AI companies are using machine learning to improve clinical trial design and execution for existing drugs, aiming to accelerate drug development. Current AI applications in clinical trials focus on process optimisation rather than advanced concepts like full trial simulation. The industry is not yet implementing more speculative AI applications in this area.
Argonne National Laboratory researchers have developed MProt-DPO, an AI-driven computing framework for accelerating protein design. It combines various data sources and uses large language models and supercomputers to process billions of parameters, achieving over one exaflop of performance. The framework incorporates a Direct Preference Optimization algorithm to improve design reliability and is being tested on protein design challenges, with plans for laboratory validation.
Stanford Medicine researchers developed an AI method to identify complex structural variants in the human genome with 95% accuracy. The study analysed over 4,000 genomes worldwide, focusing on schizophrenia and bipolar disorder. The AI identified over 8,000 distinct variants, many in regions regulating brain development. This could improve understanding of heritable psychiatric conditions and potentially lead to more precise treatment approaches.
