Medvolt

Congratulation Medvolt ✨🙌

🚀 Big News! Medvolt Joins NVIDIA Inception! 🎉 We’re thrilled to announce that Medvolt has been selected for the prestigious NVIDIA Inception Program! This marks a major milestone in our journey to revolutionize AI-driven drug discovery. 💡💊 With NVIDIA’s support, we will supercharge 𝐌𝐞𝐝𝐆𝐫𝐚𝐩𝐡, our AI-powered in silico platform, leveraging high-performance computing, generative AI, and physics-based simulations to accelerate drug discovery like never before. This partnership enables us to scale faster, optimize deep learning models, and push the boundaries of AI in biotech. A huge thank you to NVIDIA for recognizing our work in transforming pharmaceutical R&D! Exciting times ahead as we continue to cut drug discovery timelines, reduce costs, and make a real impact in global healthcare. 🚀 Please get in touch with us for the collaborations and demo: [email protected] #Medvolt #NVIDIAInception #AIforDrugDiscovery #GenerativeAI #BiotechInnovation

Microsoft’s BioEmu model is redefining protein dynamics with unprecedented efficiency, and it’s open-sourced now. Simulating protein motion is crucial for drug discovery, enzyme engineering, and biomaterials. But traditional molecular dynamics (MD) simulations are computationally expensive and impractical for large-scale studies. BioEmu, a generative deep learning model, 𝗿𝗲𝗰𝗿𝗲𝗮𝘁𝗲𝘀 𝗽𝗿𝗼𝘁𝗲𝗶𝗻 𝗲𝗾𝘂𝗶𝗹𝗶𝗯𝗿𝗶𝘂𝗺 𝗲𝗻𝘀𝗲𝗺𝗯𝗹𝗲𝘀 𝗼𝗿𝗱𝗲𝗿𝘀 𝗼𝗳 𝗺𝗮𝗴𝗻𝗶𝘁𝘂𝗱𝗲 𝗳𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗠𝗗, while preserving critical structural and thermodynamic properties. 1) Captures biologically relevant domain motions, cryptic pocket formations, and local unfolding transitions. 2) Matches experimental protein stabilities and free energy landscapes with errors below 1 kcal/mol, rivaling high-precision MD simulations. 3) Enables realistic sampling of protein conformational states, offering a cost-effective alternative to long-timescale MD and cryo-EM experiments. Most impressively, 𝗕𝗶𝗼𝗘𝗺𝘂 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝘀 𝘁𝗵𝗼𝘂𝘀𝗮𝗻𝗱𝘀 𝗼𝗳 𝗲𝗾𝘂𝗶𝗹𝗶𝗯𝗿𝗶𝘂𝗺 𝗽𝗿𝗼𝘁𝗲𝗶𝗻 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 𝗽𝗲𝗿 𝗵𝗼𝘂𝗿 𝗼𝗻 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗚𝗣𝗨. This is another major step in replacing expensive simulations with efficient machine-learning emulators, a trend already seen in quantum chemistry and ML-based force fields. The introduction of property-prediction fine-tuning (PPFT) and partial backpropagation further optimizes training efficiency, marking a shift toward compute-efficient AI-driven research. Exciting to see AI revolutionizing structural biology at this scale. 🔗 Check out the open-source repository here: https://lnkd.in/gjNTrdiK 📜 Check out the paper: https://lnkd.in/giTSYMUa Follow Medvolt for more!! #AI #DrugDiscovery #ComputationalBiology #DeepLearning #Bioinformatics

𝗔𝗜’𝘀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝘃𝗲 𝗥𝗼𝗹𝗲 𝗶𝗻 𝗟𝗶𝗳𝗲 𝗦𝗰𝗶𝗲𝗻𝗰𝗲𝘀: 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗘𝗬-𝗣𝗮𝗻𝘁𝗵𝗲𝗼𝗻 & 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗥𝗲𝗽𝗼𝗿𝘁 Artificial intelligence (AI) is reshaping the life sciences sector, driving innovation across drug discovery, clinical trials, precision medicine, and manufacturing. According to the latest EY-Pantheon & Microsoft report released at BioAsia 2025, AI-driven growth in the industry is projected to reach: 📌 $16.5B in pharmaceuticals by 2034 📌 $97B in medical devices by 2028 𝗗𝗲𝘀𝗽𝗶𝘁𝗲 𝘁𝗵𝗶𝘀 𝗺𝗼𝗺𝗲𝗻𝘁𝘂𝗺, 𝗔𝗜 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗶𝗻 𝗹𝗶𝗳𝗲 𝘀𝗰𝗶𝗲𝗻𝗰𝗲𝘀 𝗳𝗮𝗰𝗲𝘀 𝘁𝗵𝗿𝗲𝗲 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: 