The creation of a comprehensive databank on bioactivities of compounds in Indian medicinal plants involves the systematic collection, curation, and organization of data from various sources. This databank aims to consolidate information on the chemical constituents of medicinal plants, their bioactive properties, and their potential applications in the treatment of various diseases.
The compilation of this information in a structured and accessible format offers several advantages. Researchers, scientists, and pharmaceutical companies can use the databank as a valuable resource to identify potential leads for drug discovery and development. It facilitates the exploration of bioactive compounds that may exhibit specific therapeutic effects, thereby accelerating the process of finding new drug candidates from natural sources.
The data provided showcases a diverse array of organic compound Super Classes predicted by Classifire along with their respective counts. From smaller categories like "Acetylides" and "Organic 1,3-dipolar compounds" to broader classifications such as "Hydrocarbons" and "Organic oxygen compounds," each category represents a distinct subset of organic chemistry. Particularly noteworthy is the high count of compounds in categories like "Organoheterocyclic compounds" and "Phenylpropanoids and polyketides," indicating their significant presence in organic compound databases. Moreover, the substantial count of "Lipids and lipid-like molecules" highlights the importance of these compounds in biological and chemical systems.
The provided data presents a comprehensive list of compound classes predicted by Classifire along with their respective counts. The list encompasses a wide range of compounds, from specific alkaloids like "Hasubanan alkaloids" and "Gelsemium alkaloids" to broader categories such as "Organic oxides" and "Hydroxy acids and derivatives." Interestingly, the count of compounds varies significantly across different classes, with some classes represented by only a few compounds, while others, like "Indoles and derivatives" and "Coumarins and derivatives," have substantially higher counts. This data underscores the diversity and complexity of organic chemistry and highlights the prevalence of certain compound types over others in this dataset.
The data provided includes counts for compounds adhering to various drug-likeness rules, such as Lipinski, Ghose, Veber, Egan, and Muegge. These rules are often used in drug discovery to assess the likelihood of a compound's success as a drug candidate. Among the compounds evaluated, Lipinski's rule appears to be the most prevalent, with 54,602 compounds meeting its criteria, followed by Veber's rule with 67,424 compounds. In total, 73,586 compounds adhere to at least one of these rules out of a total of 102,750 compounds evaluated.
The data provided represents the distribution of compounds based on their molecular weight (MW). A significant portion, 71,792 compounds, falls within the range of 0-500 MW, indicating a prevalence of smaller molecules. Additionally, 27,022 compounds have MW between 500 and 1000, while a smaller subset of 3,941 compounds has a MW exceeding 1000. This breakdown suggests a diverse range of molecular sizes among the evaluated compounds, with the majority falling within the lower MW range.