DrugMint: a webserver for predicting and designing of drug-like molecules

Biology Direct

Gajendra PS Raghava¹  Alok K Mondal³  Deepak Singla²  Sandeep Kumar Dhanda³ 

[1]Bioinformatics Centre Institute of Microbial Technology, Sector 39A, Chandigarh, India

[2]Centre For Microbial Biotechnology, Panjab University, Chandigarh, India

[3]Institute of Microbial Technology, Chandigarh, India

关键词: Lipinski;    SVM;    DrugBank;    Fingerprints;    Substructure;    FDA;    Drug-likeness;   

Others  :  793188
DOI  :  10.1186/1745-6150-8-28

 received in 2013-06-25, accepted in 2013-10-24,  发布年份 2013

【 摘 要 】

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/ webcite).

Reviewers

This article was reviewed by Robert Murphy, Difei Wang (nominated by Yuriy Gusev), and Ahmet Bakan (nominated by James Faeder).

【 授权许可】

   
2013 Dhanda et al.; licensee BioMed Central Ltd.

【 图 表 】

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