🔹 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗖𝗼𝗻𝗰𝗲𝗿𝗻𝘀 – Bias in AI models could lead to disparities in treatment recommendations, impacting the goal of truly personalized medicine 🔹 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗕𝗮𝗿𝗿𝗶𝗲𝗿𝘀 – Complex regulatory landscapes, data privacy, and security concerns require strategic compliance frameworks 🔹 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗛𝘂𝗿𝗱𝗹𝗲𝘀 – A shortage of AI-skilled professionals and resistance to digital transformation pose challenges to enterprise-wide AI deployment 👉 Five Strategic Pillars for AI Integration To unlock AI’s full potential, the report highlights a structured roadmap: 1️⃣ AI-Driven Business Models – Embedding AI across decision-making processes. 2️⃣ Technology Infrastructure – Scalable AI solutions to support large-scale innovation. 3️⃣ Data Governance & Security – Ensuring AI models are built on high-quality, compliant data. 4️⃣ Workforce Upskilling – Addressing change management and fostering interdisciplinary expertise. 5️⃣ Regulatory & Compliance Frameworks – Strengthening AI governance, transparency, and cybersecurity. 👉 The Future of AI in Life Sciences AI is not just about automation—it is redefining efficiency, speed, and accuracy across the entire pharmaceutical value chain. From optimizing clinical trials to accelerating regulatory approvals, AI is enabling faster, more precise decision-making. As Suresh Subramanian of EY-Parthenon notes, organizations that invest in AI maturity today will emerge as tomorrow’s industry leaders. The challenge is no longer about AI’s potential, but rather about execution at scale. How are pharma and medtech companies positioning themselves to leverage AI for sustained impact? Report Link: https://lnkd.in/d3pvgQYH At Medvolt, we harness the power of generative AI, alongside other large language models (LLMs) and deep learning technologies, through our innovative platform 𝐌𝐞𝐝𝐆𝐫𝐚𝐩𝐡. 𝐅𝐞𝐞𝐥 𝐟𝐫𝐞𝐞 𝐭𝐨 𝐜𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬 𝐢𝐟 𝐲𝐨𝐮 𝐡𝐚𝐯𝐞 𝐚𝐧𝐲 𝐢𝐧𝐪𝐮𝐢𝐫𝐢𝐞𝐬 𝐨𝐫 𝐫𝐞𝐪𝐮𝐢𝐫𝐞 𝐚 𝐝𝐞𝐦𝐨𝐧𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧. Visit our website: https://www.medvolt.ai or reach out to us via email: [email protected] #AI #Pharma #DrugDiscovery #LifeSciences #PrecisionMedicine #Biotech #DigitalTransformation

AI-Powered Bioengineering Enters a New Era with Evo 2 For decades, synthetic biologists have sought to design biological systems with the precision of engineering, yet nature has often proved too complex to model. Now, AI is changing the game. Arc Institute and NVIDIA have introduced Evo 2, one of the largest open-source AI models for biology, trained on 128,000 genomes across all domains of life. With 40 billion parameters and an extended context window, Evo 2 is pushing bioengineering forward in ways previously unimaginable. 🔬 𝗞𝗲𝘆 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀: ✅ Predicts pathogenic mutations within seconds ✅ Generates new DNA sequences, from small bacterial genomes to yeast chromosomes ✅ Identifies long-range genetic interactions—a key step toward decoding eukaryotic genome regulation ✅ Models chromatin accessibility, crucial for precise gene expression control in therapies 📌 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: 🔹 Evo 2 is not just a pattern-matching tool—it has learned fundamental biological concepts, such as protein structures, gene regulation, and viral DNA signatures. 🔹 It enables scientists to design and test new DNA sequences with high accuracy—potentially transforming synthetic biology and precision medicine. 