Manideep Patel’s Post

🌟 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐰𝐢𝐭𝐡 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐌𝐞𝐝𝐢𝐜𝐢𝐧𝐞 & 𝐃𝐫𝐮𝐠 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 🌟 - IndustryARC™ 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐌𝐞𝐝𝐢𝐜𝐢𝐧𝐞 & 𝐃𝐫𝐮𝐠 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐌𝐚𝐫𝐤𝐞𝐭 Size is forecast to reach $ 1183.1 Million by 2030, at a CAGR of 3.30% during forecast period 2024-2030. 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐬𝐚𝐦𝐩𝐥𝐞 𝐫𝐞𝐩𝐨𝐫𝐭 : @ https://lnkd.in/gM2UsW2g 𝐀𝐈 𝐚𝐧𝐝 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧:The incorporation of AI and machine learning algorithms is revolutionizing drug discovery and computational medicine. These technologies enhance the ability to predict molecular interactions 𝐈𝐧 𝐒𝐢𝐥𝐢𝐜𝐨 𝐓𝐫𝐢𝐚𝐥𝐬 𝐚𝐧𝐝 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧𝐬: In silico trials, which use computer simulations to model the effects of drugs and treatments, are becoming increasingly prevalent. The concept of digital twins, where virtual models of patients are created to simulate and predict individual responses to treatments, is also gaining traction. 𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐇𝐢𝐠𝐡-𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 (𝐇𝐏𝐂): The adoption of cloud computing and HPC is enabling the handling of vast datasets and complex computational tasks required in drug discovery. 𝐠𝐞𝐭 𝐦𝐨𝐫𝐞 𝐢𝐧𝐟𝐨 : @ https://lnkd.in/gN6esfCy 𝐓𝐨𝐩 𝐤𝐞𝐲 𝐩𝐥𝐚𝐲𝐞𝐚𝐫𝐬 : Hologic, Inc.|Genesis Healthcare System|Jazz Pharmaceuticals|Sinobiopharma, Inc. |Incyte|ONO|athenahealth |Octapharma|Ipsen|Dexcom|Watershed|Teleflex|Bio-Rad Laboratories|Exact Sciences |BioMarin Pharmaceutical Inc.|enVista|Royalty Pharma|Seagen|Grünenthal Group|Neurocrine Biosciences|Exelixis|Alnylam Pharmaceuticals|OSI Systems|Chr. Hansen              #DrugDiscovery #ComputationalBiology #Bioinformatics #PharmaTech #MedTech #AIinMedicine #MachineLearning #InSilico #DigitalHealth #Biotech #PersonalizedMedicine #HealthTech #ClinicalTrials #Genomics #MolecularModeling

🌟 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐰𝐢𝐭𝐡 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐌𝐞𝐝𝐢𝐜𝐢𝐧𝐞 & 𝐃𝐫𝐮𝐠 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 🌟 - IndustryARC™ 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐌𝐞𝐝𝐢𝐜𝐢𝐧𝐞 & 𝐃𝐫𝐮𝐠 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐌𝐚𝐫𝐤𝐞𝐭 Size is forecast to reach $ 1183.1 Million by 2030, at a CAGR of 3.30% during forecast period 2024-2030. 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐬𝐚𝐦𝐩𝐥𝐞 𝐫𝐞𝐩𝐨𝐫𝐭 : @ https://lnkd.in/gM2UsW2g 𝐀𝐈 𝐚𝐧𝐝 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: The incorporation of AI and machine learning algorithms is revolutionizing drug discovery and computational medicine. These technologies enhance the ability to predict molecular interactions 𝐈𝐧 𝐒𝐢𝐥𝐢𝐜𝐨 𝐓𝐫𝐢𝐚𝐥𝐬 𝐚𝐧𝐝 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧𝐬: In silico trials, which use computer simulations to model the effects of drugs and treatments, are becoming increasingly prevalent. The concept of digital twins, where virtual models of patients are created to simulate and predict individual responses to treatments, is also gaining traction. 𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐇𝐢𝐠𝐡-𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 (𝐇𝐏𝐂): The adoption of cloud computing and HPC is enabling the handling of vast datasets and complex computational tasks required in drug discovery. 𝐠𝐞𝐭 𝐦𝐨𝐫𝐞 𝐢𝐧𝐟𝐨 : @ https://lnkd.in/gN6esfCy 𝐓𝐨𝐩 𝐤𝐞𝐲 𝐩𝐥𝐚𝐲𝐞𝐚𝐫𝐬 : Hologic, Inc.|Genesis Healthcare System|Jazz Pharmaceuticals|Sinobiopharma, Inc. |Incyte|ONO|athenahealth |Octapharma|Ipsen|Dexcom|Watershed|Teleflex|Bio-Rad Laboratories|Exact Sciences |BioMarin Pharmaceutical Inc.|enVista|Royalty Pharma|Seagen|Grünenthal Group|Neurocrine Biosciences|Exelixis|Alnylam Pharmaceuticals|OSI Systems|Chr. Hansen              #DrugDiscovery #ComputationalBiology #Bioinformatics #PharmaTech #MedTech #AIinMedicine #MachineLearning #InSilico #DigitalHealth #Biotech #PersonalizedMedicine #HealthTech #ClinicalTrials #Genomics #MolecularModeling

