TY - JOUR
T1 - From flamingo dance to (desirable) drug discovery
T2 - a nature-inspired approach
AU - Sánchez-Rodríguez, Aminael
AU - Pérez-Castillo, Yunierkis
AU - Schürer, 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
N1 - Funding Information:
This project was supported by Foundation for Science and Technology (FCT) , FEDER/COMPETE (Grants UID/QUI/00081/2013 , POCI-01-0145-FEDER-006980 , and NORTE-01-0145-FEDER-000028 ), and the NCI through the NIH Common Fund. F.B. also thanks the COST action CA15135 (Multi-Target Paradigm for Innovative Ligand Identification in the Drug Discovery Process, MuTaLig) for support. M.C-M. (Grant SFRH/BPD/90673/2012) was also supported by FCT and FEDER/COMPETE funds. S.S. acknowledges support from grants U54CA189205 ( Illuminating the Druggable Genome Knowledge Management Center, IDG-KMC ) and U54HL127624 ( Data Coordination and Integration Center for BD2K-LINCS, BD2K-LINCS DCIC ). J.L.M.-F. acknowledges support from Universidad Nacional Autónoma de México (UNAM) through grant Programa de Apoyo a la Investigación y el Posgrado (PAIP) 5000-9163 .
PY - 2017/10
Y1 - 2017/10
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.
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U2 - 10.1016/j.drudis.2017.05.008
DO - 10.1016/j.drudis.2017.05.008
M3 - Review article
C2 - 28624633
AN - SCOPUS:85021071373
VL - 22
SP - 1489
EP - 1502
JO - Drug Discovery Today
JF - Drug Discovery Today
SN - 1359-6446
IS - 10
ER -