🔹 AI-driven genome design could accelerate breakthroughs in genetic therapies, biofuels, and biomanufacturing. This milestone parallels AlphaFold's impact on structural biology—reshaping how we understand and design life. The future of AI-powered genetic engineering is here, and it's open for everyone to explore. Press Release: https://lnkd.in/dyxQqn5V At Medvolt, we harness the power of generative AI, alongside other large language models (LLMs) and deep learning technologies, through our innovative platform 𝐌𝐞𝐝𝐆𝐫𝐚𝐩𝐡. 𝐅𝐞𝐞𝐥 𝐟𝐫𝐞𝐞 𝐭𝐨 𝐜𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬 𝐢𝐟 𝐲𝐨𝐮 𝐡𝐚𝐯𝐞 𝐚𝐧𝐲 𝐢𝐧𝐪𝐮𝐢𝐫𝐢𝐞𝐬 𝐨𝐫 𝐫𝐞𝐪𝐮𝐢𝐫𝐞 𝐚 𝐝𝐞𝐦𝐨𝐧𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧. Visit our website: https://www.medvolt.ai or reach out to us via email: [email protected] #AI #SyntheticBiology #DrugDiscovery #Genomics #Bioengineering

𝗡𝗲𝘂𝗿𝗼𝗳𝗶𝗹𝗮𝗺𝗲𝗻𝘁 𝗟𝗶𝗴𝗵𝘁 (𝗡𝗳𝗟): 𝗔 𝗚𝗮𝗺𝗲-𝗖𝗵𝗮𝗻𝗴𝗲𝗿 𝗶𝗻 𝗖𝗡𝗦 𝗗𝗿𝘂𝗴 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝗮𝗻𝗱 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗕𝗶𝗼𝗺𝗮𝗿𝗸𝗲𝗿𝘀 Central nervous system (CNS) diseases remain a major global health challenge, with high drug failure rates despite advances in disease understanding. One critical gap? There is a lack of reliable biomarkers for monitoring neurodegeneration and treatment response. Enter Neurofilament Light (NfL). A structural protein found in neurons, NfL is emerging as a sensitive, translational biomarker for CNS drug discovery and clinical monitoring. 𝗪𝗵𝘆 𝗡𝗳𝗟 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 🔹 𝗜𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿 𝗼𝗳 𝗔𝘅𝗼𝗻𝗮𝗹 𝗗𝗮𝗺𝗮𝗴𝗲: NfL is released when neurons are injured, making it a key marker for neurodegenerative diseases, traumatic brain injury, and inflammation. 🔹 𝗕𝗿𝗶𝗱𝗴𝗲𝘀 𝗣𝗿𝗲𝗰𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵: Used in in vitro, animal models, and human trials, NfL provides consistent and scalable insights into disease progression. 🔹 𝗡𝗼𝗻-𝗜𝗻𝘃𝗮𝘀𝗶𝘃𝗲 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: Advances in ultrasensitive plasma assays allow NfL to serve as a surrogate for cerebrospinal fluid (CSF) analysis—bringing greater accessibility to CNS diagnostics. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗗𝗶𝘀𝗲𝗮𝘀𝗲 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 🔹 𝗔𝗹𝘇𝗵𝗲𝗶𝗺𝗲𝗿’𝘀 & 𝗗𝗲𝗺𝗲𝗻𝘁𝗶𝗮: Plasma NfL levels correlate with disease severity and can enhance early diagnosis when combined with amyloid-beta biomarkers. 🔹𝗦𝗽𝗶𝗻𝗮𝗹 𝗠𝘂𝘀𝗰𝘂𝗹𝗮𝗿 𝗔𝘁𝗿𝗼𝗽𝗵𝘆 (𝗦𝗠𝗔) & 𝗔𝗟𝗦: NfL is used to track treatment response, as seen in nusinersen for SMA and tofersen for ALS. The FDA even approved tofersen based on biomarker changes despite unmet clinical endpoints. 🔹𝗗𝗿𝘂𝗴-𝗜𝗻𝗱𝘂𝗰𝗲𝗱 𝗡𝗲𝘂𝗿𝗼𝘁𝗼𝘅𝗶𝗰𝗶𝘁𝘆: NfL serves as a safety biomarker, detecting chemotherapy-induced peripheral neuropathy and toxicities in clinical trials. 𝗧𝗵𝗲 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗔𝗜 𝗶𝗻 𝗕𝗶𝗼𝗺𝗮𝗿𝗸𝗲𝗿 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 🔍 AI-powered analytics are revolutionizing biomarker selection. Advanced machine learning models now analyze massive proteomic and genomic datasets to refine biomarker selection and predict disease progression. 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗡𝗳𝗟 𝗶𝗻 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗠𝗲𝗱𝗶𝗰𝗶𝗻𝗲 📈 With growing adoption in clinical trials and drug development, NfL is setting the stage for precision neurology—allowing researchers to stratify patients, optimize treatment pathways, and accelerate CNS drug approvals. 🧵 How do you see NfL transforming CNS drug discovery? Let’s discuss. 👇 #Neuroscience #Biomarkers #Neurodegeneration #DrugDiscovery #PrecisionMedicine #ArtificialIntelligence