We take a closer look at our speakers starting with Thrasyvoulos Karydis, Co-Founder and CTO at DeepCure. Each speaker is addressing the wider topic of using technology to bridge silos within drug discovery. His talk will cover: AI-driven drug discovery beyond kinases: How to NOT get trapped in a local minimum when designing drugs for intractable targets Here is his abstract for more details: "When binding data for a target is not available, limited, or biased, most AI drug discovery companies are ill-equipped to deliver novel, viable starting points for optimization and/or leads. This is especially true for therapeutic targets in inflammation, where targeting Protein-Protein and Protein-DNA interactions requires working with highly-charged, highly-3D-shaped molecules, pushing the limits of existing cheminformatics and computational chemistry tooling. Moreover, in inflammation there is a narrow window of acceptability for ADME-tox properties due to chronic dosing dictating the need for multi-dimensional analysis of experimental data. To tackle these challenges, biases need to be removed from every part of the design-build-test-learn cycle. At DeepCure we are removing limitations to novelty and diversity in each step, with AI tools that give a framework for identifying novel binding opportunities and design tools that allow drug design in novel chemical space while taking into account desired ADME-tox profile and synthesizability requirements. Accessing novel chemical space is made possible by a robotic automated custom synthesis system that allows for the reproducible multi-step synthesis of molecules from 100+ different reaction types." Find the link in the description to register for ChemTalks -» #MeetCXN #ChemTalks #DrugDiscovery

Generative AI is transforming the landscape of drug delivery systems, revolutionizing how we approach this critical aspect of biotechnology. As a biotech researcher, I am fascinated by how generative AI accelerates innovation by enabling the creation of sophisticated drug delivery mechanisms with unprecedented precision and efficiency. Researchers in Australia, lead by Monash University, have developed an innovative AI tool that promises to transform virtual screening processes in early-stage drug discovery, significantly boosting scientists' capacity to identify potential new medications. Read more: https://lnkd.in/gGybAX4H #Drug_delivery #generative_ai #monash_university

🚀 Unlocking the Secrets of Drug Discovery with Compound Libraries! 🧪 Have you ever wondered how researchers find new medicines? It all starts with a powerful tool: Compound Libraries. In my latest article, I break down: 🔬 What compound libraries are and how they work. 💡 The role of High-Throughput Screening (HTS) in identifying potential drugs. 🤖 How AI is transforming the way we analyze and manage compound libraries. 🌍 Real-world examples showcasing how this approach leads to life-saving discoveries. This article simplifies the science so everyone can understand how the building blocks of drug discovery come together to create breakthroughs in medicine. Read the full article here: https://lnkd.in/gRB66aRD Let me know your thoughts—how do you see AI shaping the future of drug discovery? 📲 Follow me for more insights into biotech, AI, and innovation! 📸 Don’t miss my latest updates on Instagram: @mrratanbajaj #DrugDiscovery #CompoundLibraries #AIinPharma #PharmaceuticalResearch #Innovation #Biotech

