Najat Khan, PhD’s Post

🔬 𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐌𝐞𝐞𝐭𝐬 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐚𝐫 𝐁𝐢𝐨𝐥𝐨𝐠𝐲: 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐀𝐜𝐜𝐞𝐥 𝐃𝐍𝐀 As Boltz-1 accelerates molecular interaction predictions, Accel DNA stands at the forefront of applying advanced AI-driven solutions to drug discovery and molecular research challenges. Accel DNA leverages cutting-edge computational techniques to analyze genetic and molecular data with unparalleled speed and precision. 🌟 𝐖𝐡𝐲 𝐀𝐜𝐜𝐞𝐥 𝐃𝐍𝐀 𝐢𝐬 𝐘𝐨𝐮𝐫 𝐏𝐚𝐫𝐭𝐧𝐞𝐫 𝐢𝐧 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: ➡AI at Scale: Integrates with platforms like Boltz-1 to provide scalable molecular modeling and drug research solutions. ➡Data-Driven Discovery: Uses robust algorithms to decode complex genetic patterns, advancing personalized medicine. ➡Open Collaboration: Embraces open-source advancements to enhance collaboration and innovation globally. 🔬 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐓𝐨𝐠𝐞𝐭𝐡𝐞𝐫: Accel DNA’s tools are crafted to complement breakthroughs like Boltz-1, providing researchers with a comprehensive toolkit to accelerate insights in drug development and disease mechanisms. 📢 Join the revolution with Accel DNA—where AI meets molecular biology to transform the future of health and medicine! #AI #AccelDNA #MolecularBiology #DrugDiscovery #Innovation

A reminder that good science is a messy and unoptimized thing. This is a great read to understand the challenges in doing good science. Attempts to slap one-size-fits-all AI models to science is doomed to fail, even beyond the current LLM tendency to believably make things up at random. Optimization of the entire process of science, particularly biomedical science, will inevitably to optimizing and expanding the errors and gaps. Human-in-the-loop doesn't get you past this any more than a crossing guard can stop accidents across an entire city. We need to develop smaller, focused models and bring them into the human, process, institution loop first - then expand from there. #science #research #unoptimized Amicia D. Elliott, Ph.D. Yoram Vodovotz https://lnkd.in/eUkKTGeK

I thought about the AI-Powered Drug Discovery post for 4 seconds 🔥🧠 #AI-PoweredDrugDiscovery 🌐 Market (2020-2034) – (USD Million) 🚀💊 Ready to see how AI is revolutionizing drug discovery from early-stage research to clinical trials? Here are some key segments leading the growth: 1️⃣ Deep Learning-Based Drug Design 🤖 2️⃣ Computational Biology & Bioinformatics 🧬 3️⃣ Data-Driven Clinical Trial Management 🏥 4️⃣ Virtual Screening & Molecular Modeling 🔎 ✨ Want a FREE SAMPLE of our latest report? Contact us at: sales@globaldatarouteanalytics.com Stay ahead of the innovation curve—follow our page for more AI breakthroughs, market forecasts, and cutting-edge analytics in healthcare! #AIinHealthcare #DrugDiscovery #PharmaTech #Innovation #HealthcareAnalytics

🧬 We can now accurately model the building blocks of biological life. Just think about that. Have you heard about Google DeepMind’s AlphaFold 3 yet? You should probably know these 10 things about it: 🌐 Alphafold 3 can accurately predict the structures of all life's molecules 🔍 It models complex structures of proteins, DNA, RNA, and small molecules 🧬 Alphafold 3 can predict chemical modifications that regulate cellular functions 🤝 It demonstrates unprecedented accuracy in predicting molecular interactions 💊 Alphafold 3 has profound implications for drug discovery and design 🧪 It outperforms traditional physics-based tools for biomolecular structure prediction 🆓 The AlphaFold Server democratizes access to AI-powered biology for researchers worldwide 🚀 AlphaFold 3 accelerates scientific workflows and fosters innovation 🤝 Google DeepMind and Isomorphic Labs prioritize responsible innovation and address potential risks 🌍 Alphafold 3 holds the potential to catalyze transformative discoveries across multiple fields AlphaFold 3 really is a monumental leap forward in our quest to unravel the complexities of the biological world. It’s not just hype. https://lnkd.in/d7Cpwx4y

