Ari Massoudi’s Post

Our CEO and co-founder, Avi Veidman, was featured in a STAT article, emphasizing the importance of spatial AI biomarkers in improving patient enrollment for clinical trials. The piece explores how AI is revolutionizing drug development and propelling precision medicine forward. Dive into the full story for more insights. 📖✨ https://bit.ly/4aIKOPL #spatialbiomarkers #AI #biopharma #clinicaltrials #CDx #biomarkers #precisionmedicine #precisiononcology #spatialbiology

We are excited to announce that CHARM Therapeutics has been showcased by CB Insights as one of the 100 most promising private AI companies of 2024. The 100 winners were chosen based on several factors, including CB Insights’ data on deal activity, industry partnerships, team strength, investor strength, patent activity, and their proprietary Mosaic Scores, which is created by cross-referencing thousands of datapoints to predict a company’s success. CHARM Therapeutics stands out among these top AI companies due to our unique DragonFold platform. This end-to-end 3D deep learning protein-ligand co-folding technology is designed for the discovery and development of small molecule therapeutics enabling CHARM to target and develop treatments for previously hard-to-drug targets in cancer and other therapeutic areas. To read the full AI 100 list: https://lnkd.in/gJENamuX #deeplearning #AI

A deep dive discussion from Labiotech.eu into Insilico's collaboration with University of Toronto and Igor Stagljar to design novel molecules for 15-20 "undruggable" cancer targets and the advances in Insilico's generative AI drug discovery platform, Pharma.AI, with Kyle Tretina, PhD, alliance manager of AI platforms. ➡ On the topic of "undruggability," he says: “There’s a lot of different things that can make a target undruggable, and AI can really help with quite a few of them. So first would be structural unavailability. When a protein is acting in a signaling pathway that is related to the development of a disease, there’s usually certain parts of the protein that are involved in that process more than other parts. “If it’s an enzyme, there’s maybe a little pocket that is involved in changing other molecules and enabling signaling. So it might be actually the case that the drug target may be structurally unsuitable for drug binding within that pocket where you want the small molecule to bind for chemical reasons. This is really where I think AI has been extremely powerful because of that imaginative ability." *Link to podcast and article in comments #ai #platform #drugdiscovery #undruggable #cancer #targetid #chemistry #pharma

🎗️We've just unveiled an exclusive collaboration with Dr. Artem Cherkasov and have integrated a proprietary Deep Docking AI Platform for DNA-damage response targets into our business model. With this proprietary AI platform, we're revolutionizing the discovery and development of targeted cancer drugs. 🧬💊 Our team can now rapidly evaluate billions of molecular structures, focusing on DNA-damage response-related vulnerabilities prevalent in various cancer types. This innovative approach enables us to expedite the drug discovery process.💡 From preclinical research to human clinical trials and potential partnerships, we're accelerating the journey to groundbreaking therapies. Our commitment to advancing precision medicine and delivering hope to millions of patients remains steadfast. 💪 With the support of Dr. Artem Cherkasov and the Deep Docking AI Platform, Rakovina is poised to make significant strides in the fight against cancer. 🌟 Join us as we embark on this transformative journey! Together, we're reshaping the future of cancer treatment. 🌌 #RakovinaTherapeutics #PrecisionMedicine #AIinHealthcare #CancerResearch 🎉 https://lnkd.in/gxS59_6A

A piece we put together on Flywheel.io's #AI factory for research involving medical imaging is now live as part of Drug Discovery World's new eBook

🚀 𝑵𝒆𝒘 𝑨𝑰 𝑻𝒐𝒐𝒍𝒔 𝑰𝒎𝒑𝒓𝒐𝒗𝒆 𝑪𝒂𝒏𝒄𝒆𝒓 𝑫𝒊𝒂𝒈𝒏𝒐𝒔𝒊𝒔 𝒂𝒏𝒅 𝑻𝒓𝒆𝒂𝒕𝒎𝒆𝒏𝒕 Exciting news in clinical AI! 𝑃𝑎𝑖𝑔𝑒 𝑎𝑛𝑑 𝑀𝑖𝑐𝑟𝑜𝑠𝑜𝑓𝑡 have launched 𝐕𝐢𝐫𝐜𝐡𝐨𝐰𝟐 𝐚𝐧𝐝 𝐕𝐢𝐫𝐜𝐡𝐨𝐰𝟐𝐆, two AI models that aim to improve how we diagnose and treat cancer. 📊 𝐊𝐞𝐲 𝐏𝐨𝐢𝐧𝐭𝐬 👉 Trained on over three million pathology slides from 45 countries.   👉 Virchow2G can analyze 40 different tissue types and is the largest model with 1.8 billion parameters. ✔️These models help make pathology more accurate, speed up diagnostics, and reduce errors, especially with rare cancers. ✔️In addition to diagnostics, these tools support pharmaceutical companies in developing drugs and running clinical trials, speeding up the creation of new treatments. ✔️Virchow2 is available for non-commercial research on Hugging Face, and commercial applications are now open for wider use 🌍. Paige and Microsoft are pushing forward precision medicine and advancing cancer research. Let’s discuss how AI is changing healthcare! #AIinHealthcare #CancerResearch #HealthcareInnovation

