Mosaic Biosciences’ Post

How will AI change the future of antibody discovery? The AIntibody Challenge is designed to answer this question and provide valuable insights into practical applications of AI to accelerate development.

➡️ Antibody design enters the AI era “Major biopharma companies are setting up partnerships with companies that are pioneering the application of artificial intelligence tools to develop therapeutic antibodies.”   ☑️ Traditional #therapeutic antibody discovery is time-consuming and often fails to produce antibodies with necessary features, relying on screening numerous candidates from approaches like immunizing animals or phage display.   ☑️ Advances in #AI are improving and speeding up the discovery process, with #biotech companies integrating machine learning and wet lab capabilities to optimize #antibody design and attract major #pharma partnerships.   ☑️ Recent deals include AstraZeneca and AbbVie partnering with Absci and BigHat Biosciences, respectively, for over $200 million each, and Absci's $610 million deal with Almirall, all aimed at leveraging AI for antibody development. Sanofi and BioMap's $1 billion collaboration focuses on AI models for broader biologic design. (⬇️refer to the reference article for more detail)   ☑️ AI's potential extends to designing new antibodies from scratch, highlighted by a 2024 report on de novo antibody design and the launch of Xaira Therapeutics with over $1 billion in funding, using generative AI tools like diffusion models, similar to #NGS.   ➕ Source: Biopharma Dealmakers Article🌐https://lnkd.in/dSHYPv5a (31 May 2024)   #pharmanews #marketresearch #artificialintelligence

🚀 𝗨𝗽𝗰𝗼𝗺𝗶𝗻𝗴 𝗪𝗲𝗯𝗶𝗻𝗮𝗿: 𝗔𝗜-𝗯𝗮𝘀𝗲𝗱 𝗔𝗻𝘁𝗶𝗯𝗼𝗱𝘆 𝗧𝗵𝗲𝗿𝗮𝗽𝗲𝘂𝘁𝗶𝗰𝘀 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗜𝘁𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Join us to explore how AI-based platforms are transforming the development of antibody therapeutics, ensuring they are effective and viable for large-scale production and clinical use. 👉Register here: https://ow.ly/9fS050RTIn2 What you will learn from the webinar 🔹Importance and challenges of developability in antibody therapeutics 🔹Role of AI in transforming developability assessment 🔹Technological solutions for antibody developability assessment and engineering #AntibodyTherapeutics #Biotech #Webinar #AntibodyDevelopment #MonoclonalAntibodies #mAb #PTM #AntibodyEngineering #AI

BioStrand's LENS AI in silico epitope binning technology is a game-changer for antibody discovery and drug development. IPA (ImmunoPrecise Antibodies) Here’s why it stands out: 1. High Throughput: LENS #AI can analyze over 5,000 antibody sequences in mere hours. This rapid processing speeds up the identification of potential therapeutic antibodies significantly. 2. Cost Efficiency: By eliminating the need for physical materials and wet lab experiments, LENS^{ai} drastically reduces associated costs. This includes savings on reagents, lab space, and manual labor. 3. Accuracy: The technology achieves near-perfect agreement with traditional wet lab methods, ensuring reliable and reproducible results without the need for extensive physical assays. How It Helps Drug Discovery: - Accelerated Research: Faster analysis translates to quicker identification of promising candidates, shortening the overall drug development timeline. - Enhanced Scalability: Researchers can handle large datasets efficiently, enabling more comprehensive studies and a higher throughput of antibody variants. - Reduced Expenses: By cutting down on the need for expensive wet lab resources and manual processing, LENS #AI makes drug discovery more affordable and accessible. Overall, integrating LENS #AI into the drug discovery pipeline enhances efficiency, accuracy, and cost-effectiveness, making it an invaluable tool for biotherapeutic research. For more details, visit BioStrand (a subsidiary of IPA) (https://lnkd.in/dTwdJF3T).

