Bristol Myers Squibb’s Post

Generative AI: A new frontier for drug discovery in the Middle East 🌍 Generative AI is a powerful new technology that has the potential to revolutionize drug discovery 💊. By leveraging the power of machine learning and AI, generative AI can generate new molecules and drug candidates that could lead to the development of new treatments for a wide range of diseases 🧬. The Middle East is well-positioned to capitalize on the potential of generative AI for healthcare 💡. The region has a strong scientific and technological expertise, as well as a growing number of startups and research institutions focused on AI 🚀. In addition, the Middle East has a young and growing population, which means that there is a high demand for new and innovative healthcare solutions 🙌. Key takeaways: Generative AI can be used to accelerate the drug discovery process by suggesting novel compounds, optimizing molecular properties, and expanding chemical libraries 🔬. Generative AI can also be used to identify new therapeutic applications for existing drugs 💊. The Middle East is well-positioned to capitalize on the potential of generative AI for healthcare 💡. #generativeAI #drugdiscovery #MiddleEast #healthcare #AI Learn more in the attached article!

From clinical trials to predictive analytics, “It is an exciting time for life sciences, and AI will undoubtedly play a significant role in shaping its future”. Despite being new, its potential for growth and power knows no bounds. With its ability to learn and adapt, AI is revolutionizing industries and transforming lives. https://hubs.la/Q01YmV_S0 #clinicaltrials #digitalsolutions

Artificial Intelligence in Drug Discovery Market Size Projection ... - Medgadget: Artificial Intelligence in Drug Discovery Market Size Projection ...  Medgadget #AI #artificialintelligence #Finperform

🌟 AI / Science / Healthcare 🌟 The way in which AI will allow us to discover what was previously unimaginable. AlphaFold, developed by DeepMind, is making groundbreaking strides in the field of artificial intelligence. This advanced AI system is revolutionising the way scientists predict protein structures, which is crucial for understanding how diseases work and developing new medicines. By understanding the complex shapes of proteins, researchers can now more accurately design drugs to target specific diseases. This could lead to faster and more effective treatments for a wide range of illnesses, ultimately improving healthcare for all. AlphaFold's innovative technology has the potential to transform the pharmaceutical industry and accelerate the discovery of life-saving medications. Keep an eye on the incredible progress of AlphaFold as it continues to pave the way for exciting advancements in AI and healthcare! 🚀💡 #AI #DeepMind #AlphaFold #HealthcareInnovation #ArtificialIntelligence #FutureTechnology If you would like a more in-depth read on this topic, click the link provided:

https://lnkd.in/eXJvycT7 AI is accelerating the pace of discovery—but at what cost?

A new blog posting titled "Generative AI in Drug Discovery: A Human-in-the-Loop Perspective" is live now at https://lnkd.in/dGX5usem. The takeaways are, - Generative AI offers valuable tools for hypothesis generation in drug discovery - Optimization and decision-making remain challenging areas for generative AI, particularly in science-driven research - Human-in-the-loop AI is crucial for ensuring the reliability and effectiveness of generative AI in drug discovery - A strategic approach is necessary to integrate generative AI with other established techniques - Medicinal chemists should embrace human-in-the-loop AI systems for career advancement Please leave a comment or drop me an email for more in-depth discussion on this theme.

“One-AI” - how to use #AI successfully. Case study: “The biotech company Insilico Medicine exemplifies the power of the One-AI approach, which they have used to dramatically speed up drug development while reducing cost. Typically, it takes a new drug between three and six years and $430 million just to get to the trial stage of development. Insilico developed the world’s first AI-designed drug in just 18 months, for only $2.6 million. How did pairing generative and predictive AI help make that happen? In the drug discovery process, AI is deployed to both identify the target molecule and design drugs to interact with it. Insilico used predictive AI to locate molecular compounds in the body that play a role in the progression of a rare lung disease. A natural language-processing engine then cross-referenced information about these targets with existing diseases, patents, and research literature to identify gaps. At that point, a generative AI model developed drug-like molecules from scratch that could potentially curb the disease. From those results, predictive AI algorithms selected the most promising molecules to advance to the clinical trial stage. The absence of either predictive or generative AI would have restricted the range of possibilities available to Insilico. “

Google DeepMind has launched AlphaFold 3, a new AI model that can predict DNA and protein structures. Hoping to operate with precision accuracy, the system will witness how molecules interact together across millions of different combinations. The goal, according to Google DeepMind, is to truly understand the processes of life up close and transform healthcare outcomes. Read my story covering the announcement below! #AI #Technology #Healthcare #Innovation

An interesting read in #TheAtlantic on how #AI is challenging the very nature of #scientificdiscovery. However, it is also upending the traditional #timelines to achieve them as well! Read on... https://lnkd.in/gwBM-mW7

Reading these two articles in the same day was interesting. First, I read this article from #ScientistLive examining a survey conducted by the #PistoiaAlliance regarding the perceptions of #LifeSciences professionals towards AI ➡️ https://lnkd.in/gWuf_dyM What stood out to me the most was that 30% of life sciences experts do not recognize the power of #AI and 63% of those who do question its reliability. Then, I came across an article below from Neil Versel on #BioSpace discussing the extensive capabilities of AI in #BioPharma, including data management, drug repurposing, discovery, clinical oversight, etc. One prominent issue highlighted in both articles is the lack of readily available and comprehensible information about artificial intelligence. Some scientists are hesitant to embrace it due to fears of job displacement, while others struggle to grasp its workings or adapt to its implementation. So, I wonder: what's your opinion about AI and #GenAI? How can we raise more awareness and make it more approachable?