Extended connectivity interaction features: improving binding affinity prediction through chemical description.

Journal: Bioinformatics (Oxford, England)
Published Date: Jun 16, 2021

Abstract

MOTIVATION: Machine-learning scoring functions (SFs) have been found to outperform standard SFs for binding affinity prediction of protein-ligand complexes. A plethora of reports focus on the implementation of increasingly complex algorithms, while the chemical description of the system has not been fully exploited.

Authors

  • Norberto Sánchez-Cruz
    DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
  • José L Medina-Franco
    DIFACQUIM research group, Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, 04510 Mexico City, Mexico.
  • Jordi Mestres
    Chemotargets S.L., Carrer de Baldiri Reixac, 4-8 TI05A7 Torre I, planta 5, A7, 08028, Barcelona, Spain.
  • Xavier Barril
    Institut de Biomedicina de la Universitat de Barcelona (IBUB) and Facultat de Farmacia, Universitat de Barcelona, 08028 Barcelona, Spain.

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