𝗡𝗲𝘄 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗶𝗻𝘁𝗼 𝗖𝗿𝗼𝗵𝗻’𝘀 𝗗𝗶𝘀𝗲𝗮𝘀𝗲: 𝗛𝗼𝘄 𝗚𝘂𝘁 𝗕𝗮𝗰𝘁𝗲𝗿𝗶𝗮 𝗗𝗿𝗶𝘃𝗲 𝗙𝗶𝗯𝗿𝗼𝘀𝗶𝘀 Scarring in the digestive tract is a major complication of Crohn’s disease (CD), often leading to intestinal narrowing and emergency surgeries. A new study by researchers at the University of North Carolina at Chapel Hill has identified a key bacterial molecule that may trigger this fibrosis—offering a potential treatment target. 🔬 𝗧𝗵𝗲 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗚𝘂𝘁 𝗕𝗮𝗰𝘁𝗲𝗿𝗶𝗮 𝗶𝗻 𝗖𝗗 The study focused on a specific strain of E. coli, known as adherent-invasive E. coli (AIEC), which is commonly found in CD patients. Researchers discovered that these bacteria produce yersiniabactin, a molecule previously known for iron sequestration but now implicated in immune activation and fibrosis. 🧪 𝗞𝗲𝘆 𝗙𝗶𝗻𝗱𝗶𝗻𝗴𝘀: ✅ Yersiniabactin induces fibrosis: Patients with CD showed higher levels of yersiniabactin-expressing bacteria compared to healthy individuals. ✅ Macrophage Activation: These bacteria recruit immune cells, particularly macrophages, which stimulate fibroblast activity—leading to excess scar tissue. ✅ HIF-1α Pathway Disruption: Yersiniabactin steals zinc from macrophages, causing dysregulation of the HIF-1α pathway—a key driver of inflammation and fibrosis. ✅ Direct Link to Human Disease: Colon tissue samples from CD patients also showed elevated HIF-1α expression, confirming its role in disease progression. 🚀 𝗪𝗵𝗮𝘁 𝗧𝗵𝗶𝘀 𝗠𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 𝗧𝗿𝗲𝗮𝘁𝗺𝗲𝗻𝘁 This study shifts the focus from treating CD symptoms to addressing the root cause of inflammation. By targeting yersiniabactin-producing bacteria or the HIF-1α signaling pathway, researchers could develop new therapies that prevent fibrosis before it becomes severe. 💡 𝗪𝗵𝗮𝘁’𝘀 𝗡𝗲𝘅𝘁? 🔹 Researchers are developing HIF-1α-deficient mouse models to validate these findings. 🔹 Spatial transcriptomics will help map how yersiniabactin influences fibrosis at the cellular level. 🔹 Future work may explore anti-bacterial strategies to reduce yersiniabactin production and modulate immune activation. This research marks a critical step toward understanding and potentially preventing fibrosis in Crohn’s disease. 🔗 Let’s discuss: Could targeting bacterial metabolites be a game-changer for IBD treatment? 📜 Paper: https://lnkd.in/gQKMX-Vw At Medvolt, we harness the power of generative AI, alongside other large language models (LLMs) and deep learning technologies, through our innovative platform 𝐌𝐞𝐝𝐆𝐫𝐚𝐩𝐡. 𝐅𝐞𝐞𝐥 𝐟𝐫𝐞𝐞 𝐭𝐨 𝐜𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬 𝐢𝐟 𝐲𝐨𝐮 𝐡𝐚𝐯𝐞 𝐚𝐧𝐲 𝐢𝐧𝐪𝐮𝐢𝐫𝐢𝐞𝐬 𝐨𝐫 𝐫𝐞𝐪𝐮𝐢𝐫𝐞 𝐚 𝐝𝐞𝐦𝐨𝐧𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧. Visit our website: https://www.medvolt.ai or reach out to us via email: [email protected] #IBD #CrohnsDisease #Microbiome #Inflammation #MedicalResearch #Biotech

🧬 Exploring the Link Between Uremic Toxins, Insulin Resistance, and Chronic Kidney Disease (CKD) A recent study published in Scientific Reports uncovers how the gut microbiota-derived uremic toxin, phenyl sulfate (PS), impacts glucose metabolism, particularly in patients with CKD and diabetic kidney disease (DKD). This research highlights the intricate relationship between PS accumulation, insulin secretion, and insulin resistance. 