As compute costs fall and models mature, sustaining a competitive edge in global drug development requires innovation across the entire data infrastructure stack. Biology-native data, agentic workflows, and lab automation will define the next generation of leading biotechs. 随着计算成本下降和模型成熟，保持全球药物开发的竞争优势需要在整个数据基础设施栈中进行创新。生物学原生数据、智能工作流程和实验室自动化将定义下一代领先的生物技术。
Drug development has long been a process of trial-and-error to test biological hypotheses against clinical reality. Despite advancements in science and technology, the timeline from target ID to a clinical candidate still often takes more than five years, and nearly 90% of drugs that enter clinical trials fail. With treatment landscapes evolving and modalities becoming increasingly complex, it is no surprise that R&D costs per approved therapy continue to double every nine years. The limiting factor in drug development has never been a shortage of hypotheses, but rather a shortage of the resources to evaluate them effectively and efficiently. 药物开发长期以来一直是一个反复试验的过程，旨在将生物学假说与临床现实对抗。尽管科学和技术不断进步，从靶点鉴定到临床候选药的时间线仍常常超过五年，且近90%进入临床试验的药物失败。随着治疗环境的演变和治疗方式日益复杂，每批获批治疗的研发成本每九年翻一番也就不足为奇了。药物开发的限制因素从来不是假设的短缺，而是缺乏有效且高效的评估这些假设的资源。
Machine learning in drug design holds the promise to change that math by accelerating iteration and improving the odds of success. Between 2012 and 2022, approximately 200 companies that leveraged AI for drug discovery raised a collective $18B. We are now seeing the results of these efforts play out in the clinic. 药物设计中的机器学习有望通过加快迭代和提高成功几率来改变这一数学。2012 年至 2022 年间，约有 200 家利用人工智能进行药物发现的公司共筹集了 180 亿美元。 我们现在正在临床中看到这些努力的成果。
In June 2025, Insilico Medicine published positive Phase IIa results in Nature Medicine for its first-in-class small molecule TNIK inhibitor rentosertib in idiopathic pulmonary fibrosis. This made it the first drug to generate clinical proof-of-concept for which both the target was discovered and the molecule was designed entirely using generative AI. In this example, AI played a critical role in changing “the math” by leveraging a generative chemistry platform for molecule design and optimization. The team nominated a preclinical candidate after screening only 78 molecules, rather than the thousands typically required, and did so in 18 months at less than 10% of the average cost per approved drug. 2025 年 6 月，Insilico Medicine 在其首创小分子 TNIK 抑制剂 rentosertib 在特发性肺纤维化中发表了 Nature Medicine 的 IIa 期阳性结果 。这使其成为首个在生成式人工智能下，既发现了靶点，又完全设计出了该分子的临床概念验证。在这个例子中，人工智能通过利用生成式化学平台进行分子设计和优化，在改变“数学”方面发挥了关键作用。团队在筛选了 78 种分子后提名了临床前候选药物，而非通常所需的数千个分子，并在 18 个月内完成，且成本低于每批药物平均成本的 10%。
With a favorable investment-reward profile, it is no surprise that many companies, including large pharma, have made concerted efforts to incorporate AI platforms into the R&D process to accelerate drug discovery. In early 2026, GSK and Eli Lilly announced deals with NOETIK and Chai Discovery for access to their oncology and drug design foundation models, with GSK committing $50M upfront to NOETIK and Lilly paying a mid-eight-figure annual access fee to Chai for biologics design. 凭借有利的投资回报特征，许多公司，包括大型制药公司，积极努力将人工智能平台纳入研发流程，以加速药物发现。2026 年初，葛兰素史克和礼来宣布与 NOETIK 和 Chai Discovery 达成协议，获得其肿瘤学和药物设计基础模型的访问权限，GSK 预付 5000 万美元给 NOETIK，礼来支付每年中位数八位数的生物制剂设计使用费。
insitro, which integrates large-scale human cell data generation with machine learning, recently saw BMS nominate two additional ALS targets from their collaboration, validation of the value of the full-stack approach in pairing proprietary data generation with drug development. Insitro 将大规模人类细胞数据生成与机器学习结合，最近 BMS 通过他们的合作提名了另外两个 ALS 目标，验证了全栈方法在将专有数据生成与药物开发结合中的价值。
