RESUMEN
INTRODUCTION: Despite cancer treatment strides, mortality due to ovarian cancer remains high globally. While immunotherapy has proven effective in treating cancers with low cure rates, it has limitations. Growing evidence suggests that both tumoral and non-tumoral components of the tumor immune microenvironment (TIME) play a significant role in cancer growth. Therefore, developing novel and focused therapy for ovarian cancer is critical. Studies indicate that TIME is involved in developing ovarian cancer, particularly genome-, transcriptome-, and proteome-wide studies. As a result, TIME may present a prospective therapeutic target for ovarian cancer patients. AREAS COVERED: We examined several TIME-targeting medicines and the connection between TIME and ovarian cancer. The key protagonists and events in the TIME and therapeutic strategies that explicitly target these events in ovarian cancer are discussed. EXPERT OPINION: We highlighted various targeted therapies against TIME in ovarian cancer, including anti-angiogenesis therapies and immune checkpoint inhibitors. While these therapies are in their infancy, they have shown promise in controlling ovarian cancer progression. The use of 'omics' technology is helping in better understanding of TIME in ovarian cancer and potentially identifying new therapeutic targets. TIME-targeted strategies could account for an additional treatment strategy when treating ovarian cancer.
RESUMEN
In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.
