When it comes to AI for drug discovery what used to take thousands of molecules in a design-make-test-iteration cycle can now be done in as few as hundreds of molecules. And what used to take six years might take less than a year now. A full life cycle here of the design-make-test cycle is designing molecules, medicines, proteins in a computer, being able to validate those in a wet lab and using that data that's generated to further train or fine-tune that initial model and prediction.And where we're seeing ...

Core Insights - Bruker Corporation has secured a $10 million order for advanced Nuclear Magnetic Resonance (NMR) instrumentation from notable research institutions, including the New York Structural Biology Center, the University of Delaware, and Northwestern University [1][2]. Group 1: Order Details - The $10 million order is aimed at enhancing research capabilities in drug discovery and disease biology [2]. - The advanced NMR instrumentation includes a fast NMR sample shuttle system, a magnetic tunnel, and an electromagnetic field cycling coil [2]. Group 2: Technical Capabilities - The NMR Relaxometry system allows for measurements of spin-lattice relaxation rate constants for various spins in biological macromolecules, including proteins and nucleic acids [3]. - The system operates across a range of magnetic fields from 100 µT to 16.4 T, facilitating advanced research [3]. Group 3: Research Implications - The new instrumentation is expected to open new time regimes for studying molecular dynamics, aiding in the understanding of biological processes and the discovery of potential new drugs for cancer and other diseases [4]. Group 4: Company Overview - Bruker Corporation specializes in developing, manufacturing, and distributing high-performance scientific instruments and analytical solutions across life sciences, applied sciences, and industrial applications [5]. - The company's product portfolio includes NMR spectrometers, mass spectrometry systems, atomic force microscopes, and preclinical imaging systems [5].

Core Insights - Shuttle Pharmaceuticals Holdings, Inc. has executed a non-binding letter of intent to acquire Molecule.ai, an AI-driven company focused on transforming drug discovery and development [1][2] - Molecule.ai utilizes advanced machine learning models, including large language models, to enhance the accuracy and efficiency of evaluating novel molecules, thereby shortening development timelines and reducing costs [3][4] - The acquisition aims to integrate Molecule.ai's innovative platform into Shuttle's operations, enhancing its capabilities in drug-target interaction and autonomous drug discovery workflows [5][9] Acquisition Details - Shuttle will acquire all rights and interests of Molecule.ai, assuming all liabilities, with Molecule.ai having no further obligations post-closing [5][7] - The purchase price for the acquisition is set at $10 million, payable in cash and common shares of Shuttle, contingent upon the completion of certain milestones [7] Molecule.ai's Innovations - Molecule.ai's platform includes features such as rapid evaluation of chemical and biological properties, drug-target interaction modeling, and an autonomous framework for drug discovery workflows [9] - The introduction of the Agentic AI mode will enable AI agents to perform multi-step drug discovery processes, significantly reducing manual effort [6][9] Company Missions - Shuttle Pharmaceuticals focuses on improving outcomes for cancer patients undergoing radiation therapy by developing therapies that enhance the effectiveness of radiation while minimizing side effects [10] - Molecule.ai's mission is to empower researchers with next-generation tools that lower costs and improve efficiency in therapeutic development [8]

Group 1 - Bristol-Myers Squibb, Takeda Pharmaceuticals, and Astex Pharmaceuticals have formed a collaboration to utilize AI for drug discovery by pooling proprietary data [1][3] - The collaboration will contribute data from several thousand experimentally determined protein-small molecule structures to train an AI model named OpenFold3 [2][3] - The initiative employs a federated data sharing model provided by Apheris, allowing secure aggregation of diverse datasets while keeping sensitive data in its original location [2][3] Group 2 - The goal of the initiative is to enhance the accuracy of OpenFold3 in predicting interactions between proteins and small molecules, which is vital for drug discovery and development [3] - OpenFold3 is part of the AI Structural Biology Network, conducted in collaboration with the AlQuraishi Lab at Columbia University [3]

