MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: The process of drug development is inherently complex, marked by extended intervals from the inception of a pharmaceutical agent to its eventual launch in the market. Additionally, each phase in this process is associated with a significant failure rate, amplifying the inherent challenges of this task. Computational virtual screening powered by machine learning algorithms has emerged as a promising approach for predicting therapeutic efficacy. However, the complex relationships between the features learned by these algorithms can be challenging to decipher.

Authors

  • Luigi Ferraro
    Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33131, United States.
  • Giovanni Scala
    Theoreo srl - Spin-off company of the University of Salerno, Via S. De Renzi, 50., Salerno, Italy.
  • Luigi Cerulo
    Biogem Scarl, Istituto di Ricerche Genetiche "Gaetano Salvatore", Ariano Irpino, Italy.
  • Emanuele Carosati
    Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, Italy.
  • Michele Ceccarelli
    Computational Biology-Genomic Research Center, ABBVIE, Redwood City, CA, USA. michele.ceccarelli@unina.it.