Точка Синхронізації

Leaders from biotech startups Insilico Medicine and GenEditBio presented new AI-driven solutions to address critical labor shortages in drug discovery and rare disease treatment during the Web Summit Qatar this week. These executives highlighted how the pharmaceutical industry has historically struggled to treat thousands of rare conditions due to a lack of specialized human talent and the high costs of research. By implementing 'pharmaceutical superintelligence' and automated laboratory systems, these companies aim to bridge the gap between genomic potential and accessible patient therapies. Alex Aliper, CEO of Insilico Medicine, introduced his company’s 'MMAI Gym,' a platform designed to train large language models to perform as specialist scientists. This technology automates the analysis of biological and chemical data, allowing the firm to identify disease targets and candidate molecules at a fraction of the traditional cost. Recent successes include using these models to repurpose existing drugs for the treatment of ALS, a rare neurological disorder that has long lacked effective interventions. These automated systems can generate hypotheses and test them without the need for the 'legions of chemists' traditionally required for such tasks. Simultaneously, GenEditBio is utilizing AI to advance 'in vivo' gene editing, moving beyond the complex process of editing cells outside the body. CEO Tian Zhu explained that their NanoGalaxy platform uses machine learning to mine natural resources and design nanoparticle delivery vehicles that can carry gene-editing tools directly to specific tissues like the liver or eyes. This approach aims to turn gene editing into a standardized, 'off-the-shelf' injection, making treatments more affordable and scalable for global populations. The company recently achieved a major milestone with FDA approval to begin CRISPR therapy trials for corneal dystrophy. Despite these advancements, both leaders emphasized that the future of AI in medicine depends on solving a persistent data bottleneck. Current biological data sets are heavily biased toward Western populations, necessitating more localized data collection to ensure AI models work effectively for all demographics. Looking ahead, the vision for the next decade includes the development of 'digital twins' for virtual clinical trials and a significant increase in the number of FDA-approved drugs annually to combat the challenges of a globally aging population.