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 language | English (US) |
|---|---|
| Journal | Drug Discovery Today |
| DOIs | |
| State | Accepted/In press - 2017 |
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ASJC Scopus subject areas
- Pharmacology
- Drug Discovery
Cite this
From flamingo dance to (desirable) drug discovery : A nature-inspired approach. / Sánchez-Rodríguez, Aminael; Pérez-Castillo, Yunierkis; Schuerer, Stephan C; Nicolotti, Orazio; Mangiatordi, Giuseppe Felice; Borges, Fernanda; Cordeiro, M. Natalia D.S.; Tejera, Eduardo; Medina-Franco, José L.; Cruz-Monteagudo, Maykel.
In: Drug Discovery Today, 2017.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - From flamingo dance to (desirable) drug discovery
T2 - A nature-inspired approach
AU - Sánchez-Rodríguez, Aminael
AU - Pérez-Castillo, Yunierkis
AU - Schuerer, Stephan C
AU - Nicolotti, Orazio
AU - Mangiatordi, Giuseppe Felice
AU - Borges, Fernanda
AU - Cordeiro, M. Natalia D.S.
AU - Tejera, Eduardo
AU - Medina-Franco, José L.
AU - Cruz-Monteagudo, Maykel
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85021071373&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021071373&partnerID=8YFLogxK
U2 - 10.1016/j.drudis.2017.05.008
DO - 10.1016/j.drudis.2017.05.008
M3 - Article
C2 - 28624633
AN - SCOPUS:85021071373
JO - Drug Discovery Today
JF - Drug Discovery Today
SN - 1359-6446
ER -