🚀 The PitchBook Emerging Tech Research team's SynBioBeta Recap for Q2 2024 is live! Dive into our comprehensive report covering the latest insights and trends from SynBioBeta 2024. 🌟 Key Takeaways: 💡 AI & Digital Biology: Dominating discussions with a focus on generative AI, protein folding, and drug discovery tools like AlphaFold 3. 💊  Biopharma: Strong sector driving funding through high-impact investments, larger exits like IPOs, and M&A despite fewer deals post-pandemic. 🔬 Industrial Biotech: Facing investment struggles due to low exit activity and profitability, highlighting the need for stronger data to de-risk science. Life Science Tools & Services: Selective funding for later-stage companies with scalable, profitable products. Innovations in DNA synthesis, proteomic characterization, and single-cell genomics are critical but capital-intensive. 🌱 Bioeconomy: Focus on scalable biomanufacturing solutions and advanced life science tools to reduce costs and improve efficiency in product/drug development. 🍄 Psychedelics: Growing attention on integrating synthetic biology with psychedelic therapies, navigating unique regulatory and scientific challenges. 📅 Longevity: Rising interest in cell and gene therapies for antiaging, a field with transformative potential but long timelines and shifting regulations. #SynBio #AI #DigitalBiology #Biopharma #IndustrialBiotech #LifeScienceTools #Bioeconomy #Psychedelics #Longevity #EmergingTech #Innovation #Research

A groundbreaking project that will give wings to research and development in world medicine in the near future. Summary This paper, "How to Build the Virtual Cell with Artificial Intelligence - Priorities and Opportunities," outlines a vision for creating an AI-powered virtual cell (AIVC), a complex model that simulates the intricate workings of a real biological cell. The authors propose a multi-layered AIVC, with each layer capturing the behaviour of cells at different scales: molecular, cellular and multicellular. The AIVC would integrate information from a wide range of biological data, including genetic sequences, single-cell RNA sequencing, and high-resolution imaging, and use this information to learn patterns and predict cell behaviour under varying conditions. The authors envision this model enabling breakthroughs in a range of fields, such as drug discovery, personalized medicine, and synthetic biology. The paper delves into the key challenges and opportunities associated with building the AIVC, highlighting the need for extensive data sets, robust model evaluation, and a collaborative, open science approach. The paper concludes by emphasizing the immense potential of the AIVC to transform biological research and advance our understanding of the fundamental unit of life, the cell. https://lnkd.in/gbgj2nP6

𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐢𝐧 𝐁𝐢𝐨𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐜𝐬 𝐌𝐚𝐫𝐤𝐞𝐭: 𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐢𝐧𝐠 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 Artificial Intelligence in Bioinformatics is transforming healthcare by enabling the rapid analysis of large and complex biological datasets. AI-driven tools are being used to decode genetic information, identify disease markers, and predict potential therapeutic responses. This revolution is enhancing the precision of diagnostics and personalized medicine, leading to more effective treatments tailored to individual patients. The integration of AI in bioinformatics also accelerates drug discovery and development, reducing time and costs by predicting drug interactions, toxicity, and efficacy more accurately. By streamlining research processes, AI is reshaping the healthcare landscape, making breakthroughs in genomics, proteomics, and other fields more accessible. 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗮 𝗙𝗿𝗲𝗲 𝗦𝗮𝗺𝗽𝗹𝗲 𝗥𝗲𝗽𝗼𝗿𝘁:- https://lnkd.in/guu5SGn6 𝗧𝗼𝗽 𝗞𝗲𝘆 𝗣𝗹𝗮𝘆𝗲𝗿𝘀:- Fios Genomics, Source BioScience, Eurofins Scientific Finland Oy, Thermo Fisher Scientific, Insilico Medicine, Paige - AI Website Builder, SomaLogic, SOPHiA GENETICS, QIAGEN #artificialintelligence #bioinformatics #healthcareinnovation #drugdiscovery #genomics #precisionmedicine #aiinhealthcare #biotech #personalizedmedicine #medicaltechnology