🔍 𝗞𝗲𝘆 𝗙𝗶𝗻𝗱𝗶𝗻𝗴𝘀: 1. Increased Insulin Secretion: PS was found to stimulate insulin secretion by enhancing glucose-stimulated insulin secretion (GSIS) from pancreatic β-cells. This process is mediated through the Ddah2 enzyme, which plays a critical role in insulin regulation. 2. Insulin Resistance in CKD/DKD: In diabetic mice models, PS induced insulin resistance by altering lncRNA expression and activating Erk phosphorylation in adipocytes. This insulin resistance is more prominent in CKD patients with eGFR < 60 mL/min/1.73m². 3. Clinical Insights: Analysis of 462 patients showed a negative correlation between PS and HbA1c levels, along with increased insulin resistance, as reflected by elevated urinary C-peptide/creatinine ratio (UCPCR). 4. Gut Microbiota Connection: PS is a product of gut microbiota metabolism, suggesting that gut microbiome modulation through probiotics or inhibitors of tyrosine phenol-lyase (TPL) could serve as potential interventions for managing insulin resistance in CKD/DKD. 5. Mitochondrial Impact: PS impaired mitochondrial function, further complicating metabolic regulation and contributing to insulin secretion abnormalities. 💡 𝗪𝗵𝗮𝘁 𝗗𝗼𝗲𝘀 𝗧𝗵𝗶𝘀 𝗠𝗲𝗮𝗻? This study provides new insights into how PS alters glucose metabolism, paving the way for targeted therapies in metabolic disorders associated with kidney disease. Regulating gut microbiota and inhibiting PS production may offer a novel strategy for better glycemic control in affected patients. At Medvolt, we harness the power of generative AI, alongside other large language models (LLMs) and deep learning technologies, through our innovative platform 𝐌𝐞𝐝𝐆𝐫𝐚𝐩𝐡. 𝐅𝐞𝐞𝐥 𝐟𝐫𝐞𝐞 𝐭𝐨 𝐜𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬 𝐢𝐟 𝐲𝐨𝐮 𝐡𝐚𝐯𝐞 𝐚𝐧𝐲 𝐢𝐧𝐪𝐮𝐢𝐫𝐢𝐞𝐬 𝐨𝐫 𝐫𝐞𝐪𝐮𝐢𝐫𝐞 𝐚 𝐝𝐞𝐦𝐨𝐧𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧. Visit our website: https://www.medvolt.ai or reach out to us via email: [email protected] #CKD #DKD #MetabolicHealth #InsulinResistance #GutMicrobiome #MedicalResearch #ScientificReports #AIInHealthcare

How AI is Transforming Drug Discovery & Drug–Target Interactions (DTIs)! 💊 Drug discovery is complex, costly, and time-intensive, especially when it comes to identifying drug–target interactions (DTIs). A recent study by Yuxin Yang and Feixiong Cheng (2025) explores how artificial intelligence (AI) is transforming this process by boosting prediction accuracy, reducing costs, and accelerating discovery timelines. 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: 🔍 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹𝘀 𝗶𝗻 𝗗𝗧𝗜 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻: 1. Classical machine learning models like Support Vector Machines (SVMs) and Random Forests (RFs) laid the groundwork. 2. Deep learning techniques, including Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs), now dominate for their ability to process vast datasets. 📊 𝗔𝗜 𝗧𝗼𝗼𝗹𝘀 & 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: 1. DrugBank and ChEMBL offer comprehensive datasets for drugs, proteins, and interactions. 2. Tools like RDKit, Open Babel, and AlphaFold2 help compute molecular features critical for AI models. 🔬 𝗔𝗜 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝗔𝗽𝗽𝗹𝗶𝗲𝗱: 1. Feature Vector Models: Use chemical fingerprints to identify potential interactions. 2. Sequence-Based Models: Apply NLP-inspired techniques using SMILES strings for drugs and amino acid sequences for proteins. 3. Network-Based Models: Analyze protein–protein interactions and drug–target networks to find novel relationships. 💡 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: 1. Alzheimer's Disease: AI models have pinpointed novel genes and potential therapeutics. 