AntiViral Phyto Chemical Database (AVPCD)

Anti Viral Phyto Chemical Database (AVPCD) aims to bring all antiviral phytochemical and diseases together from published work specially related with Covid-19, Cancer, HIV and malaria. These diseases are becoming the world's most critical public health issues. The lack of effective medications and vaccines against this viral infection induced a challenge for scientists to identify viable therapies in the early stages. Many viral diseases caused thousands of deaths per year worldwide. According to the statistics from World Health Organization (WHO), the numbers of the Latest updated deaths by Covid-19 and malaria are 5020204, and 409000 respectively, while cancer is responsible for 10 million deaths worldwide. To date many valuable computational resources about medicinal plants are constructed based on their different medicinal properties. As an important source, these plant-derived medicines could be used to identify lifesaving drugs, and save much costs in terms of time, money and labours. Therefore, we tried to provide a user-friendly platform for global researchers, drug developers, health practitioners, and students with very frequent updates to add more information for their study and research. AVPCD is a compressive collection of 2537 antiviral phytochemicals from 384 medicinal compounds, 319 multiple families and included several parameters like “their scientific”, “family”, and “common names”, as well as “their utilized parts”, “disease information”, “active compounds”, and ”PubMed IDs” or links of the relevant articles. More, each compound, in AVPCD was annotated with its 2D and 3D structure, Molecular formula, Molecular weight, Isomeric SMILES, InChi, Inchi key, IUPIC Name, and 21 other properties. A scoring method was also designed to measure the confidence of each phytochemical in the viral disease. We have used different keywords such as “Medicinal plant”, “phytochemical”, “plant-derived compound”, “Covid related drugs plant”, “cancer related medicinal plant”, “malaria” and “medicinal plant” and so on, in several search-engines such as Google, Google Scholar, Research Gate, PubMed, Science Direct, Bing, etc. In conclusion, we provided a comprehensive online database which is built by HTML, PHP, JavaScript, and CSS.