Isomorphic Labs, the Google DeepMind spinout behind AlphaFold, has pursued deep partnerships with Lilly, Novartis, and J&J with potential value exceeding $3B, while advancing its own internal oncology pipeline toward first-in-human trials. Its newly released IsoDDE model more than doubles AlphaFold 3's accuracy on the hardest generalization benchmarks, making it one of the most closely watched companies in AI-driven drug design. And it's not just pharma showing interest: in early April 2026, Anthropic acquired Coefficient Bio, an eight-month-old startup built by former Evozyne/Genentech/Prescient Design computational biologists, for $400M in stock, signaling that frontier AI labs are now making direct bets on drug discovery. Isomorphic Labs，作为 AlphaFold 背后的 Google DeepMind 衍生企业，已与礼来、诺华和强生建立了深度合作，潜在价值超过 30 亿美元，同时推进自身内部肿瘤学流程，朝着首创人体试验迈进。其新发布的 IsoDDE 模型使 AlphaFold 3 在最难的泛化基准测试中的准确率翻倍多，使其成为 AI 驱动药物设计中备受关注的公司之一。而且不仅仅是制药行业表现出兴趣：2026 年 4 月初，Anthropic 以 4 亿美元股票收购了 Coefficient Bio，这是一家由前 Evozyne/Genentech/Prescient Design 计算生物学家创立、成立仅八个月的初创公司，表明前沿 AI 实验室开始直接押注药物发现。
Though computational chemistry tools first emerged in the 1980s, the modern era of AI for biotech effectively began with the rise of deep learning in the 2010s, when it became clear that neural networks could learn meaningful representations of molecular structure from data. The watershed moment occurred when DeepMind's AlphaFold2 and the Baker Lab's RoseTTAFold solved the problem of predicting a protein’s 3D structure from its amino acid sequence alone. From there, the number of biological AI models grew exponentially. By 2024, over 350 biological AI models were published, including AlphaFold3, ESM3, Boltz-1, BindCraft, Evo, scGPT, and H-Optimus-0, highlighting AI’s ability to perform tasks across generative protein design, genomics and perturbation modeling, and pathology image analysis. 尽管计算化学工具最早出现在 20 世纪 80 年代，但现代人工智能在生物技术领域的时代实际上始于 2010 年代深度学习的兴起，当时神经网络能够从数据中学习有意义的分子结构表示。分水岭时刻出现在 DeepMind 的 AlphaFold2 和贝克实验室的 RoseTTAFold 解决了仅凭氨基酸序列预测蛋白质三维结构的问题。从那以后，生物人工智能模型的数量呈指数增长。 到 2024 年，已发布超过 350 个生物人工智能模型，包括 AlphaFold3、ESM3、Boltz-1、BindCraft、Evo、scGPT 和 H-Optimus-0，展示了人工智能在生成蛋白设计、基因组学与扰动建模以及病理图像分析等多个任务中的执行能力。
(The Cambrian explosion of AI models for biology has already happened. Between 2015 and 2025, the number of new biology AI models released each year has exponentially increased from sub-ten to 380+ and counting. (Note that this is based purely on the dataset from Epoch AI and may not be complete.))
Most recently, new models such as JAM-2, BoltzGen, Latent-X2, Chai-2, and IsoDDE have continued to bring us closer to designing drug-like biologics straight from the computer. Momentum on the zero-shot design task has never been stronger. Following a surge in new AI models for biology, the field now has an armamentarium of tools spanning the drug development continuum, from structural modeling to molecule design and drug optimization. 最近，JAM-2、BoltzGen、Latent-X2、Chai-2 和 IsoDDE 等新型号，持续让我们更接近直接从计算机设计出类药物生物制剂。零发设计任务的势头从未如此强烈。随着生物学中新型人工智能模型的激增，该领域现已拥有涵盖药物开发全过程的工具库，从结构建模到分子设计和药物优化。
Three principles of biology-native data infrastructure 生物学原生数据基础设施的三大原则
In an increasingly crowded landscape, we believe the AI-driven biotechs that persist and scale over time will be those built on three core principles, which together we define as the principles of biology-native data infrastructure: 在日益拥挤的环境中，我们相信能够持续并逐步扩展的 AI 驱动生物技术将建立在三大核心原则之上，我们将其定义为生物学原生数据基础设施的原则：
Curating scalable, multi-modal datasets informed by the biological challenges associated with a drug’s mechanism of action. 策划可扩展、多模态数据集，参考药物作用机制相关的生物学挑战。
Incorporating the newest agentic AI frameworks across entire R&D workflows. 将最新的代理人工智能框架整合到整个研发工作流程中。
Adopting lab automation to power rapid, closed experimental feedback loops. 采用实验室自动化以驱动快速、封闭的实验反馈循环。
Companies that enable or embody these principles will be the ones to truly accelerate drug design timelines, reduce clinical trial failure risk, and deliver on the promise that AI in biology holds. 能够实现或体现这些原则的公司，将真正加速药物设计进度，降低临床试验失败风险，并兑现人工智能在生物学领域的承诺。
