Pharmacelera’s Post

🔬 Revolutionizing Pharma Labs with Computer Vision 🔬 At ThinkPhenom, we specialize in providing state-of-the-art Computer Vision applications designed specifically for the pharmaceutical industry. Our AI-powered solutions are transforming Pharma Labs by enhancing accuracy, efficiency, and productivity across all stages of research, production, and quality control. 💡 How Our Computer Vision Solutions Help Pharma Labs: ✅ Automated Quality Control: Detect defects and anomalies in real-time, ensuring high-quality production standards. ✅ Data-Driven Insights: Extract and analyze visual data to improve decision-making and optimize processes. ✅ Accelerated Research: Analyze complex data from experiments and research faster, enhancing the development of new drugs and treatments. ✅ Regulatory Compliance: Ensure compliance with industry standards by automating the documentation and monitoring of lab processes. With our advanced Computer Vision applications, pharma labs can achieve greater precision, reduce human error, and significantly improve operational efficiency. Let’s work together to take your lab's capabilities to the next level with cutting-edge technology! Visit: https://lnkd.in/eF6FDY8r #ComputerVision #AI #Pharma #PharmaceuticalIndustry #PharmaTech #LabInnovation #QualityControl #Automation #TechForPharma #AIinHealthcare #DataScience #DigitalTransformation #MachineLearning #Innovation #HealthTech #PharmaSolutions #ResearchAndDevelopment #SmartLabs

Generative AI: The Game-Changer for Life Sciences! 🚀 In 2024, the adoption of #GenerativeAI is set to revolutionize the life sciences sector. Over 90% of biopharma and medtech companies are exploring its potential to automate PMO tasks, enhance supply chain management, and support compliance and regulatory affairs. With 70% of biopharma prioritizing AI for research and discovery, the innovation landscape is expanding rapidly. At TechSphere Global, LLC, this means new opportunities to drive transformation and efficiency in the healthcare and biotech industries. Staying ahead in AI integration can position your organization as a leader in this dynamic field. Let's work together and embrace the future of AI in life sciences! 💡 #LifeSciences #Biotech #GenerativeAI #PMO #Innovation #ITManagementConsulting #HealthcareIT #TechSphereGlobalLLC

The pharmaceutical sector has witnessed a remarkable surge in data digitalisation in recent years, enabling ground-breaking advancements in drug discovering and development. However, this digital transformation introduces challenges in data acquisition, analysis, and application for solving complex clinical problems. We've written a new insight on 𝐀𝐈 𝐚𝐧𝐝 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐢𝐧 𝐃𝐫𝐮𝐠 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 & 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭. The integration of AI and machine learning in drug discovery is reshaping the requirements and priorities for lab design and construction. Modern laboratories must now accommodate advanced AI-driven workflows, including high-throughput data analysis, automated robotics, and integrated multiomic sequencing technologies. This demands infrastructure tailored to support cutting-edge computational tools, flexible layouts for evolving research needs, and robust data management systems. Read more on this report here: https://lnkd.in/eNwEweUE #AI #drugtesting #labfunctionality #futureofdesign #machinelearning

Given the market and the maturity of the technology, wet-lab enabled AI solutions are a no-brainer in today's market and will get you the Partners you are looking for. As a community we need to be able to prove that your models work and the scientific community (future partners, M&A activity) needs hard evidence to ensure commercial success. Vertical integration is a theme that I've observed digging through hundreds of businesses this week alongside several other learnings. As exciting as it is to see small and large molecule in silico design accelerate the hit identification process, we need to sample the space effectively and in a multifactorial way. There are not shortcuts yet, especially for more complex multi-disease states such as oncology. Hence, you need a wet-lab ASAP in your process. This also means ensuring one heck of a streamlined change management and integrated process for companies snapping up AI talent and not enabling them with wet-lab support. This feels like dejavu...anyone else? Also, why would you not enable AI teams with even more wet-lab support? The front-end of drug discovery is the low hanging fruit and where we can still double down on HTPS technologies that have served us for decades to help us continue to learn... If anything, it's surely worth doubling down but focus in on parallelisation like never before? (Does anyone see the downstream bottleneck coming?...) However, we're still struggling with enabling HTS and other biotech processes with technology that is dominant in adjacent industries. Take for example machine vision, high-throughput imaging, sensor technologies for in-line screening, high-speed communication tech. and monitoring, etc. The story gets worse when you look at where we are in some manufacturing facilities. How much are we not learning as a result? (A lot!) We must continue to converge the technologies of today to create the scientific and health future of tomorrow - the regulators want to see more too! AI-hardware-wetware-scientist = the future and a combination where we can help you leapfrog into at Cambridge Consultants. BTW, If you're in Europe, you need to move faster still! #Biotech #pharma #therapeutics #drugmanufacture #AI #labautomation

AI company spotlight on Luo Automation 📈     We had the pleasure of interviewing their founder and CEO, Joren van der Horst as part of our Disruptor Watchlist to learn more about their technology.     • Luo Automation offers a tailorable visual inspection solution based on a proprietary photographic methodology and machine learning model.   • This automated visual inspection technology minimises the risk of defective products entering the market, all the while ensuring that patients receive accurate and effective medicine.   • AI plays a key role in pharma through the potential of speeding up drug discovery and production, which ultimately will lead to increased quality and consistency.     In addition to Luo Automation, our data team has identified 380+ pre-clinical CROs, from computational chemistry to custom synthesis.    #AIinPharma #Pharma #AIDrugDiscovery #ComputationalChemistry  

