In recent years, the use of artificial intelligence (AI) has been on the rise in many sectors, including the pharmaceutical industry. AI can be used to perform genetic sequencing, predict the effectiveness of drugs and side effects, and manage the large amounts of data that support any pharmaceutical product. AI has the potential to significantly increase the chances of identifying new drug candidates that can be commercialized. Therefore, pharmaceutical companies must plan for a future in which AI is routinely used in drug discovery. Standigm technologies can generate candidate drugs by applying AI models, which automatically analyze the various ways in which data can be combined and configured to create weight-optimized deep learning models based on rules hidden in the data.
Experimental work will take a backseat, focusing on areas where it is necessary to validate the results of in silico drug discovery (for example, for regulatory purposes) and on areas where AI technology does not (yet) work reliably. The use of AI has been demonstrated to be beneficial in drug discovery and development processes. It can make them more cost-effective or even eliminate the need for clinical trials due to its ability to perform simulations. Additionally, AI can help select a specific sick population for recruitment in phases II and III of clinical trials by analyzing the genome profile and specific exosome of the patient. This can help predict available pharmacological targets in selected patients. AI is also attractive for drug discovery because it applies rapid and massive numerical processing capabilities of 21st century computer technologies, such as machine learning, to compare and analyze data in a fraction of the time.
Major pharmaceutical companies are partnering with AI organizations working in fields such as oncology, cardiovascular diseases and central nervous system disorders. AI can also make an important contribution to continuing to incorporate the developed drug into its correct dosage form, as well as to its optimization. This leads to faster manufacture of better quality products and ensures consistency between batches. Market forecasting is essential for several pharmaceutical distribution companies, which can implement AI in their field. The results of this article demonstrate the predominant application of machine learning and artificial intelligence methods in drug discovery and indicate a promising future for these technologies; these results should allow researchers, students and the pharmaceutical industry to delve into machine learning and artificial intelligence in a context of drug discovery and development.