| Autor: |
Ruano-Ordás D; Department of Computer Science, University of Vigo, ESEI - Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004, Ourense, Spain.; CINBIO - Biomedical Research Centre, University of Vigo, Campus Universitario Lagoas-Marcosende, 36310, Vigo, Spain.; Multicriteria Optimization and Decision Analysis (MODA) Research Group, LIACS, Leiden University, Niels Bohrweg 1, 2333-CA, Leiden, The Netherlands.; SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain.; School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester, LE1 9BH, UK., Burggraaff L; Drug Discovery and Safety, LACDR, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands., Liu R; Drug Discovery and Safety, LACDR, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands., van der Horst C; Drug Discovery and Safety, LACDR, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands., Heitman LH; Drug Discovery and Safety, LACDR, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands., Emmerich MTM; Drug Discovery and Safety, LACDR, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands., Mendez JR; Department of Computer Science, University of Vigo, ESEI - Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004, Ourense, Spain.; CINBIO - Biomedical Research Centre, University of Vigo, Campus Universitario Lagoas-Marcosende, 36310, Vigo, Spain.; SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain., Yevseyeva I; School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester, LE1 9BH, UK., van Westen GJP; Drug Discovery and Safety, LACDR, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands. gerard@lacdr.leidenuniv.nl. |
| Abstrakt: |
Drugs have become an essential part of our lives due to their ability to improve people's health and quality of life. However, for many diseases, approved drugs are not yet available or existing drugs have undesirable side effects, making the pharmaceutical industry strive to discover new drugs and active compounds. The development of drugs is an expensive process, which typically starts with the detection of candidate molecules (screening) after a protein target has been identified. To this end, the use of high-performance screening techniques has become a critical issue in order to palliate the high costs. Therefore, the popularity of computer-based screening (often called virtual screening or in silico screening) has rapidly increased during the last decade. A wide variety of Machine Learning (ML) techniques has been used in conjunction with chemical structure and physicochemical properties for screening purposes including (i) simple classifiers, (ii) ensemble methods, and more recently (iii) Multiple Classifier Systems (MCS). Here, we apply an MCS for virtual screening (D2-MCS) using circular fingerprints. We applied our technique to a dataset of cannabinoid CB2 ligands obtained from the ChEMBL database. The HTS collection of Enamine (1,834,362 compounds), was virtually screened to identify 48,232 potential active molecules using D2-MCS. Identified molecules were ranked to select 21 promising novel compounds for in vitro evaluation. Experimental validation confirmed six highly active hits (> 50% displacement at 10 µM and subsequent Ki determination) and an additional five medium active hits (> 25% displacement at 10 µM). Hence, D2-MCS provided a hit rate of 29% for highly active compounds and an overall hit rate of 52%. |