🚀 𝗨𝗽𝗰𝗼𝗺𝗶𝗻𝗴 #FREE 𝗪𝗲𝗯𝗶𝗻𝗮𝗿: 𝗔𝗜-𝗯𝗮𝘀𝗲𝗱 𝗔𝗻𝘁𝗶𝗯𝗼𝗱𝘆 𝗧𝗵𝗲𝗿𝗮𝗽𝗲𝘂𝘁𝗶𝗰𝘀 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗜𝘁𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Join us tomorrow to explore how AI-based platforms are transforming the development of antibody therapeutics, ensuring they are effective and viable for large-scale production and clinical use. 👉Register here: https://ow.ly/9fS050RTIn2 What you will learn from the webinar 🔹Importance and challenges of developability in antibody therapeutics 🔹Role of AI in transforming developability assessment 🔹Technological solutions for antibody developability assessment and engineering #AntibodyTherapeutics #Biotech #Webinar #AntibodyDevelopment #MonoclonalAntibodies #mAb #PTM #AntibodyEngineering #AI

Data needed to forge the link between AI and mRNA While the potential for using AI to develop mRNA therapeutics is now a real possibility, accurate and diverse data is needed to make it a reality

What is the best way to accelerate #drugdiscovery and #drugdevelopment? In this blog + case study, we show how #chromatinmapping assays can help with: ✔️ Identifying new druggable targets ✔️ Preclinical mechanism-of-action studies ✔️ Monitoring patient responses https://hubs.la/Q02TRj2S0

Of the more than 300 CRISPR-based therapies in the development pipeline, ~90% are at the early stage of development, with the majority still in preclinical testing...

🌐 This Week in 𝐖𝐡𝐞𝐫𝐞 𝐓𝐞𝐜𝐡 𝐌𝐞𝐞𝐭𝐬 𝐁𝐢𝐨: 🌐 BioPharmaTrend.com cover the global "𝐬𝐮𝐩𝐞𝐫𝐛𝐮𝐠 𝐦𝐚𝐩" for targeted phage therapy, XtalPi Inc.'s AI platform accelerating #antibody discovery, the rise of LLC models in biotech as shared by Nimbus Therapeutics, Google DeepMind’s improved Tx-LLM for #DrugDiscovery, and companies using AI to optimize clinical trials. Interested to learn more? 👉 https://lnkd.in/ebjTat72 Institute of Biochemistry, Biological Research Centre, Szeged, Hungary XtalPi Inc. Abbas Kazimi Nimbus Therapeutics Takeda Google DeepMind #Biotech #Pharma #AI #Innovation #TechMeetsBio #ClinicalTrials

In this issue of our weekly newsletter Where Tech Meets Bio: • Mapping Superbugs with Precision Phage Therapy: A global "superbug map" developed by scientists at the Institute of Biochemistry, Biological Research Centre, Szeged, Hungary, tracks the spread of Acinetobacter baumannii—a highly resistant pathogen. By pinpointing dominant strains, hospitals can deploy pre-emptive phage therapies, potentially treating up to 80% of local infections with region-specific phage cocktails. • XtalPi Inc.’s XtalFold Platform and Antibody Discovery: XtalPi's AI platform, XtalFold, is helping Janssen Biotech, Inc. accelerate antibody discovery. It predicts antibody-antigen interactions with higher accuracy than AlphaFold-Multimer, making drug discovery faster and more precise. • LLC Business Models Take Off in Biotech: In a recent discussion, Abbas Kazimi of Nimbus Therapeutics highlighted how the LLC model has provided flexibility in structuring deals, such as their $6 billion TYK2 inhibitor sale to Takeda. This approach allows biotech companies to sell specific assets while maintaining overall control of their operations. • Google DeepMind’s Tx-LLM Refined for Drug Discovery: The Tx-LLM model, powered by Google’s PaLM-2, has been officially introduced—fine-tuned to predict properties of small molecules, proteins, and cell lines. The update improves accuracy in therapeutic tasks and invites external collaboration for further development. • Companies to Watch in Clinical Trials: We will also be taking a look at some of the companies leveraging AI to streamline and optimize clinical trials, tackling the most significant challenges in drug development. 📩 Read the full article in the comments!