Industry Trends & Competitive Landscape - China is gaining ground in biotech due to lower costs, not superior science [1] - U S drugmakers are increasingly licensing early-stage compounds from China, driven by cost advantages [5][6] - Approximately one-third of new compounds licensed across the pharmaceutical sector last year were from China [6] - Venture capital investors are concerned that sourcing innovation from China could hollow out the U S biotech sector [5] - U S companies are increasingly conducting Phase 1 and Phase 2 clinical trials in places like Australia due to regulatory complexities in the U S [13][14] Regulatory & Clinical Trials - The FDA previously rejected a drug from Lilly due to misconduct found in late-stage clinical trials conducted in China [4] - Current regulatory processes in the U S make it difficult to initiate Phase 1 clinical trials [9][14] - Regulatory reform is needed to make the U S more competitive in early-stage drug development by lowering hurdles for Phase 1 clinical trials while maintaining patient safety [15] - Extending the Phase Zero microdosing trial framework to new formulations like ASOs, mRNA, and antibody-based drugs could improve efficiency [17][18] Innovation & Intellectual Property - China is adept at copying U S Western discoveries, synthesizing molecules to hit biological targets quickly and inexpensively [5] - China primarily focuses on developing fast followers or slightly improved molecules based on U S patents, rather than novel compounds [8][9]

Core Insights - C4X Discovery Holdings Ltd has selected a pre-clinical candidate from its oral α4β7 integrin inhibitor programme aimed at treating inflammatory bowel disease (IBD), which supports once-daily dosing and has the potential to be a best-in-class therapy [2][3] Company Overview - C4X Discovery is a pioneering drug discovery company that combines scientific expertise with advanced technologies to develop next-generation oral medicines, focusing on immuno-inflammatory diseases [8] Market Potential - IBD affects over five million people globally and is projected to reach a market size of USD 47.7 billion by 2034, indicating a significant opportunity for C4X Discovery's oral α4β7 integrin inhibitor [3] Treatment Advantages - The oral α4β7 inhibitor offers a convenient treatment option that could advance ahead of biologics, potentially increasing the number of patients treated and improving patient outcomes while reducing the burden on healthcare systems [4] Pipeline and Future Plans - C4X Discovery is advancing multiple programmes, including oral α4β7 and TNFα inhibitors, and a first-in-class PAD4 programme, with plans to continue partnering discussions and generate pivotal data in the coming months [5]

AI驱动的药物发现引擎 - Isomorphic Labs 正在构建一个药物设计引擎，旨在为不同疾病领域和适应症设计新的分子，甚至适用于不同的药物形式[2] - 该引擎由一系列AI模型驱动，需要大约六个类似AlphaFold的突破性技术共同作用[3] - 这些模型包括预测蛋白质结构、分子结合强度以及药物安全性和吸收性的能力[3][6] 传统药物发现的变革 - 传统药物设计是迭代的，耗时且成本高昂，而Isomorphic Labs 正在将这一过程转移到虚拟世界，在计算机上进行设计和测试，从而缩短时间并提高效率[8][9][10] - 通过在计算机上进行多次迭代，选择最佳结果进行实验室测试，从而跳过步骤，更快地获得更好的结果[10] 模型能力与挑战 - AlphaFold 已经解决了蛋白质折叠问题，但仍需要提高模型的准确性，并理解分子如何与身体的不同部分相互作用[11][12][13] - 分子空间巨大（约10的60次方），需要生成模型和搜索过程来有效地搜索整个空间，从而减少需要实验室测试的分子数量[15][16] 数据策略与质量 - 机器学习依赖于高质量的数据，Isomorphic Labs 拥有全面的数据战略，利用公共数据、历史数据以及专门为机器学习模型创建的湿实验室数据[24][25] - 公司在理解、摄取、清理数据以及从中提取信号方面投入了大量精力[26] 通用性与可重用性 - Isomorphic Labs 专注于构建通用的技术，可以应用于任何靶点、任何疾病领域，甚至任何不同的药物形式[27][28] - 通用模型更具挑战性，但可以创造针对长期未解决的问题的全新化学物质[30][31] 疾病领域重点 - 公司专注于免疫学和肿瘤学，因为这些领域的临床试验更易于处理，可以在更短的时间内完成，并且对患者的影响很大[33][34] - 目标是将癌症转变为一种慢性疾病，患者可以通过药物治疗获得正常的寿命，这可能在几年内实现[36][37] 模型能力与应用 - 这些模型可以应用于整个人类蛋白质组，并且由于神经网络模型运行速度快，可以并行分析数千个蛋白质[39] - 这改变了实验设计，因为在实验世界中，一次只能研究一个蛋白质[40] 合作与里程碑 - 与诺华和礼来等公司的合作非常密切，在一些具有挑战性的靶点上取得了良好的进展[44] - 在某些情况下，已经识别出与以前未知的蛋白质结合的首批化学物质[45] 未来展望 - 目标是在2-3年内将AI药物发现过程缩短至几个月，关键在于提高计算机实验结果的预测准确性[47][48][49] - 展望未来，AI工具可以帮助诊断疾病，并提供相应的药物治疗，从而对人类健康和疾病产生重大影响[50][51]

