Osteosarcoma is a malignant osseous neoplasm. Osteosarcoma is a primary bone malignancy capable of producing osteoid tissue or immature bones. A subsequent malignant degeneration of the primary bone pathology occurs less frequently in adults. The over-expression of several proteins, including Heat shock proteins, Cofilin, Annexins, Insulin-like growth factor, transforming growth factor-β, Receptor tyrosine kinase, Ezrin, Runx2, SATB2, ATF4, Annexins, cofilin, EGFR, VEGF, retinoblastoma 1 (Rb1) and secreted protein, has been associated to the development and progression of osteosarcoma. These proteins are involved in cell adhesion, migration, invasion, and the control of cell cycle and apoptosis. In genomic studies, osteosarcoma has been associated with several genetic abnormalities, including chromosomal rearrangements, gene mutations, and gene amplifications. These differentially expressed proteins could be used as early identification biomarkers or treatment targets. Proteomics and genomics play significant parts in enhancing our molecular understanding of osteosarcoma, and their integration provides essential insights into this aggressive bone cancer. This review will discuss the tumour biology that has assisted in helping us better understand the causes of osteosarcoma and how they could potentially be used to find new treatment targets and enhance the survival rate for osteosarcoma patients.
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.