Below we explain why these principles are critical to the drug development industry and highlight the emerging categories and companies that are putting these principles into practice. 以下我们将解释为何这些原则对药物开发行业至关重要，并重点介绍正在将这些原则付诸实践的新兴类别和公司。
Market map 市场地图
(Our market map highlights private life science companies that are leveraging AI to create and analyze biological datasets that address challenges along the drug development continuum, accelerate R&D workflows end-to-end, and automate the physical work of conducting wet lab experiments. )
Biology-native data at scale
- 大规模的生物学原生数据
Much of the data that makes current AI biology models possible was slowly assembled over decades of publicly-funded science. The Protein Data Bank’s (PDB) 200K+ protein structures were experimentally determined by techniques like X-ray crystallography and NMR spectroscopy. Similarly, the Human Genome Project’s map of human genes and DNA was the result of sequencing efforts across global research institutions, and ChEMBL’s bioactivity database on millions of small molecules was accumulated through years of manual patent and literature data extraction. The impact of these databases is remarkable—for example, structural data from the PDB contributed to the development of 100% of the protein-targeted small-molecule cancer drugs approved by the FDA between 2019 and 2023. 使当前人工智能生物学模型成为可能的许多数据，都是通过数十年公共资助的科学慢慢积累而成。蛋白质数据库（PDB）的 200K+蛋白质结构通过 X 射线晶体学和核磁共振光谱学等技术进行了实验确定。同样，人类基因组计划的人类基因和 DNA 图谱是全球各研究机构测序努力的结果，ChEMBL 数百万小分子的生物活性数据库则是通过多年手工专利和文献数据提取积累而成。这些数据库的影响显著——例如，PDB 的结构数据促成了 2019 年至 2023 年间 FDA 批准的 100%蛋白质靶向小分子癌症药物的开发。
The AI biology models developed in the last few decades reflect the data that is readily available, with nearly 63% of models being trained on protein sequences and structures from the Uniprot database and the PDB (Epoch AI). The most common tasks these models are used for are the contextual understanding of protein or nucleotide sequences, protein folding prediction, or protein design. However, significant gaps in our understanding of early-stage drug discovery biology persist, driven by the sheer complexity of biological systems and the limitations of the tools we have to study them. 过去几十年开发的 AI 生物学模型反映了现成的数据，近 63%的模型是在 Uniprot 数据库和 PDB（Epoch AI）中的蛋白质序列和结构上训练的。 这些模型最常用的任务包括蛋白质或核苷酸序列的上下文理解、蛋白质折叠预测或蛋白质设计。然而，我们对早期药物发现生物学的理解仍存在重大空白，这主要是由于生物系统的极其复杂性和我们所拥有研究工具的局限性所致。
Despite its scale, the PDB is heavily biased toward proteins that are stable, soluble, and amenable to crystallization. Though membrane proteins, intrinsically disordered proteins, and transient protein complexes are some of the most compelling drug targets in oncology and neurodegeneration, they often defy these criteria and therefore remain dramatically underrepresented. Additionally, the structures the PDB captures are static snapshots, freezing proteins in a single conformation rather than the dynamic ensemble of shapes they adopt in a living cell. Yet it is often these alternative conformations that are most therapeutically relevant, as seen with allosteric binding sites that only become accessible upon ligand binding. 尽管规模较大，PDB 仍严重偏向于稳定、可溶且易结晶的蛋白质。尽管膜蛋白、内在无序蛋白和短暂蛋白复合物是肿瘤学和神经退行性疾病中最具吸引力的药物靶点之一，但它们常常违背这些标准，因此仍然严重不足。此外，PDB 捕捉的结构是静态快照，将蛋白质冻结在单一构象中，而非活细胞中动态的形状集合。然而，往往这些替代构象在治疗上最具意义，如只有配体结合后才能接触到的变构结合位点。
