Improving binding affinity prediction by using a rule-based model with physical-chemical and structural descriptors of the nano-environment for protein-ligand interactions.

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Author(s): BORRO, L. C.; SALIM, J. A.; MAZONI, I.; YANO, I.; JARDINE, J. G.; NESHICH, G.

Summary: 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.

Publication year: 2015

Types of publication: Abstract in annals or event proceedings

Unit: Embrapa Digital Agriculture

Keywords: Aprendizado de máquina, Artificial intelligence, Inteligência artificial, Interação proteína-ligante, Machine learning, Protein-ligand interaction, Scoring functions

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