DrugMint: a webserver for predicting and designing of drug-like molecules
Open Access
- 5 November 2013
- journal article
- research article
- Published by Springer Science and Business Media LLC in Biology Direct
- Vol. 8 (1), 28
- https://doi.org/10.1186/1745-6150-8-28
Abstract
Background: Identification of drug-like molecules is one of the major challenges in the field of drug discovery. Existing approach like Lipinski rule of 5 (Ro5), Operea have their own limitations. Thus, there is a need to develop computational method that can predict drug-likeness of a molecule with precision. In addition, there is a need to develop algorithm for screening chemical library for their drug-like properties. Results: In this study, we have used 1347 approved and 3206 experimental drugs for developing a knowledge-based computational model for predicting drug-likeness of a molecule. We have used freely available PaDEL software for computing molecular fingerprints/descriptors of the molecules for developing prediction models. Weka software has been used for feature selection in order to identify the best fingerprints. We have developed various classification models using different types of fingerprints like Estate, PubChem, Extended, FingerPrinter, MACCS keys, GraphsOnlyFP, SubstructureFP, Substructure FPCount, Klekota-RothFP, Klekota-Roth FPCount. It was observed that the models developed using MACCS keys based fingerprints, discriminated approved and experimental drugs with higher precision. Our model based on one hundred fifty nine MACCS keys predicted drug-likeness of the molecules with 89.96% accuracy along with 0.77 MCC. Our analysis indicated that MACCS keys (ISIS keys) 112, 122, 144, and 150 were highly prevalent in the approved drugs. The screening of ZINC (drug-like) and ChEMBL databases showed that around 78.33% and 72.43% of the compounds present in these databases had drug-like potential. Conclusion: It was apparent from above study that the binary fingerprints could be used to discriminate approved and experimental drugs with high accuracy. In order to facilitate researchers working in the field of drug discovery, we have developed a webserver for predicting, designing, and screening novel drug-like molecules (http://crdd.osdd.net/oscadd/drugmint/). Reviewers: This article was reviewed by Robert Murphy, Difei Wang (nominated by Yuriy Gusev), and Ahmet Bakan (nominated by James Faeder).Keywords
This publication has 27 references indexed in Scilit:
- A physicochemical descriptor-based scoring scheme for effective and rapid filtering of kinase-like chemical spaceJournal of Cheminformatics, 2012
- The impact of natural products upon modern drug discoveryCurrent Opinion in Chemical Biology, 2008
- Drug-therapy networks and the prediction of novel drug targetsJournal of Biology, 2008
- Drug discovery beyond the ‘rule-of-five’Current Opinion in Biotechnology, 2007
- ADMET in silico modelling: towards prediction paradise?Nature Reviews Drug Discovery, 2003
- Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settingsAdvanced Drug Delivery Reviews, 2001
- Pharmacokinetics and metabolism in early drug discoveryCurrent Opinion in Chemical Biology, 1999
- Future pathways for combinatorial chemistry.Molecular Diversity, 1997
- Discovery of enzyme inhibitors through combinatorial chemistryMolecular Diversity, 1997
- Libraries of non-polymeric organic moleculesCurrent Opinion in Biotechnology, 1995