Several medicinal plants are being used in Indian medicine systems since ancient times. However, in most cases, the specific molecules or the active ingredients responsible for the medicinal or therapeutic properties are not yet known. The objective of this study was to develop 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. 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 for building 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. A supra network of molecules and scaffolds identifying the relationships between the plant molecules and drugs was developed. Cluster analysis of virtual library molecules showed that the novel molecules had more pharmacophoric properties than toxicophoric and chemophoric properties. These predicted molecules need to be subjected to biological screening to identify potential molecules for drug discovery research. We also developed a Java-based open-source toolkit-cum-database called DoMINE (Database of Medicinally Important Natural products from plantaE) to advance the natural product-based drug discovery through chemoinformatics approaches. This study will be useful in developing new drug molecules from the known medicinal plant molecules. We hope that this work will encourage experimental organic chemists to synthesize these molecules based on the predicted values.

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