Artificial intelligence has been making inroads in drug discovery for a good part of the last decade. Given the transformative potential of AI, pharma companies are all in to invest for a future in which AI is routinely used in drug discovery.

Traditional drug discovery methods are time-consuming, resource-intensive, and often reliant on trial-and-error experimentation. The emergence of AI has revolutionized this paradigm empowering researchers with enhanced decision-making tools, enabling them to analyze vast datasets, extract valuable insights, and predict potential drug candidates with remarkable precision.

Drugs off-target interactions are one of the main reasons of candidate failure in the drug discovery process. Anticipating potential drug’s adverse effects in the early stages is necessary to minimize health risks on patients, animal testing, and economical costs. AI-driven methods can be exploited as first-tier screening tools proving liability estimation for drug candidates.

In this presentation we will guide you on our journey to incorporate AI through the SAS centralized analytical platform to develop AI models, in our Drug discovery process; by building a series of models able to profile small molecules; and how this allows us to prioritize only the safest compounds with a desirable activity profile.