𝐀 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐯𝐞 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐀𝐈 𝐢𝐧 𝐃𝐫𝐮𝐠 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 [𝐏𝐃𝐅] 𝐆𝐞𝐭 𝐚 𝐅𝐫𝐞𝐞 𝐒𝐚𝐦𝐩𝐥𝐞 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐏𝐃𝐅 𝐑𝐞𝐩𝐨𝐫𝐭: https://lnkd.in/dddNRqR2 The global AI in drug discovery market, valued at $0.9 billion in 2023, is projected to grow to $4.9 billion by 2028, with a CAGR of 40.2%. 𝐀𝐈 𝐢𝐬 𝐫𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐢𝐧𝐠 𝐝𝐫𝐮𝐠 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐛𝐲 𝐬𝐩𝐞𝐞𝐝𝐢𝐧𝐠 𝐮𝐩 𝐚𝐧𝐝 𝐢𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐭𝐡𝐞 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬. 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥𝐥𝐲, 𝐝𝐫𝐮𝐠 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐭𝐚𝐤𝐞𝐬 𝐲𝐞𝐚𝐫𝐬, 𝐛𝐮𝐭 𝐀𝐈 𝐜𝐚𝐧 𝐡𝐞𝐥𝐩 𝐛𝐲: ✔ 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: AI rapidly analyzes vast datasets, identifying potential drug candidates from genomic, chemical, and biological data. ✔ 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐞 𝐃𝐞𝐬𝐢𝐠𝐧: AI algorithms can design new molecules by predicting which structures are most likely to interact effectively with disease targets. ✔ 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐬: AI predicts drug efficacy and safety by analyzing past data, helping to reduce failed trials and side effects. ✔ 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐓𝐫𝐢𝐚𝐥𝐬: AI streamlines clinical trials by identifying suitable patient groups, predicting outcomes, and monitoring progress. 𝐓𝐡𝐞 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐫𝐞𝐩𝐨𝐫𝐭 𝐢𝐧𝐜𝐥𝐮𝐝𝐞 NVIDIA Exscientia Google BenevolentAI Recursion Insilico Medicine Schrödinger. Microsoft Atomwise Illumina NuMedii XtalPi Inc. iktos Arterys (Acquired by Tempus Labs) Deep Genomics, Verge Genomics BenchSci insitro Valo Health BPGbio, Inc. Predictive Oncology Labcorp IQVIA IGE/Affinity Media (acq. Tencent Holdings Ltd.) Celsius Therapeutics CytoReason Owkin Cloud Pharmaceuticals, Inc. Evaxion Biotech A/S Standigm BioAge Labs Envisagenics ARIAD Pharmaceuticals, Inc. #aiindrugdiscovery #artificialintelligence #drugdiscovery #machinelearning #biotechnology #pharmaceuticals #healthtech #aiforhealth #medicalinnovation #lifesciences #digitalhealth

