伙伴关系
Phylo 与……合作 Bayer 关于Biomni实验室的 AI Agents 药物研究
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2026年10月22日，Phylo, Inc. 宣布与Bayer合作，部署Biomni Lab,Phylo的代理AI平台，以加快科研与评价，Bayer科学家在药物发现方面全面获取文献和漫画证据工作流程。
Bayer伙伴关系
根据协议，Phylo将为拜耳提供对其代理平台的充分访问。 Phylo 称这个平台帮助拜耳科学家将科学文献，生物医学数据，分析方法汇集在一个环境中，减少了收集和评估证据所需的人工努力。 拜耳工作流程将侧重于收集文献和漫画证据，用于药物的发现。
称公司高级副总裁、数据科学与人工智能主管，Jasti 称是AI的合作伙伴，在每一个步骤上都应用严格，使得拜耳科学家能够考虑更多想法，提出更雄心勃勃的问题，其宣称的目标是更快地给患者带来新药。
Phylo 称平台的工作流程是可检查的：每个结果都保留其基本背景，因此研究人员可以审查背后的来源、方法和执行，并应用自己的科学判断。
Kexin Huang，博士，共同创始人兼Phylo，称的首席执行官，“为了代理AI在研究中的重要性，它必须赢得从事这项工作的科学家的信任。” Huang称拜耳选择Biomni Lab是对Phylo在平台中构建的刚性与AI能力的强烈认可，并描述了该公司对生物药的AI-原生操作系统的愿景，其中每个科学家可以指挥一组AI agents来对付复杂的研究问题。 与Bayer，黄 Z称一起，Phylo旨在展示AI能力遇到深层科学专业知识后可能实现的事情。
Biomni实验室平台
Phylo称Biomni Lab是第一个综合生物学环境，公司将这个类别定义为科学家可以搜索文献，设计实验，分析生物数据，解释结果，规划下一步的单一工作空间。 根据Phylo的Biomni Lab产品页面，该平台支持跨药物发现，基因组学，生物信息学，以及多基因组的研究，并为板凳科学家，计算生物学家，生物科学家，翻译研究人员，生命科学研发团队所建。 个别研究人员可以利用它来探索科学问题并进行分析，而多学科小组则可以跨数据、文献和研究工作流程进行协作。
Phylo的文献将Biomni Lab与通用的LLMs和AI助手对比，将其描述为用于生物学和生命科学的研究与开发的目的：科学家定义问题，设计实验，执行分析，并和AI agents一起解释结果。 为了可靠性，该平台在引用科学文献时提供引用和与原始来源的链接，并保存每项分析背后的工具、代码、方法和执行细节，以便研究人员检查方法、核实结果和复制工作流程。 文件告诫说，如同任何AI协助的科学工作一样，研究人员应当利用适当的科学判断来审查结果。
Phylo的企业层级，Biomni Lab for Enterprise，提供专用的基础设施，定制的agents，先进的安全特性，以及对更大的生命科学组织的优先支持，它可以与一个组织的专利研究数据，工具和跨多学科工作流程连接。 受控产品与源于斯坦福的开源 Biomni项目分享其研究世系，提供科学家和开发商可以自行托管和定制的社区代码；Biomni Lab 新增是一个受控研究环境，合作特征，可扩展的基础设施，以及不断更新的能力。
Phylo 称 Biomni Lab支持药物发现跨越早期发现，目标验证，引领研究，临床前分析，以及临床证据生成；列出的用途包括综合疾病生物学，分析基因组数据，确定重点并评价目标，调查生物结构和候选者，以及审查临床证据。 基因组学的涵盖范围包括人类遗传学研究，变体注释，因果基因优先化，RNA-seq分析，单细胞分析，以及空间转录学，研究人员能够用自然语言定义科学目标，然后检查由此产生的方法，代码，可视化，以及解释。
生物信息学和多基因组工作流程包括基因组学，转录组学，蛋白质组学，单细胞数据，以及空间生物学；研究人员可以计划并执行计算分析，与数据集合作，解释结果，创建可视化，生成报告，同时保留工作背后的代码，来源，以及执行细节。
研发领导人可在Contact@phylo.bio上请求Biomni Lab的企业示范。
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Partnerships
Phylo Partners With Bayer on Biomni Lab AI Agents for Drug Research
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Phylo, Inc. announced on October 22, 2026, a partnership with Bayer to deploy Biomni Lab, Phylo’s agentic AI platform, to accelerate scientific research and evaluation, with Bayer scientists gaining full access for literature and omics evidence workflows in drug discovery.
