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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]

• AI methods Accelerated Drug Screening and Design with structure and Ligand-based methods. • AI Learning models for categorizing data-driven drug discovery. • Applications and Limitations of AI models for drug discovery in pharmaceutical industries. • Need for balance between AI-driven predictions and experimental validations. Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design. [ABSTRACT FROM AUTHOR]