𝐀 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐯𝐞 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐀𝐈 𝐢𝐧 𝐃𝐫𝐮𝐠 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 [𝐏𝐃𝐅] 𝐆𝐞𝐭 𝐚 𝐅𝐫𝐞𝐞 𝐒𝐚𝐦𝐩𝐥𝐞 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐏𝐃𝐅 𝐑𝐞𝐩𝐨𝐫𝐭: https://lnkd.in/dddNRqR2 The global AI in drug discovery market, valued at $0.9 billion in 2023, is projected to grow to $4.9 billion by 2028, with a CAGR of 40.2%. 𝐀𝐈 𝐢𝐬 𝐫𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐢𝐧𝐠 𝐝𝐫𝐮𝐠 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐛𝐲 𝐬𝐩𝐞𝐞𝐝𝐢𝐧𝐠 𝐮𝐩 𝐚𝐧𝐝 𝐢𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐭𝐡𝐞 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬. 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥𝐥𝐲, 𝐝𝐫𝐮𝐠 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐭𝐚𝐤𝐞𝐬 𝐲𝐞𝐚𝐫𝐬, 𝐛𝐮𝐭 𝐀𝐈 𝐜𝐚𝐧 𝐡𝐞𝐥𝐩 𝐛𝐲: ✔ 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: AI rapidly analyzes vast datasets, identifying potential drug candidates from genomic, chemical, and biological data. ✔ 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐞 𝐃𝐞𝐬𝐢𝐠𝐧: AI algorithms can design new molecules by predicting which structures are most likely to interact effectively with disease targets. ✔ 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐬: AI predicts drug efficacy and safety by analyzing past data, helping to reduce failed trials and side effects. ✔ 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐓𝐫𝐢𝐚𝐥𝐬: AI streamlines clinical trials by identifying suitable patient groups, predicting outcomes, and monitoring progress. 𝐓𝐡𝐞 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐫𝐞𝐩𝐨𝐫𝐭 𝐢𝐧𝐜𝐥𝐮𝐝𝐞 NVIDIA Exscientia Google BenevolentAI Recursion Insilico Medicine Schrödinger. Microsoft Atomwise Illumina NuMedii XtalPi Inc. iktos Arterys (Acquired by Tempus Labs) Deep Genomics, Verge Genomics BenchSci insitro Valo Health BPGbio, Inc. Predictive Oncology Labcorp IQVIA IGE/Affinity Media (acq. Tencent Holdings Ltd.) Celsius Therapeutics CytoReason Owkin Cloud Pharmaceuticals, Inc. Evaxion Biotech A/S Standigm BioAge Labs Envisagenics ARIAD Pharmaceuticals, Inc. #aiindrugdiscovery #artificialintelligence #drugdiscovery #machinelearning #biotechnology #pharmaceuticals #healthtech #aiforhealth #medicalinnovation #lifesciences #digitalhealth

As you know, we are developing a Drug Discovery platform that enables the generation of de novo molecules. We are excited to announce that we will be launching the first version at the end of May/beginning of June. Why should you try it? 1. Nature-Based Interactions Our platform is based on natural interactions from existing protein-ligand structures. We trained our models using crystalline data (real examples) to ensure they reflect what truly happens in nature, rather than relying on artificially invented physical and chemical quantities like existing standard scoring functions. 2. Step-by-Step Generation Our platform allows for step-by-step generation (Positioning the selected starting fragment -> Generating the scaffold -> Generating the periphery) with the ability to select chemotypes of interest at each stage. This enables you to direct the generation process towards the most relevant chemotypes. 3. Superior Binding Site Search Our AI binding site search model, SiteRadar, has outperformed existing solutions in binding site prediction accuracy, providing you with the best possible search results. If you are interested, please contact me. #DrugDiscovery #AI #Biotech #Pharmaceuticals #Innovation #Healthcare #MolecularGeneration

𝐀 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐯𝐞 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐀𝐈 𝐢𝐧 𝐃𝐫𝐮𝐠 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 [𝐏𝐃𝐅] 𝐆𝐞𝐭 𝐚 𝐅𝐫𝐞𝐞 𝐒𝐚𝐦𝐩𝐥𝐞 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐏𝐃𝐅 𝐑𝐞𝐩𝐨𝐫𝐭: https://lnkd.in/dddNRqR2 The global AI in drug discovery market, valued at $0.9 billion in 2023, is projected to grow to $4.9 billion by 2028, with a CAGR of 40.2%. 𝐀𝐈 𝐢𝐬 𝐫𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐢𝐧𝐠 𝐝𝐫𝐮𝐠 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐛𝐲 𝐬𝐩𝐞𝐞𝐝𝐢𝐧𝐠 𝐮𝐩 𝐚𝐧𝐝 𝐢𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐭𝐡𝐞 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬. 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥𝐥𝐲, 𝐝𝐫𝐮𝐠 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐭𝐚𝐤𝐞𝐬 𝐲𝐞𝐚𝐫𝐬, 𝐛𝐮𝐭 𝐀𝐈 𝐜𝐚𝐧 𝐡𝐞𝐥𝐩 𝐛𝐲: ✔ 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: AI rapidly analyzes vast datasets, identifying potential drug candidates from genomic, chemical, and biological data. ✔ 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐞 𝐃𝐞𝐬𝐢𝐠𝐧: AI algorithms can design new molecules by predicting which structures are most likely to interact effectively with disease targets. ✔ 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐬: AI predicts drug efficacy and safety by analyzing past data, helping to reduce failed trials and side effects. ✔ 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐓𝐫𝐢𝐚𝐥𝐬: AI streamlines clinical trials by identifying suitable patient groups, predicting outcomes, and monitoring progress. 𝐓𝐡𝐞 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐫𝐞𝐩𝐨𝐫𝐭 𝐢𝐧𝐜𝐥𝐮𝐝𝐞 NVIDIA Exscientia Google BenevolentAI Recursion Insilico Medicine Schrödinger. Microsoft Atomwise Illumina NuMedii XtalPi Inc. iktos Arterys (Acquired by Tempus Labs) Deep Genomics, Verge Genomics BenchSci insitro Valo Health BPGbio, Inc. Predictive Oncology Labcorp IQVIA IGE/Affinity Media (acq. Tencent Holdings Ltd.) Celsius Therapeutics CytoReason Owkin Cloud Pharmaceuticals, Inc. Evaxion Biotech A/S Standigm BioAge Labs Envisagenics ARIAD Pharmaceuticals, Inc. #aiindrugdiscovery #artificialintelligence #drugdiscovery #machinelearning #biotechnology #pharmaceuticals #healthtech #aiforhealth #medicalinnovation #lifesciences #digitalhealth