The Bayer Partnership
Under the agreement, Phylo will provide Bayer with full access to its agentic platform. Phylo says the platform helps Bayer scientists bring together scientific literature, biomedical data, and analytical methods in one environment, reducing the manual effort required to assemble and assess evidence. The Bayer workflows will focus on assembling literature and omics evidence for drug discovery.
Sai Jasti, Senior Vice President, Head of Data Science & Artificial Intelligence, Bayer Pharmaceuticals R&D, said the partnership “reflects our belief that AI should expand scientific curiosity, not just speed up routine work.” Jasti said an AI partner that applies rigor at every step allows Bayer scientists to consider more ideas and pose more ambitious questions, with the stated aim of bringing new medicines to patients faster.
Phylo says the platform’s workflows are inspectable: each result preserves its underlying context, so researchers can review the sources, methods, and execution behind it and apply their own scientific judgment.
Kexin Huang, Ph.D., Co-Founder and CEO of Phylo, said, “For agentic AI to matter in research, it has to earn the trust of the scientists doing the work.” Huang called Bayer’s selection of Biomni Lab a strong endorsement of the rigor and AI capabilities Phylo has built into the platform, and described the company’s vision of an AI-native operating system for biopharma, one where each scientist can direct a team of AI agents against complex research questions. With Bayer, Huang said, Phylo aims to show what becomes possible when AI capabilities meet deep scientific expertise.
The Biomni Lab Platform
Phylo calls Biomni Lab the first Integrated Biology Environment, a category the company defines as a single workspace where scientists can search literature, design experiments, analyze biological data, interpret results, and plan next steps. According to Phylo’s Biomni Lab product page, the platform supports research across drug discovery, genomics, bioinformatics, and multi-omics, and is built for bench scientists, computational biologists, bioinformaticians, translational researchers, and life sciences R&D teams. Individual researchers can use it to explore scientific questions and run analyses, while multidisciplinary teams can collaborate across data, literature, and research workflows.
Phylo’s documentation contrasts Biomni Lab with general-purpose LLMs and AI assistants, describing it as purpose-built for biology and life sciences R&D: scientists define questions, design experiments, execute analyses, and interpret results alongside AI agents. For reliability, the platform provides citations and links to original sources when referencing scientific literature and preserves the tools, code, methodology, and execution details behind each analysis so researchers can inspect the approach, verify results, and reproduce the workflow. The documentation cautions that, as with any AI-assisted scientific work, researchers should review results using appropriate scientific judgment.
Phylo’s enterprise tier, Biomni Lab for Enterprise, provides dedicated infrastructure, custom agents, advanced security features, and priority support for larger life sciences organizations, and it can connect with an organization’s proprietary research data, tools, and processes across multidisciplinary workflows. The managed product shares its research lineage with the open-source Biomni project, which originated at Stanford and offers community code that scientists and developers can self-host and customize; Biomni Lab adds a managed research environment, collaboration features, scalable infrastructure, and continuously updated capabilities.
Phylo says Biomni Lab supports drug discovery across early discovery, target validation, lead research, preclinical analysis, and clinical evidence generation; listed uses include synthesizing disease biology, analyzing omics data, prioritizing and evaluating targets, investigating biological structures and candidates, and reviewing clinical evidence. The genomics coverage spans human genetics research, variant annotation, causal gene prioritization, RNA-seq analysis, single-cell analysis, and spatial transcriptomics, with researchers able to define a scientific objective in natural language and then inspect the resulting methodology, code, visualizations, and interpretation.
Bioinformatics and multi-omics workflows cover genomics, transcriptomics, proteomics, single-cell data, and spatial biology; researchers can plan and execute computational analyses, work with datasets, interpret results, create visualizations, and generate reports while preserving the code, sources, and execution details behind the work.
R&D leaders can request an enterprise demonstration of Biomni Lab at contact@phylo.bio.
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