The rapid growth of Lantern Pharma’s $LTRN #AI platform could lead to accelerated development of better treatments, greater precision in clinical development, and improved combination regimens with the potential for longer and more durable patient responses. Lantern's RADR® platform recently surpassed 60 billion data points and is planned to exceed 100 billion data points during 2024 and has been crucial in the expansion of the indications for drug candidate LP-184 and in the accelerated development of LP-284. Lantern seeks to focus additional data growth efforts of the RADR® platform on: drug sensitivity data, combination treatment outcome data, and biomarker data in rare cancers, and on emerging synthetic lethal targets that are aimed at accelerating the development of new therapies for Lantern and its partners. Lantern will also enhance the RADR® platform's generative AI capabilities, focusing on molecular optimization and automated feature extraction to improve understanding and prediction of molecular dynamics, safety, and drug-drug interactions. https://lnkd.in/g7Ekkh68

✨ ✨ ✨ I'd like to kick off a new series highlighting some of the best and brightest across the Life Science space ✨ ✨ ✨   My first instalment of this kicks off with a company doing incredible work in the Drug Design space... 🥁 🥁   Exscientia   🔎 They utilise precision information derived from patient tissues alongside sophisticated AI technology to source optimised drug profiles for research. This integrated system has been the first to demonstrate improved clinical outcomes in oncology, and I believe that is just the beginning in their revolution.   📈 They currently have four targets at pre clinical, and phase 1-2, across Haematology, Oncology, and Inflammatory Diseases. With 40% of total clinical growth within Biotech/ Pharmaceuticals sitting within Oncology in 2023, Exscientia should be on your radar!   🗞 With the recent appointment of Marie-Louise Fjaellskog, and promotion of John Overington, I can't wait to see the continued success of this truly pioneer company. #Biotech #innovation #AI #DrugDesign

Check out this new eBook from Drug Discovery World, “Using Imaging in Drug Discovery,” where you can learn more about: • How new imaging modalities are supporting the development of new therapies for cancer and neurodegenerative diseases. • The benefits of integrating #AI with medical imaging for clinical trials via Flywheel and the NVIDIA MONAI toolkit. • And more We were excited to be a part of this piece exploring the important role that analysis-ready imaging plays in drug discovery. Download now to learn more.

Is precision oncology killing more patients than it's saving? Shocking, I know. But hear me out... The latest market analysis reveals a disturbing trend: For almost every successful precision therapy, 3 others fail in late-stage trials. This isn't just burning through billions in R&D. It's burning through patient hope. Here's the kicker: 80% of these failures are due to poor biomarker strategy, not drug efficacy. We're chasing the wrong targets, folks. 🔬 The deep dive into the top 50+ #oncology pipelines shows: - 65% of companies are over-invested in "me-too" PD-1/PD-L1 inhibitors - Only 12+% are seriously exploring multi-omics approaches - A mere 5+% have robust real-world evidence programs The result? A #market saturated with lookalikes, while true innovation stagnates. But it's not all doom and gloom. The analysis identified 3 under-the-radar #biotechs poised to disrupt this pattern. Their secret? Integrating #AI with multi-omics for hyper-personalized trial design. Early results? Staggering. - 40+% reduction in trial failures - 60% improvement in patient outcomes - 2x faster time-to-market The oncology market is at a crossroads. Will you stick with the failing status quo? Or pivot to where the real breakthroughs are happening? The choice is yours. But choose fast. Because in this game, second place is just the first loser. Agree? Disagree? Let's debate. Drop your thoughts below 👇 𝐏.𝐒. 𝐃𝐨𝐧'𝐭 𝐟𝐨𝐫𝐠𝐞𝐭 𝐭𝐨 𝐫𝐞𝐩𝐨𝐬𝐭 ♻️ 𝐭𝐡𝐢𝐬 𝐟𝐨𝐫 𝐨𝐭𝐡𝐞𝐫𝐬. Let's shake up the industry together. #ColorectalCacner #Oncolyst