David González’s Post

“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. “

@Artificial Intelligence, Deep Learning, Machine Learning

While there is a clear consensus that artificial intellingence (AI) has a role to play in future of scientific research and development, how its power and potential is harnessed is still the focus of ongoing debate. With this in mind, ArisGlobal has established a new generative AI council, that will consist of representatives from the fields of tech, academia, pharma and regulation. Learn more about the objectives of this new body below. #artificialintelligence #ai #lifesciences #lifesciencesindustry #biotechnology #biotechnologyindustry #researchanddevelopment #innovation #regulation

Interested in learning more about how (good quality) DEL data can drive the development of better drug discovery algorithms for AI? At X-Chem we can provide AI support for your drug discovery project based on your data, but we also are taking steps to provide broader support for the development of this field through our collaboration with SGC. Read about it below, and reach out if you want to learn more! #cro #drugdiscovery #contractresearchorganization #aidrugdiscovery

Generative AI is transforming every step of the pharma R&D and LLMs can also be used as copilots to control many domain-specific AI models to plan the shortest route from disease hypothesis to the patient. Nice article in Forbes by Bernard Marr. Happy to see Insilico Medicine mentioned. In the early days, especially in 2017-2019, many companies and reviewers criticized us for focusing on generative AI systems instead of sticking to more classical methods. Now, many of them are using our tools.

In-situ data is so valuable-- the chemical probe/ligand-protein interactions across various cell lines data is such a big treasure. It enables us to transcend the limitations of existing models like Alphafold2. Leveraging such information, we can even build models predict the ligand performance in different cell types, predicting degraders, allosteric binders, mutant selective inhibitors, and biomarkers. So much to expect. #chemoproteomics #AI #TPD #allostericdrugs #biomarkers #alphafold

Are you interested in the future of AI❔ Click below for a fantastic article now live! 📣 Robert Scoffin and Matthew Habgood from solutions provider Cresset look to the future of drug discovery and the roles that artificial intelligence and machine learning could play. #AI #ML #drugdiscovery

Did you catch Fortune Magazine’s latest article “The next evolution of AI is already here—and hiding in plain sight"? This article shows how #partnerships and collaboration are spearheading the next phase of #AIevolution. The biotech company Insilico Medicine exemplifies the power of the One-AI approach, which they have used to dramatically speed up #drugdevelopment 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. Continue reading: https://lnkd.in/guxp4gfv #FortuneMagazine #AIDrugdiscovery #raredisease #AIalgorithm #clinicaltrials

AI continues to unlock unprecedented possibilities in drug testing and development. A recent article from BioPharmaTrend.com highlights AI's transformative journey in drug discovery, showcasing groundbreaking concepts that have reshaped and continue to reshape traditional approaches. Quris AI is proud to be a part of this revolution, with our Bio-AI Clinical Prediction Platform that is revolutionizing how drug candidates are evaluated for safety and efficacy in humans. Together, we will continue driving innovation and transforming healthcare. https://lnkd.in/eTwDwMgn  #AI #DrugDiscovery #HealthcareInnovation #QurisAI

Did you catch Fortune Magazine’s latest article “The next evolution of AI is already here—and hiding in plain sight"? This article shows how #partnerships and collaboration are spearheading the next phase of #AIevolution. The biotech company Insilico Medicine exemplifies the power of the One-AI approach, which they have used to dramatically speed up #drugdevelopment 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. Continue reading: https://lnkd.in/guxp4gfv #FortuneMagazine #AIDrugdiscovery #raredisease #AIalgorithm #clinicaltrials