Join us for an insightful webinar on "Decoding De Novo Design with Generative Methods," where we will explore cutting-edge applications of AI/ML to novel molecule design. This session will provide an overview of various de novo methods, including standard, scaffold-, reaction-, and structure-based approaches. We will delve into their applications across different workflow challenges such as hit identification, hit-to-lead (H2L), and lead optimization. Learn about the specific use cases for each de novo method and understand the unique benefits they offer in designing novel, drug-like, and synthetically viable compounds. This webinar is designed to equip medicinal chemists and drug discovery scientists with the knowledge to unlock new molecule design possibilities and accelerate their drug development journey. Seize this chance to deepen your understanding of generative AI-driven drug discovery and explore how Aiddison software can augment your research methodologies. Sponsored by Merck Group MilliporeSigma #webinar #drugdesign #Aiddison

🚨 Pharma R&D costs are climbing while returns shrink, threatening the sustainability of drug discovery. According to Deloitte, average R&D costs per asset have jumped by 76% over the past decade, while forecasted peak sales per asset dropped by 30% between 2013 and 2023. This trajectory is unsustainable. 💡 𝘛𝘦𝘤𝘩𝘉𝘪𝘰 𝘤𝘰𝘶𝘭𝘥 𝘣𝘦 𝘵𝘩𝘦 𝘢𝘯𝘴𝘸𝘦𝘳. TechBio can scale up experiments and eliminate candidates early, by leveraging: 1. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐬 𝐢𝐧 𝐛𝐢𝐨𝐥𝐨𝐠𝐲 – Moving from small molecules to biologics and cell therapies is transforming therapeutic possibilities. Increasingly complex disease models generate more accurate results. 2. 𝐏𝐫𝐨𝐥𝐢𝐟𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐥𝐚𝐛 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 – Enables high throughput to explore broader solution spaces. 3. 𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐧𝐞𝐬𝐬 𝐨𝐟 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 – With some models now surpassing human performance, ML is guiding smarter, faster decisions. The industry talks a lot about AI in labs. In this recent talk, I introduced a framework for talking about the different levels of lab intelligence: - 𝗟𝗲𝘃𝗲𝗹 𝟱: Artificial General Intelligence - 𝗟𝗲𝘃𝗲𝗹 𝟰: Autonomous - 𝗟𝗲𝘃𝗲𝗹 𝟯: ML-Guided - 𝗟𝗲𝘃𝗲𝗹 𝟮: Data-Centric - 𝗟𝗲𝘃𝗲𝗹 𝟭: Automated - 𝗟𝗲𝘃𝗲𝗹 𝟬: Manual 📽️ Interested in learning more? Comment below for a copy of my slides, or watch the 𝗗𝗔𝗬 𝟮 recording here: https://hubs.la/Q02Y6_vb0 #PharmaInnovation #TechBio #DrugDiscovery #MachineLearning #Automation #Biotech #FutureOfMedicine

Life sciences offers some of the most innovative and exciting AI use cases, from drug discovery to engineering new proteins and materials. However, as drug development requires comprehensive clinical trials to ensure accuracy and efficacy, life sciences companies will need to take a similarly cautious, responsible approach to #GenAI deployment. As Dr. Parvin Moyassari puts it, before organizations implement generative AI for research and discovery, they should start with use cases that are firmly within the 'safe zone' where inaccuracies would be minor with manageable consequences. These use cases can help build experience across an organization in a controlled manner, whilst still delivering value. Read here for some of the best use cases which build up experience and demonstrate value - https://lnkd.in/gaH8DpGS #ArtificialIntelligence #LifeSciences #Innovation

https://lnkd.in/da4gePHH "Artificial intelligence (AI) is transforming the landscape of drug discovery and medical equipment innovation, driving unprecedented advancements in healthcare. In drug discovery, AI is enabling faster and more accurate identification of potential drug candidates by analyzing vast datasets, predicting molecular interactions, and modeling complex biological processes. This reduces the time and cost traditionally associated with bringing new treatments to market, accelerating the development of life-saving medications. On the medical equipment front, AI is revolutionizing device design, manufacturing, and optimization. From AI-powered imaging tools that enhance diagnostics to predictive maintenance in medical machines, the technology is improving the precision, efficiency, and functionality of medical devices.  #PharmaceuticalResearch #InnovationInMedicine #FutureOfHealthcare

AI is accelerating formulation development, transforming how we approach one of the most critical stages in drug discovery and manufacturing. By leveraging machine learning and predictive modeling, researchers can now simulate and optimize formulations, reducing reliance on trial-and-error experimentation. This not only accelerates development timelines but also enhances the stability, bioavailability, and safety profiles of drugs. From predicting excipient interactions to assessing API stability, AI empowers scientists to make data-driven decisions that save time and resources. #ArtificialIntelligence #FormulationDevelopment #DrugDiscovery