Although bringing a new drug to market begins with protein structure and design tasks, early-stage drug discovery accounts for only a fraction of the time and cost of the drug development process. More than two-thirds of drug development time and resources are allocated to the steps after early drug discovery, including the ADME (pharmacokinetic properties related to “absorption, distribution, metabolism, and excretion”) and formulation optimization work done in preclinical studies, as well as the safety and efficacy studies run in clinical trials. To advance a drug from a hit to lead to a development candidate, far more is needed than confirmation that a molecule binds its target. The drug development process also requires an understanding of developability, immunogenicity, off-target effects, thermostability, solubility, and aggregation propensity, properties for which large, high-quality public datasets to supervise model learning currently don't exist. 虽然将新药推向市场始于蛋白质结构和设计任务，但早期药物发现仅占药物开发过程时间和成本的一小部分。 超过三分之二的药物开发时间和资源被分配到早期药物发现后的各个步骤 ，包括 ADME（与“吸收、分布、代谢和排泄”相关的药代动力学特性）和临床前研究中的制剂优化工作，以及临床试验中的安全性和有效性研究。要将药物从成功转化为开发候选药物，所需的远不止确认分子是否能与其靶标结合。药物开发过程还需要理解可开发性、免疫原性、离靶效应、热稳定性、溶解度和聚集倾向，而目前尚无大型高质量的公开数据集用于监督模型学习。
While drug discovery is fundamentally a problem of understanding perturbations, there is no PDB-equivalent repository for understanding cell phenotypes in response to perturbations, or even proteomics data across disease states. Tying cellular to clinical data presents an even greater gap, as patient-level omics profiles linked to treatment outcomes and trial responses exist siloed in hospital systems and biopharma databases, making it nearly impossible to train models that could predict which patients will respond to a given therapy before they ever enroll in a trial. These are precisely the properties that determine whether a molecule can eventually become an approved drug, which means the most commercially important predictions are also the ones where the data infrastructure is weakest. 虽然药物发现本质上是理解扰动的问题，但目前尚无 PDB 等效的数据库来理解细胞表型对扰动的响应，甚至没有跨疾病状态的蛋白质组学数据。将细胞数据与临床数据联系起来则存在更大的差距，因为患者层面的组学画像与治疗结果和试验反应相关联，存在于医院系统和生物制药数据库中，几乎不可能在患者加入试验前预测哪些患者对某疗法有反应。这些正是决定分子最终能否成为批准药物的性质，这意味着最具商业价值的预测往往是数据基础设施最薄弱的部分。
Much of the biological data available today was generated before the explosion of AI biology models, meaning it often lacks the traits that make it useful for machine learning. Annotations are often incomplete or unstandardized, and important context, such as cellular environment or lab equipment used, is rarely captured or encoded into datasets. In many cases, biological datasets simply don’t have the scale for models to draw statistically significant conclusions or make unbiased predictions. And even where scale exists, data tends to be siloed by modality—genomic, transcriptomic, pathology, and clinical outcome datasets frequently are collected and live in separate places, making it challenging to construct a data layer that allows AI to reason across the full picture of human biology. 如今可用的大部分生物数据是在 AI 生物学模型爆发之前生成的，这意味着它们往往缺乏使其在机器学习中有用的特性。 注释通常不完整或未标准化，重要的上下文，如所用的蜂窝环境或实验室设备，很少被捕获或编码到数据集中。在许多情况下，生物数据集根本没有足够的规模让模型得出统计显著的结论或做出无偏的预测。即使存在规模，数据也往往被模式隔离——基因组、转录组、病理和临床结局数据集常常被收集并存放在不同地方，这使得构建一个让人工智能能够全面推理人类生物学图景的数据层具有挑战性。
We’re grateful to have backed several companies that exemplify this principle. Peptone is combining atom-level biophysics with supercomputing to generate proprietary structural data on intrinsically disordered proteins, and Inductive Bio is assembling one of the industry's largest and most diverse ADMET datasets to train its Beacon models, which recently placed first among 370+ submissions in the OpenADMET-ExpansionRx endpoint prediction challenge. Converge Bio is generating large-scale datasets to train and validate their own models to deploy with pharma and biotech customers for antibody design or sequence optimization, and Seismic is taking a pipeline-first approach, using its IMPACT platform to parallelize the optimization of multiple drug-like properties of novel immunology biologics. 我们很感激能够支持几家体现这一原则的公司。Peptone 将原子级生物物理学与超级计算结合，生成内在无序蛋白质的专有结构数据，Inductive Bio 则正在组装业内最大、最多样化的 ADMET 数据集之一，用于训练其 Beacon 模型，该模型最近在 OpenADMET-ExpansionRx 终点预测挑战中 370+份提交中名列第一 。Converge Bio 正在生成大规模数据集，用于训练和验证自有模型，部署给制药和生物技术客户用于抗体设计或序列优化; 而 Seismic 则采取了以流水线为先的方法，利用其 IMPACT 平台并行优化新型免疫生物制剂的多种类药特性。
