Development of Cancer Drug Targets and Potential Anti-Cancer Agents using Deep Learning and Screening Processes

Cancer is one of the global health issues with various complications that lead to fatal. A novel approach is initiated using deep learning techniques. They help in the analysis of complex biological structures which helps to provide potential anti-cancer drugs. They help in the screening of emergent compounds through therapeutic potential. The first stage in the proposed system involves the development of comprehensive datasets that include multi-omics data and protein-protein interaction network structures. They proceeded with preprocessing and feature extraction that led to the contribution of cancer progression stages. The deep learning model involves learning from cancer-associated genes and pathways. They help in the prediction of protein targets that are decisive for cancer development. In the subsequent stage, the deep learning model helps in employing a screen for the collection of emerging compounds from diverse platforms ranging from synthetic libraries. Another stage involves the validation process that leads to in vivo and in vitro experiments. They are proceeded with cellular assays. Compound efficacy is obtained through xenograft and mouse models followed by molecular biology techniques such as protein profiling and gene analysis. Thus the system provides a cumulative approach in obtaining anti-cancer drug targets.