Karade Divya, Vijayasarathi Durairaj, Kadoo Narendra, Vyas Renu, Ingle P K, Karthikeyan Muthukumarasamy
Chemical Engineering and Process Development (CEPD) Division, CSIR-National Chemical Laboratory, Pune - 411008, India.
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
Comb Chem High Throughput Screen. 2020;23(10):1113-1131. doi: 10.2174/1386207323666200606211342.
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.
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.
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.
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.
自古以来，印度医学体系中就使用了多种药用植物。然而，在大多数情况下，负责药用或治疗特性的特定分子或活性成分尚不清楚。
本研究旨在报告一种计算方案以及一种工具，用于通过化学信息学方法从印度药用和芳香植物的生物活性分子中生成新型潜在药物候选物。
我们建立了一个印度药用和芳香植物数据库，并附上相关信息（植物科、用于药用的植物部位、结构信息、治疗特性等）。我们还开发了一个基于Java的化学信息学开源工具，称为DoMINE（来自植物的药用重要天然产物数据库），用于生成虚拟库并从已知药用植物分子中筛选新型分子。我们采用化学信息学方法对104种印度药用和芳香植物的代谢产物进行了虚拟筛选，并设计了新型类药物生物活性分子。为此，通过对与药用植物物种相关的文献进行文本挖掘，确定了1665个含环分子，随后用于提取209个分子支架。不同的支架进一步用于构建一个聚焦虚拟库。在药物发现的背景下，通过聚类分析进行虚拟筛选，以预测这些植物分子中的类药物和类先导分子。使用化学信息学方法和统计参数对预测的类药物和类先导分子进行评估，仅将最显著的分子作为开发新药的候选分子提出。
分子和支架的超网络确定了植物分子与药物之间的关系。虚拟库分子的聚类分析表明，新型分子具有更多的药效团特性，而不是毒效团和化学团特性。我们还开发了DoMINE工具包，以通过化学信息学方法推进基于天然产物的药物发现。这项研究将有助于从已知药用植物分子中开发新的药物分子。因此，这项工作将鼓励实验有机化学家根据预测值合成这些分子。这些合成分子需要进行生物筛选，以鉴定用于药物发现研究的潜在分子。
