Using AI to drive home drug development

Given there is no open source software available for the types of analysis required, proprietary algorithms are required. For this, mechanisms to extract data based on the objective of analysis are required together with the selection of the necessary biological entities and features related to the objective of analysis. From this, AI can assist the scientist with interrogating the data to clarify ambiguity or verify the relevance of entities, helping the scientists on a faster path towards drug discovery. For example, a project might utilize a next generation platform to predict the absorption, distribution, metabolism, excretion, and toxicity of new drug candidates far faster than any traditional laboratory testing could achieve. Hence, AI has the potential to provide deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes.