência artificial  aInteração proteína-ligante  aMachine learning  aProtein-ligand interaction  aScoring functions1 aSALIM, J. A.1 aMAZONI, I.1 aYANO, I.1 aJARDINE, J. G.1 aNESHICH, G.