We’re also seeing progress downstream in the drug development continuum. For example, NOETIK is assembling one of the most comprehensive datasets in oncology by pairing tumor multi-omics with longitudinal treatment outcomes, and Prima Mente is building whole-genome epigenetic and multi-omic data models applied to brain disease. These data-rich disease-specific foundation models aim to enable novel target and biomarker discovery, more precise virtual cell simulation perturbation models, and improved clinical trial design. 我们也看到药物开发连续体的下游进展。例如，NOETIK 正在通过将肿瘤多组学与纵向治疗结果配对，构建肿瘤学领域最全面的数据集之一，Prima Mente 则构建应用于脑疾病的全基因组表观遗传和多组学数据模型。这些数据丰富的疾病特异基础模型旨在实现新的靶点和生物标志物发现、更精确的虚拟细胞模拟扰动模型，以及改进临床试验设计。
Agentic AI across R&D workflows 2. 研发工作流中的代理人工智能
While the cost of bringing a drug to market has increased, the cost of computing has decreased exponentially since the 1950s, consistent with Moore’s Law. Tasks along the drug development continuum that are computationally expensive today will be dramatically cheaper within a few years, and companies that build their tech stack to be rapidly adaptable to the evolving capabilities of AI will find themselves with an increasingly substantial structural advantage over those that view AI as a fixed investment. 虽然药物上市成本上升 ，但自 20 世纪 50 年代以来计算成本呈指数级下降，这与摩尔定律一致。目前计算成本高的药物开发连续体任务，几年内成本将大幅降低，构建技术栈以快速适应 AI 不断演变能力的公司，将相较于将 AI 视为固定投资的企业，拥有越来越显著的结构性优势。
The evolution of computational drug discovery workflows is a useful lens for what this adaptability looks like in practice. While building proprietary molecular modeling and simulation tools in-house may have been a differentiator a decade ago, the abundance of off-the-shelf in silico tools has changed this defensibility narrative. Structure predictors, ADMET models, and molecular dynamics simulators have greatly matured and are now widely accessible through both closed-source architectures and open-source repositories, often making it more time and resource-efficient to strategically mosaic tools across this ecosystem rather than build from scratch. The same logic applies as new foundation models emerge, new training techniques evolve, and new hardware enables greater compute efficiency. 计算药物发现工作流程的演变是观察这种适应性在实际中表现的有用视角。十年前，自行构建专有分子建模和仿真工具或许是优势，但现成的计算机模型工具的丰富性改变了这一可辩护性的叙事。结构预测器、ADMET 模型和分子动力学模拟器已大幅成熟，现已广泛通过闭源架构和开源仓库访问，这使得在生态系统中战略性地拼接工具比从零构建更节省时间和资源。同样的逻辑适用于新的基础模型出现、新的训练技术演进以及新硬件带来的更高计算效率。
Cheaper compute has made long-context inference economically practical, enabling AI agents to synthesize over 1,000 papers and 40K lines of code in a single run. In combination with techniques that boost the accuracy and efficiency of AI, such as chain-of-thought reasoning and multi-agent frameworks, it has become increasingly realistic that AI can meaningfully compress the cost and time of the R&D lifecycle. 更便宜的计算使得长上下文推断经济实用，使 AI 代理能够在一次运行中合成超过 1000 篇论文和 4 万行代码。 结合提升人工智能准确性和效率的技术，如思维链推理和多智能体框架，人工智能能够有效压缩研发生命周期的成本和时间，这一现实日益增强。
Agentic AI scientists could mine preprint servers, patent filings, and public biological databases to surface non-obvious connections, generate novel hypotheses, perform in silico data analysis, design wet lab experiments, and write reports, all while maintaining team-wide research context and a historical record of experiments that empowers scientists to make smarter and faster decisions. 智能人工智能科学家可以挖掘预印本服务器、专利申请和公共生物数据库，挖掘不显而易见的联系，提出新假设，进行计算机模拟数据分析，设计湿实验室实验，撰写报告，同时保持团队范围的研究背景和实验历史记录，使科学家能够做出更聪明、更快速的决策。
