AI's role in revolutionizing drug discovery | Kaja Milanowska-Zabel | TEDxIILOPoznań

Drug Discovery Process - Traditional drug discovery is lengthy and costly, taking 10-15 years and billions of dollars to bring a single drug to market [4][5] - AI can accelerate drug discovery and reduce costs by using machine learning and deep learning algorithms [6] - AI-driven virtual screening can identify potential drug candidates from vast chemical spaces, reducing the number of candidates for lab validation from millions to 50-100 [8][9][10] AI Applications in Drug Development - AI can assist in target identification by analyzing data to determine connections with specific indications [19] - AI can predict toxicology, efficacy, stability, and pharmacokinetics of drug candidates [20] - AI can aid in patient stratification during clinical studies by analyzing patient data (genetic information, images, physical information) to group patients who may or may not benefit from the drug [13][14] Clinical Trials and Regulatory Approval - Only 10% of drugs entering clinical studies are successful, highlighting the need for optimized clinical trial design [15] - AI can be used for risk management in clinical trials, predicting risk factors and adapting protocols based on results [17] - AI can assist in preparing FDA submissions and communicating drug information to patients using large language models [17][18]