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Combinatorial Chemistry & High Throughput Screening


ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

General Research Article

Design of Novel Drug-like Molecules Using Informatics Rich Secondary Metabolites Analysis of Indian Medicinal and Aromatic Plants

Author(s): Divya Karade, Durairaj Vijayasarathi, Narendra Kadoo, Renu Vyas, P.K. Ingle and Muthukumarasamy Karthikeyan*

Volume 23 , Issue 10 , 2020

Page: [1113 - 1131] Pages: 19

DOI: 10.2174/1386207323666200606211342

Price: $65


Background: Several medicinal plants are being used in Indian medicine systems from ancient times. However, in most cases, the specific molecules or the active ingredients responsible for the medicinal or therapeutic properties are not yet known.

Objective: This study aimed to report a computational protocol as well as a tool for generating novel potential drug candidates from the bioactive molecules of Indian medicinal and aromatic plants through the chemoinformatics approach.

Methods: We built a database of the Indian medicinal and aromatic plants coupled with associated information (plant families, plant parts used for the medicinal purpose, structural information, therapeutic properties, etc.) We also developed a Java-based chemoinformatics open-source tool called DoMINE (Database of Medicinally Important Natural products from plantaE) for the generation of virtual library and screening of novel molecules from known medicinal plant molecules. We employed chemoinformatics approaches to in-silico screened metabolites from 104 Indian medicinal and aromatic plants and designed novel drug-like bioactive molecules. For this purpose, 1665 ring containing molecules were identified by text mining of literature related to the medicinal plant species, which were later used to extract 209 molecular scaffolds. Different scaffolds were further used to build a focused virtual library. Virtual screening was performed with cluster analysis to predict drug-like and lead-like molecules from these plant molecules in the context of drug discovery. The predicted drug-like and lead-like molecules were evaluated using chemoinformatics approaches and statistical parameters, and only the most significant molecules were proposed as the candidate molecules to develop new drugs.

Results and Conclusion: The supra network of molecules and scaffolds identifies the relationship between the plant molecules and drugs. Cluster analysis of virtual library molecules showed that novel molecules had more pharmacophoric properties than toxicophoric and chemophoric properties. We also developed the DoMINE toolkit for the advancement of natural product-based drug discovery through chemoinformatics approaches. This study will be useful in developing new drug molecules from the known medicinal plant molecules. Hence, this work will encourage experimental organic chemists to synthesize these molecules based on the predicted values. These synthesized molecules need to be subjected to biological screening to identify potential molecules for drug discovery research.

Keywords: Medicinal plants, metabolites, text mining, drugs, scaffolds, virtual libraries, virtual screening.

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