Soon, it will be standard to adopt an AI operating system that spans the entire drug development process, leveraging AI’s ability to retain extensive context to unify analyses and results into a single research environment rather than leaving them siloed across disparate point solutions. 很快，采用涵盖整个药物开发流程的 AI 操作系统将成为标准，利用 AI 保留大量上下文的能力，将分析和结果统一到单一研究环境中，而非分散在不同点解决方案中。
A growing wave of companies is building toward this vision, including both startups focused purely on the life sciences and frontier labs like Anthropic, which now offers connectors to integrate Claude with platforms such as Benchling, PubMed, ChEMBL, ClinicalTrials.gov, and more. K-Dense and Edison Scientific are developing autonomous AI scientist platforms that can plan, execute, and iterate on complex, long-horizon research workflows end-to-end, from hypothesis generation to running computational experiments. Phylo is taking a complementary approach with its Integrated Biology Environment, a unified workspace where scientists can seamlessly collaborate with AI agents across their datasets and analytical pipelines without switching between fragmented interfaces. 越来越多的公司正朝着这一愿景努力，包括专注于生命科学的初创企业和像 Anthropic 这样的前沿实验室，后者现在提供连接器 ，将 Claude 与 Benchling、PubMed、ChEMBL、ClinicalTrials.gov 等平台集成。K-Dense 和 Edison Scientific 正在开发自主 AI 科学家平台，能够从假设生成到运行计算实验，从假设生成到运行计算实验，从设计、执行和迭代复杂的长期研究流程。Phylo 采用了互补的方法，推出了集成生物学环境，这是一个统一的工作空间，科学家可以无缝地与 AI 代理跨数据集和分析流程协作，无需在分散的界面间切换。
Companies like Potato and Convoke are building the operating systems for biopharma across early-stage drug discovery and downstream commercialization workflows, with Potato serving as the infrastructure to autonomously design and run experiments, and Convoke serving as a system of record and action to accelerate the regulatory and document-based workflows to bring drugs to market. 像 Potato 和 Convoke 这样的公司正在构建生物制药从早期药物发现到下游商业化工作流程的操作系统，Potato 作为自主设计和运行实验的基础设施，Convoke 则作为记录和行动系统，加速将药物推向市场的监管和文档工作流程。
Closed loop lab automation 3. 闭环实验室自动化
Even companies that utilize the most cutting-edge AI models run into the constraints of generating experimental data. Despite dramatic advances in structure prediction and molecular modeling, many in silico outputs, such as binding affinity predictions, still need to be validated in the wet lab before any downstream development decision can be made with confidence. Beyond that, in vivo efficacy is essentially unpredictable from first principles, with late-stage failures driven disproportionately by pharmacokinetic and toxicity properties that in silico models failed to flag. Given that experimental results are the ultimate source of biological ground truth, it’s crucial that these models continuously incorporate feedback from the wet lab to be anchored in accuracy. 即使是使用最前沿 AI 模型的公司，也会遇到生成实验数据的限制。尽管结构预测和分子建模取得了巨大进展，许多计算机生成的成果，如结合亲和力预测，仍需在湿实验室中得到验证，才能有信心做出后续开发决策。除此之外，体内疗效从基本原理起就难以预测，晚期失败主要由药物动力学和毒性特性驱动，而这些在计算机模型中未能明显显现。 鉴于实验结果是生物真实的最终来源，这些模型必须持续吸收湿实验室的反馈，以确保准确性。
Unfortunately, the experimental cycles that stand between a model's output and the data needed to update that model’s priors often take weeks to months. Wet lab experiments are slow, prone to failure, and dependent on skilled human labor, making them one of the biggest bottlenecks in shortening drug development timelines. The iterative design-test-make-analyze cycle that characterizes lead optimization can take up to three years itself and accounts for nearly a quarter of the total drug development timeline. These timelines are further extended by the reality that experimental validation is often outsourced to Contract Research Organizations (CROs), where coordination overhead, queue times, and data quality inconsistencies can add weeks or months to each iteration cycle. Bringing experimental capabilities in-house is increasingly necessary as it gives teams control over the context and caliber of data generation that makes closed-loop learning meaningful. 不幸的是，模型输出与更新先验数据之间的实验周期往往需要数周到数月。湿式实验室实验速度缓慢、易失败，且依赖熟练的人力，是缩短药物开发周期的最大瓶颈之一。设计-测试-制作-分析的迭代周期本身就可能持续长达三年，占整个药物开发时间线的近四分之一。实验验证常被外包给合同研究组织（CRO）的现实进一步延长，协调开销、队列时间和数据质量不一致可能为每个迭代周期增加数周甚至数月。将实验能力引入内部变得越来越必要，因为这让团队能够掌控数据生成的环境和质量，从而使闭环学习变得有意义。
