{"id":13305,"date":"2024-02-29T06:00:00","date_gmt":"2024-02-29T14:00:00","guid":{"rendered":""},"modified":"2024-08-06T14:39:24","modified_gmt":"2024-08-06T21:39:24","slug":"microsoft-and-1910-genetics-partner-to-turbocharge-rd-productivity-for-the-pharmaceutical-industry","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/quantum\/2024\/02\/29\/microsoft-and-1910-genetics-partner-to-turbocharge-rd-productivity-for-the-pharmaceutical-industry\/","title":{"rendered":"Microsoft and 1910 Genetics partner to turbocharge R&D productivity for the pharmaceutical industry"},"content":{"rendered":"\n

Unprecedented collaboration will build the most powerful, fully integrated, AI-driven drug discovery and development platform to dramatically improve pharmaceutical research and development (R&D) productivity and bring novel therapeutics to patients faster and more cost-effectively than traditional approaches.<\/p>\n\n\n

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Microsoft and 1910 Genetics<\/a>, the only biotechnology company advancing small and large molecule drug discovery with a multimodal AI platform powered by laboratory automation, announced a new five-year commercial agreement and go-to-market collaboration to harness advanced AI and next generation high-performance computing (HPC). This is to reverse 70 years of declining pharmaceutical R&D productivity by combining 1910\u2019s massive computational and wet lab biological data, robotics-driven laboratory automation and multimodal AI models for drug discovery with Microsoft Azure Quantum Elements<\/a>, a groundbreaking cloud platform purpose built at the intersection of advanced AI and HPC to accelerate scientific discovery. 1910 Genetics will offer the platform to pharmaceutical, biotechnology, government, academic, and research institutions via three partnership models: co-discovery, co-engineering, and Platform-as-a-Service (PaaS). <\/p>\n\n\n\n

AI enables a paradigm shift for scientific discovery<\/h2>\n\n\n\n

Since the 17th<\/sup> century, the scientific method\u2014the fundamental process by which scientists make discoveries\u2014has remained largely unchanged. While this empirical approach has yielded some of mankind\u2019s most remarkable breakthroughs, the scientific method is inherently slow, and human-intensive, requiring iterative cycles of observation, hypothesis, experimentation, and analysis. Today, AI offers an opportunity to compress these cycles and accelerate the time from observation to discovery. This paradigm shift\u2014\u201cAI for Scientific Discovery<\/strong>\u201d, is characterized by the augmentation of human intelligence by AI to rapidly digest centuries of scientific knowledge, identify patterns, make observations, postulate hypotheses, and predict results, all while reducing the need for experimentation. At its most basic level, AI will transform scientific discovery across all industries\u2014from pharmaceuticals to chemicals, sustainable energy, automotive, manufacturing, technology, and more.<\/p>\n\n\n\n

Pharma\u2019s declining R&D productivity can be reversed by AI<\/h2>\n\n\n\n

In the pharmaceutical industry, scientific discovery is fundamentally geared towards the design and development of novel therapies to combat diseases such as cancer, cardiovascular disease, infectious diseases, and neurodegenerative conditions like Alzheimer\u2019s and Parkinson\u2019s disease. However, pharmaceutical R&D is a costly, time-consuming, and failure-prone process. On average, it takes 12\u201315 years and costs over $2 billion to develop a new drug, with a less than 10% probability that a new molecule entering Phase 1 clinical testing will be successfully approved by the FDA. Since the 1950s, pharmaceutical R&D productivity\u2014defined as the number of new drugs approved by the FDA per billions of dollars invested in R&D\u2014has steadily declined, an observation often revered to as \u201cEroom\u2019s Law\u201d\u2014which is Moore\u2019s law in reverse.<\/p>\n\n\n\n

As humans live longer into old age, we are more likely to become sick, with more diseases to treat. Yet there are not enough therapies currently produced by the pharmaceutical industry to keep pace with aging and disease, given the R&D productivity bottleneck. This bottleneck is caused by a traditional, artisanal and human-intensive approach to drug discovery and development that has remained unchanged for decades\u2014and one for which AI holds tremendous potential to transform. <\/p>\n\n\n\n

To realize the promise of AI, the entire drug discovery and development value chain has to be reimagined from the ground up with AI. Critically, an AI-driven drug discovery and development process needs to overcome the pharmaceutical industry\u2019s historically fragmented and disparate data, while creating novel types of data at scale, purpose-built for AI, and with a standardized end-to-end infrastructure that puts massive parallel data, AI, and laboratory automation in the same iterative loop.<\/p>\n\n\n\n

1910 Genetics is realizing the promise of AI for science to accelerate R&D productivity<\/h2>\n\n\n\n

Founded in 2018 and backed by leading investors including M12-Microsoft\u2019s Venture Fund, and Playground Global, 1910 Genetics was built from the ground up as a next-generation, AI-native biotechnology company with three differentiating capabilities:<\/p>\n\n\n\n