Broad Institute of MIT and Harvard’s Post

The BROAD's AI models promise to help better inform #research and make #drugdevelopment more efficient. These models have been trained with FDA-curated data and can predict (potential) toxic effects before testing in humans. This is hopeful, not only for efficiency's sake, but in a more conscious and improved approach for human beings. Broad Institute of MIT and Harvard #ai #aimodels #stem #scienceandtechnology #healthcare #clinicalresearch #innovation #drugdiscovery #researchanddevelopment #meaningfulai #tech #technology

Potential toxic effects of drugs by predictive AI... Drug-induced liver injury (DILI)... Prediction_Probobility_Literature_Validation/Confirmation... False negative vs. False positive... Phase I clinical trials guided by the prediction...

There is a lot of money going into AI for drug discovery, and there is plenty of hype. But there are also a lot of really interesting things happening that are already transforming the way drugs are discovered and developed. Now comes the critical test - how well AI predictions hold up when drugs are tested in humans.   “There is still a lot we don’t know about human biology, the evolution of disease and why some patients respond better to drugs than others,” Gisbert SCHNEIDER at ETH Zurich told SWI swissinfo.ch . “There’s a tendency now to overhype the potential benefits of AI tools, because we forget this element of chaos when we interact with human biology.” #biotech #pharma #AI #deeplearning   For full story. https://t.co/kgDbmhkb1K

A new AI tool now allows researchers to model the structure of all biomolecules and how these elements interact with each other – which can be key to drug development #InvestingInvolvesRisk

A new AI tool now allows researchers to model the structure of all biomolecules and how these elements interact with each other – which can be key to drug development #InvestingInvolvesRisk

The impact of #AI in medicine is profound. Imagine solving biochemical puzzles in hours, not years! This article explores how AI is accelerating drug discovery & development. #HealthInnovations   From ‘turbocharged’ vaccines to powerful antibiotics, AI-driven drug discovery is tackling complex health challenges faster than ever.   https://lnkd.in/dz4PVmMT

More than a decade spent and upwards of $1 billion exhausted. That’s what it takes to develop a new drug. Why is this happening? In my latest article, I delve into the problems in the current drug discovery process and how AI is transforming this field with faster, more precise, solutions. Dive into the future today! Check out my article: https://lnkd.in/gJMwxb96

🚀 GenAI revolutionizes life sciences! In recent years, this generative artificial intelligence (GenAI) has accelerated drug development, predicting new compounds with medicinal properties. It’s like a supercomputer in a lab coat! Moreover, GenAI unravels human body mysteries, revealing patterns and links that elude even the most experienced minds. Personalized healthcare is on the horizon, tailored to individual genetic makeup. In her latest article and video, @Grishma Nair, Life Sciences and Healthcare Client Partner at @Cognizant shares her view on how generative AI holds immense promise for faster, more efficient drug development, benefiting patients worldwide. #AI #DrugDevelopment #Pharmaceuticals #Innovation

While AI can accelerate drug development it cannot replace human expertise. https://okt.to/OU6lMn #ArtificialIntellegence #HealthcareSolutions #DrugDevelopment

How AI could transform drug development and the life sciences. Where are we going to be in 5 years? - Open - Multi modal - Empowering - Imagination - Cures Summary by GPT: 🔬 AI's Impact in Medicine: The panel discusses the transformative potential of artificial intelligence (AI) in drug development. AI's ability to analyze vast amounts of data can revolutionize the identification of new drug targets and the development of therapeutics. 🧬 Advances in Biomedical AI: Innovations such as digital twins and causal AI models allow for a new approach to understanding human diseases and drug discovery. This technology enables a deeper insight into the "hidden circuitry" of diseases, which has been a major challenge in medicine. 🧪 Challenges in Drug Development: Drug development is notoriously fraught with high failure rates, with over 80% of drugs failing in clinical trials. AI offers a promise to significantly reduce these failure rates by enhancing target identification and patient response prediction. 👨🔬 Integration of AI and Biomedical Research: The conversation covers the integration of AI tools like generative AI into the process of protein structure prediction and its implications for drug design. This integration marks a significant shift from traditional methods, making the drug discovery process more efficient and less costly. 📈 Future Prospects: The panelists discuss the future of AI in biomedicine, emphasizing the ongoing need for improvements in data quality, interdisciplinary collaborations, and the development of AI systems that can lead to actual clinical and therapeutic breakthroughs. Insights based on numbers: - Cost and Speed of Genome Sequencing: The cost of sequencing a human genome has dramatically decreased from $11 billion over a decade to less than $1,000 in less than a day, accelerating biomedical research and applications of AI. - Drug Development Failure Rates: Highlighting the need for innovation, more than 80% of drugs that enter clinical trials fail, underscoring the potential impact of AI in improving success rates. https://lnkd.in/g3fcVxiS #AI #GenAI #Health #Healthcare #Genetics #Science #Medicine #Pharmaceutical #ProductDevelopment #ProductManagement