Artificial intelligence to speed up active compounds screening

Identifying chemical compounds that have the potential to be drug candidates is one of the most important steps in drug discovery. The process of finding chemical compounds that interact favorably with biological targets that impact a particular disease is complex, slow, uncertain, and requires a high level of expertise and financial resources. In this context, artificial intelligence (AI) algorithms and machine learning (ML) are currently reshaping the drug discovery landscape by increasing the efficiency of the different tasks. This includes the early stages of the process, which have been accelerated by, for example, virtual HTS, which solves the problem of the long time needed to discover new drugs and the lower clinical failure rate. The success of most current state-of-the-art methods can be attributed to recent developments in DL. Given the new technological developments, the use of AI and ML in biomedical research will bring the treatment and diagnosis of brain tumors, including glioblastoma to an advanced level. In this chapter, we provide an overview of current ML technologies that are helping to accelerate anticancer drug screening, highlighting both the opportunities and challenges, and examples where AI has made a significant impact.