🌟 Breaking New Ground in Protein Prediction with AlphaFold3's Open Source Release! 🌟 Exciting news for the scientific community! ,Google DeepMind has officially open-sourced AlphaFold3, their advanced AI protein-structure prediction tool. Announced on November 11, 2024, this release means that researchers worldwide can now download and run the code for non-commercial use, unlocking new possibilities for understanding protein interactions, including those crucial in drug discovery. 🔬 Why This Matters: AlphaFold3’s ability to model proteins alongside other molecules could be a game-changer for drug discovery and molecular biology. For the first time, academic scientists can use the tool to predict how proteins behave with various potential drugs – a feature previously limited to server-based predictions. 📈 Industry Impact: In an era where AI is reshaping drug discovery, DeepMind’s move promotes transparency and reproducibility, which are critical for academic research. It also fuels a competitive edge in the open-source community, inspiring other companies to innovate and release their own versions. Models like OpenFold3, set to release by the year’s end, will offer even more openness and adaptability for industry applications. 💡 Looking Ahead: The potential for AlphaFold3 to drive new discoveries is immense. From novel protein designs to understanding complex biological processes, this tool empowers scientists to take the next big leap in molecular biology and drug discovery. Here's to the innovations and discoveries that will follow this open-source breakthrough! 🚀 #AlphaFold3 #DeepMind #AI #DrugDiscovery #OpenSource #ProteinStructure #Bioinformatics #MolecularBiology #AIResearch

Multimodal Foundational Models: A New Era in Biology? Foundational models have become a cornerstone in machine learning, where large models are trained on vast datasets in a self-supervised manner. These models can perform a variety of new tasks without specific training for each task. While this concept is well-known in image and text processing, it is now gaining traction in biology. Traditionally, biological models have focused on a single type of data, such as protein sequences for structure prediction. However, recent developments have introduced multimodal models that integrate various data types, leading to groundbreaking advancements. Here are some notable examples: - AlphaFold 3: This model not only predicts protein structures but also bio-molecular interactions and complexes. It can incorporate ligands, nucleic acids, and other inputs in addition to protein sequences. - LLaVA-med: By processing both images and text, LLaVA-med provides more accurate answers for medical imaging questions, leveraging the power of multimodality. - scGPT: This model classifies cell types and predicts the effects of gene perturbations. It takes gene expression data and cellular conditions as inputs, enhancing its predictive capabilities. These emerging trends suggest that we may be heading towards robust multimodal models capable of modeling entire biological systems. Imagine models that can integrate images, text, and protein sequences to make superhuman predictions from diverse inputs. This direction raises several intriguing questions: - How will these models be trained and fine-tuned? - Can they achieve a comprehensive understanding of biological processes? - What new insights and applications will they unlock? I look forward to seeing how these advancements unfold and their impact on the future of biological research and healthcare. We are dicussing our efforts to organize the world of foundational models in bilogy in an upcomming webinar - you are welcome to join: https://lnkd.in/dgXARbRm 👉 What do you think? Let’s discuss!

Data scientist, June Guo transitioned from developing AI for self-driving cars to creating an AI/ML analytical platform for drug discovery at Athos Therapeutics Inc.   This platform, includes tools for transcriptomics, proteomics, and deep machine learning, aims to uncover genetic patterns in autoimmune and chronic inflammatory diseases.   Despite the complexity of the biological data, the AI platform supports precision medicine by enabling automated yet controlled data processing, making sure that human experts validate the findings. This is enhancing the development of personalised treatments while safeguarding patient data. #drugdiscovery #ai #machinelearning #healthtech #precisionmedicine #biotech #datascience #innovation #computationalbiology #deeplearning