I had an opportunity to host our FAS Kyle Nilson to talk about using knowledge bases and omics data sources in target identification and then further insilico validation. It ended up being a fascinating chat with a few attendees chiming in with questions. Check out this great new webinar: https://lnkd.in/dZGM8vRx #drugdiscovery #drugdevelopment #bioinformatics #datascience
Venkatesh Moktali, PhD’s Post
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I had such a great time participating in the keynote panel this week at #BiotechX in Basel, Switzerland thanks for capturing the notes Christian Hein #AI #drugdiscovery #QIAGEN
Scaling AI in Healthcare & Pharma | Senior Executive & Advisor | Board Member | Healthtech - Pharma | former VP Digital @ Novartis
#Hype vs #Hope of #AI in Drug Discovery, a fascinating panel this morning at #BiotechX in Basel, with participants from Big Pharma and HealthTech, including Bulent Kiziltan, PhD, Hebe Middlemiss, Daniel Kuhn, James Malone, Venkatesh Moktali, PhD, Ivan Griffin Some core insights: - The idea of that an AI is just going to spit out new NMEs is clearly more of a hype than a reality, but the potential mid term is still huge - There a lot of low hanging fruit for AI applications in individual steps in discovery that will have to be rapidly adopted if you want to break the current productivity plateau in research - You need to address both technology and operating culture at the same time, a technology first approach won’t work, you need to create trust with key stakeholders - Acceleration of drug discovery is just one aspect of the AI impact (an important success metric for AI adoption is for example be able to fail new candidates faster than before) but also making molecules substantially better and more effective #AIindiscovery #innovation
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Check this out!
✨ We're working with Neo4j to make improvements in life possible! By integrating Neo4j’s industry-leading graph capabilities with our Biomedical Knowledge Base (BKB), we're uncovering hidden patterns and relationships in biomedical data. See how we're advancing #DrugDiscovery and more ⬇️
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Hey Folks, Join me for Data Science Day just before the BiotechX EU conference on Oct 8, and let’s dive into how #knowledgegraphs and unified omics data are advancing #drugdiscovery and therapeutics! Come by, meet industry experts, share ideas, and enjoy some good conversations before we all get wrapped up in the conference buzz. ✨ Let’s have some fun before BiotechX officially begins! See you there! 😎👋 #DoYouAI #BiotechX #Networking
Join us for our first-ever Data Science Day in Basel! 🤩 Meet your peers and other industry experts in-person and explore how #knowledgegraphs and unified ‘omics data can advance #drugdiscovery and #therapeutics. Slots are limited; reserve yours now 👉 https://lnkd.in/gxA8Zmiv #DoYouAI
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Inviting all my biopharma colleagues to the QIAGEN User Group Meeting 2024 in Boston on September 24-25. This event brings together industry experts to discuss the latest in omics and bioinformatics. I’ll be presenting on the use of knowledge graphs for GenAI architectures, and how they’re driving new innovations in biopharma. The agenda includes: Day 1: Keynotes and presentations covering knowledge graphs, multi-omics, GenAI, and more. Great venue for networking and knowledge-sharing. Day 2: Hands-on workshops focusing on IPA, OmicSoft, and Biomedical Knowledge Bases (BKBs). Practical sessions designed to provide in-depth learning. Also, an opportunity to experience our knowledge graph in Apple Vision Pro—a unique and immersive way to engage with data. Space is limited, so if you’re interested, I recommend registering soon. Link to the event is in the comments. #Biopharma #GenAI #KnowledgeGraphs #UserGroupMeeting2024 #Networking #Innovation
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Hey Folks attending #BiotechXUSA2024 Check out the talk from my colleague Andreas Kraemer on his work building causal embeddings from the Biomedical KB-HD knowledge graph to power drug repurposing. The talk is tomorrow, details are in the image Some highlights in the talk: - Explore how biomedical knowledge graphs are transforming data into actionable insights! - Machine learning-based approach leveraging causal interactions to predict novel drug-disease relationships. - Learn how this method can help in constructing networks that highlight supporting evidence for these relationships. - Look at real-world examples of drug repurposing, showcasing the power of this approach in accelerating discoveries. #DrugRepurposing #MachineLearning #KnowledgeGraphs #BiomedicalInnovation #BiotechX2024
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