𝐀 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐯𝐞 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐀𝐈 𝐢𝐧 𝐃𝐫𝐮𝐠 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 [𝐏𝐃𝐅] 𝐆𝐞𝐭 𝐚 𝐅𝐫𝐞𝐞 𝐒𝐚𝐦𝐩𝐥𝐞 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐏𝐃𝐅 𝐑𝐞𝐩𝐨𝐫𝐭: https://lnkd.in/dddNRqR2 The global AI in drug discovery market, valued at $0.9 billion in 2023, is projected to grow to $4.9 billion by 2028, with a CAGR of 40.2%. 𝐀𝐈 𝐢𝐬 𝐫𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐢𝐧𝐠 𝐝𝐫𝐮𝐠 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐛𝐲 𝐬𝐩𝐞𝐞𝐝𝐢𝐧𝐠 𝐮𝐩 𝐚𝐧𝐝 𝐢𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐭𝐡𝐞 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬. 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥𝐥𝐲, 𝐝𝐫𝐮𝐠 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐭𝐚𝐤𝐞𝐬 𝐲𝐞𝐚𝐫𝐬, 𝐛𝐮𝐭 𝐀𝐈 𝐜𝐚𝐧 𝐡𝐞𝐥𝐩 𝐛𝐲: ✔ 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: AI rapidly analyzes vast datasets, identifying potential drug candidates from genomic, chemical, and biological data. ✔ 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐞 𝐃𝐞𝐬𝐢𝐠𝐧: AI algorithms can design new molecules by predicting which structures are most likely to interact effectively with disease targets. ✔ 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐬: AI predicts drug efficacy and safety by analyzing past data, helping to reduce failed trials and side effects. ✔ 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐓𝐫𝐢𝐚𝐥𝐬: AI streamlines clinical trials by identifying suitable patient groups, predicting outcomes, and monitoring progress. 𝐓𝐡𝐞 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐫𝐞𝐩𝐨𝐫𝐭 𝐢𝐧𝐜𝐥𝐮𝐝𝐞 NVIDIA Exscientia Google BenevolentAI Recursion Insilico Medicine Schrödinger. Microsoft Atomwise Illumina NuMedii XtalPi Inc. iktos Arterys (Acquired by Tempus Labs) Deep Genomics, Verge Genomics BenchSci insitro Valo Health BPGbio, Inc. Predictive Oncology Labcorp IQVIA IGE/Affinity Media (acq. Tencent Holdings Ltd.) Celsius Therapeutics CytoReason Owkin Cloud Pharmaceuticals, Inc. Evaxion Biotech A/S Standigm BioAge Labs Envisagenics ARIAD Pharmaceuticals, Inc. #aiindrugdiscovery #artificialintelligence #drugdiscovery #machinelearning #biotechnology #pharmaceuticals #healthtech #aiforhealth #medicalinnovation #lifesciences #digitalhealth