From flamingo dance to (desirable) drug discovery: a nature-inspired approach

Aminael Sánchez-Rodríguez, Yunierkis Pérez-Castillo, Stephan C. Schürer, Orazio Nicolotti, Giuseppe Felice Mangiatordi, Fernanda Borges, M. Natalia D.S. Cordeiro, Eduardo Tejera, José L. Medina-Franco, Maykel Cruz-Monteagudo

Research output: Contribution to journalReview articlepeer-review

14 Scopus citations

Abstract

The therapeutic effects of drugs are well known to result from their interaction with multiple intracellular targets. Accordingly, the pharma industry is currently moving from a reductionist approach based on a ‘one-target fixation’ to a holistic multitarget approach. However, many drug discovery practices are still procedural abstractions resulting from the attempt to understand and address the action of biologically active compounds while preventing adverse effects. Here, we discuss how drug discovery can benefit from the principles of evolutionary biology and report two real-life case studies. We do so by focusing on the desirability principle, and its many features and applications, such as machine learning-based multicriteria virtual screening. Here, we describe a multicriteria virtual screening approach based on desirability functions and tailored ensemble machine-learning classifiers.

Original languageEnglish (US)
Pages (from-to)1489-1502
Number of pages14
JournalDrug Discovery Today
Volume22
Issue number10
DOIs
StatePublished - Oct 2017

ASJC Scopus subject areas

  • Pharmacology
  • Drug Discovery

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