The Role of Artificial Intelligence in Streaming Drug Discovery Processes: A Commercial Perspective

Abstract

The use of AI and ML offers a chance to enhance medication safety. These algorithms make it possible to conduct toxicity and safety evaluations based on data, which might reveal trends that would have gone unnoticed before. Logistic regression, random forests, and support vector machines are examples of traditional machine learning algorithms that may provide simple models that are easy to understand. If you want to know how the predictors influence the likelihood of an unpleasant event happening, these are the ways to proceed. Deep neural networks, a new category of approaches sometimes referred to as "artificial intelligence," make it possible to construct more complicated models, but also need a lot more data. These algorithms' strength lies in their ability to detect non-linear patterns in data automatically, with little to no human input needed. Recurrent and convolutional neural networks are two common neural network types used in medication safety research. Both models are used in pre-clinical drug toxicity studies to mimic patient variability and assist in lead selection and trial design; they are also used in post-marking surveillance for comparative efficacy research, drug-drug interaction discovery, and clinical decision support. Additional study is needed to assess the possible therapeutic effect of artificial intelligence-assisted medication safety and toxicity science, given it is still a young and expanding subject.