2. Cardiovascular Disease: Network-based approaches have identified existing drugs with new therapeutic applications. 3. COVID-19: AI-driven methods helped repurpose drugs during the pandemic, accelerating research timelines. 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 & 𝗙𝘂𝘁𝘂𝗿𝗲 𝗗𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻𝘀: 1. Data quality remains a hurdle, with noise and incomplete annotations affecting model performance. 2. The shift to multimodal models that integrate diverse data types—like sequences, images, and structures—is expected to improve predictive accuracy. 3. Quantum computing holds promise for even more efficient drug discovery, though it remains in early stages. As AI methods become more refined and data integration improves, the landscape of drug discovery will continue to evolve, potentially revolutionizing how we develop treatments for complex diseases. 📜 Paper: https://lnkd.in/dTSPAyGb At Medvolt, we harness the power of generative AI, alongside other large language models (LLMs) and deep learning technologies, through our innovative platform 𝐌𝐞𝐝𝐆𝐫𝐚𝐩𝐡. 𝐅𝐞𝐞𝐥 𝐟𝐫𝐞𝐞 𝐭𝐨 𝐜𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬 𝐢𝐟 𝐲𝐨𝐮 𝐡𝐚𝐯𝐞 𝐚𝐧𝐲 𝐢𝐧𝐪𝐮𝐢𝐫𝐢𝐞𝐬 𝐨𝐫 𝐫𝐞𝐪𝐮𝐢𝐫𝐞 𝐚 𝐝𝐞𝐦𝐨𝐧𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧. Visit our website: https://www.medvolt.ai or reach out to us via email: [email protected] #DrugDiscovery #AIinHealthcare #MachineLearning #PharmaInnovation #Biotech #AI #ComputationalBiology #BigData

𝗡𝗲𝘄 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗶𝗻𝘁𝗼 𝗞𝗶𝗱𝗻𝗲𝘆 𝗙𝗶𝗯𝗿𝗼𝘀𝗶𝘀: 𝗧𝗵𝗲 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗔𝗖𝗢𝗧12 𝗶𝗻 𝗟𝗶𝗽𝗶𝗱 𝗠𝗲𝘁𝗮𝗯𝗼𝗹𝗶𝘀𝗺 A recent study highlights the critical role of ACOT12 in kidney fibrosis, shedding light on how lipid metabolism influences disease progression. The research provides a new perspective on fibrosis regulation and potential therapeutic targets. 𝗞𝗲𝘆 𝗙𝗶𝗻𝗱𝗶𝗻𝗴𝘀: 📌 ACOT12 Deficiency and Fibrosis Progression - Lower ACOT12 levels were observed in chronic kidney disease (CKD) patients and fibrotic mouse models, indicating its role in disease advancement. - ACOT12 deficiency leads to abnormal lipid accumulation, which exacerbates fibrosis. 📌 Mechanistic Insights: ACOT12 Functions Independently of PPARα - Unlike previous assumptions, ACOT12's impact on fibrosis is not mediated through PPARα, suggesting an alternative regulatory pathway. - This distinction challenges conventional fibrosis treatment approaches focused on PPARα activation. 📌 Restoring ACOT12 as a Potential Therapeutic Strategy - Gene therapy experiments demonstrated that reintroducing ACOT12 significantly reduced lipid accumulation and fibrosis, highlighting its potential as a therapeutic target. - Research Implications These findings provide a new metabolic perspective on kidney fibrosis, emphasizing the importance of lipid regulation in disease progression. Targeting ACOT12 could lead to novel interventions for CKD, moving beyond existing treatment paradigms. 🔗 Read the full study: https://lnkd.in/g_w_Qvww At Medvolt, we harness the power of generative AI, alongside other large language models (LLMs) and deep learning technologies, through our innovative platform 𝐌𝐞𝐝𝐆𝐫𝐚𝐩𝐡. 𝐅𝐞𝐞𝐥 𝐟𝐫𝐞𝐞 𝐭𝐨 𝐜𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬 𝐢𝐟 𝐲𝐨𝐮 𝐡𝐚𝐯𝐞 𝐚𝐧𝐲 𝐢𝐧𝐪𝐮𝐢𝐫𝐢𝐞𝐬 𝐨𝐫 𝐫𝐞𝐪𝐮𝐢𝐫𝐞 𝐚 𝐝𝐞𝐦𝐨𝐧𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧. Visit our website: https://www.medvolt.ai or reach out to us via email: [email protected] #KidneyResearch #Fibrosis #LipidMetabolism #MedicalInnovation #ChronicKidneyDisease #BiomedicalResearch