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Cervical cancer stands as a prevalent gynaecologic malignancy affecting women globally, often linked to persistent human papillomavirus infection. Biomarkers associated with cervical cancer, including VEGF-A, VEGF-B, VEGF-C, VEGF-D, and VEGF-E, show upregulation and are linked to angiogenesis and lymphangiogenesis. This research aims to employ in-silico methods to target tyrosine kinase receptor proteins-VEGFR-1, VEGFR-2, and VEGFR-3, and identify novel inhibitors for Vascular Endothelial Growth Factors receptors (VEGFRs). A comprehensive literary study was conducted which identified 26 established inhibitors for VEGFR-1, VEGFR-2, and VEGFR-3 receptor proteins. Compounds with high-affinity scores, including PubChem ID-25102847, 369976, and 208908 were chosen from pre-existing compounds for creating Deep Learning-based models. RD-Kit, a Deep learning algorithm, was used to generate 43 million compounds for VEGFR-1, VEGFR-2, and VEGFR-3 targets. Molecular docking studies were conducted on the top 10 molecules for each target to validate the receptor-ligand binding affinity. The results of Molecular Docking indicated that PubChem IDs-71465,645 and 11152946 exhibited strong affinity, designating them as the most efficient molecules. To further investigate their potential, a Molecular Dynamics Simulation was performed to assess conformational stability, and a pharmacophore analysis was also conducted for indoctrinating interactions., (© 2024. The Author(s).)

India being an agrarian economy, rainfall is an essential component that directly or indirectly influences agricultural produce. With the increasing impacts of the changing climate scenario, it is anticipated that in the near future, frequent and extreme rainfall episodes will trigger events like severe floods, landslides, etc. Therefore, it is extremely important to make precise predictions so that the intensity of the impacts on life and property can be reduced. In recent times, with the advancement of AI/ML applications, it has become popular in weather and climate sciences. The current work uses 121 years of rainfall data for analysis and prediction purposes, where deep learning (DL) approaches like LSTM (Long Short Term Memory), BiLSTM (Bi-directional LSTM) and GRU (Gated Recurrent Unit) have been adopted. The long-term rainfall analysis and prediction over selected smart cities of India is based on their location in the homogenous monsoon regions. The results obtained from the models indicated that for univariate forecasting, the overall performance of BiLSTM is better than others for most cities considered, while GRU predicted well for places with higher rainfall variability. In the multivariate approach, LSTM's performance is superior. Therefore, a combination of BiLSTM and GRU might offer a better result in the univariate approach, or an advanced version of LSTM might enrich the outcomes in the multivariate approach for rainfall analysis and forecasting. [ABSTRACT FROM AUTHOR]