Company Mission & Vision - Isomorphic Labs' mission is to solve all disease by building a general platform to understand biology and chemistry [1][2] - The company aims to revolutionize drug discovery and potentially discover hundreds of cures by focusing on platform capabilities [20] - The long-term vision includes expanding into areas like energy and material design to address climate and energy crises [22] Drug Discovery & Development - Isomorphic Labs is currently at the preclinical stage and building a team for the clinical phase [3] - Internal pipeline focuses on oncology and immunology, particularly cancer, due to clinical favorability for new drugs [5][6] - The company aims to significantly reduce preclinical research time from 3-6 years to a matter of months [7] - While focusing on drug discovery, the company plans to partner for the clinical and regulatory phases [9][10] Partnerships & Funding - Isomorphic Labs has partnerships with Novartis and Eli Lilly to refine its platform [11][12] - The partnership with Novartis expanded from 3 to 6 targets [13] - The company raised $600 million USD in its first external funding round to build out the platform and attract talent [14] Technology & Innovation - Isomorphic Labs is building upon work with AlphaFold, extending beyond protein structure to chemistry, compound design, and toxicity [4][17][18] - The company is taking a multi-modal approach, developing specialized models adapted to specific scientific areas [19][23]

>> YOU ARE WATCHING BLOOMBERG TECH EUROPE. COMING UP, A. I.DISCOVERY COULD BE THE NEXT FRONTIER OF MEDICINE TO SPEEDING UP DEVELOPMENT. THE TECHNOLOGY PROMISES PLENTY BUT WILL IT DELIVER. WE SPEAK EXCLUSIVELY TO DENNIS HASSABIS THE NOBLE LAUREATE, WHOSE LONDON-BASED ISOMORPHIC LABS HAS THE AMBITION 'TO SOLVE ALL DISEASE.' THE MAN RESPONSIBLE FOR ALL OF ALPHABET’S CORE A. I. WORK AS HEAD OF GOBBLE DEEPMIND IS SETTING THE BAR HIGH FOR ISOMOPHIC.>> THE KIND OF HOLY GRAIL IS TO TACKLE CANCER. BUT WE’VE DONE IT ...

Company Focus - DeepMind is leveraging frontier AI to reimagine drug discovery and advance human health [1] - DeepMind's Iso project aims to solve all diseases [1] Leadership Engagement - DeepMind's CEO, Chief AI Officer, and Head of Medicinal Drug Design were featured on Bloomberg Tech Europe to discuss AI in drug discovery [1]