Though Hamilton robots for liquid handling and Chemspeed platforms for automated synthesis have existed in labs for decades, they’re optimized for the high throughput of specific point tasks rather than the automation and integration of entire experimental workflows. Most lab automation today still requires significant human intervention to transfer materials between instruments, troubleshoot failures, and interpret results before the next experimental step can begin, compressing individual tasks without compressing the end-to-end cycle. 尽管汉密尔顿机器人用于液体处理和 Chemspeed 自动化合成平台在实验室中已存在数十年，但它们更注重高通量的特定点任务，而非自动化和整合整个实验流程。如今大多数实验室自动化仍需大量人工干预，以便在仪器间转移材料、排查故障并解读结果，才能开始下一个实验步骤，这样可以压缩单个任务，而不压缩端到端的周期。
In particular, automating robotic lab equipment has historically required dedicated automation engineers to configure the instruments and continuously write new scripts for different workflows. Natural language interfaces for robot control could effectively democratize automation capabilities, enabling scientists without any robotics or software engineering background to run, monitor, and iterate on experiments remotely and autonomously. Advancements in robotics and physical AI could further orchestrate the material and data transfer that is still being done by humans today. For example, vision-native systems can now autonomously read and interpret microscopy images of cells and feed structured data directly back into model pipelines without a scientist manually extracting and inputting the results. 特别是，自动化机器人实验室设备历来需要专门的自动化工程师配置仪器，并持续为不同工作流程编写新脚本。用于机器人控制的自然语言接口可以有效实现自动化能力的民主化，使没有机器人或软件工程背景的科学家能够远程自主地运行、监控和迭代实验。机器人技术和物理人工智能的进步可以进一步协调当今人类仍在进行的材料和数据传输。例如，视觉原生系统现在可以自主读取和解读细胞的显微镜图像，并将结构化数据直接反馈到模型流程中，无需科学家手动提取和输入结果。
Progress towards autonomous labs provides companies with significant leverage in both speed and operational spend. A model trained on five design-test-analyze cycles in the time a competitor completes one will compound its biological understanding far faster, and that compounding translates directly into better models, better molecules, and a structural advantage that is very difficult for those dependent on traditional CRO timelines to close. The companies building toward increased iteration speed through lab automation will also achieve greater data consistency, accuracy, and volume—reinforcing our first principle on the need for biology-native data at scale. 自主实验室的进步为企业在速度和运营成本上提供了显著优势。一个在竞争者完成一个周期内训练五个设计-测试-分析循环的模型，将更快地积累其生物学理解，这种复合直接转化为更好的模型、更好的分子，以及一种结构优势，而这种优势对于依赖传统 CRO 时间表的人来说很难实现。通过实验室自动化提升迭代速度的公司，也将实现更高的数据一致性、准确性和体积——这强化了我们关于大规模生物学原生数据需求的第一原则。
Companies across the lab automation landscape are pursuing this from different angles. Medra is building an instrument-agnostic robotics platform where general-purpose robots interact with existing lab equipment through physical controls and software interfaces. Automata takes a lab orchestration approach with its LINQ platform, providing modular hardware and software that connects disparate instruments into coordinated, end-to-end automated workflows. Dash Bio is using robotics to become a faster and more automated CRO that offers the speed and consistency that in-house automation provides. Lila Sciences represents one of the most vertically integrated approaches, building a fully automated lab for end-to-end drug discovery and development. 实验室自动化领域的公司从不同角度都在推进这一目标。Medra 正在构建一个仪器无关的机器人平台，通用机器人通过物理控制和软件接口与现有实验室设备交互。Automata 采用实验室编排方法，利用其 LINQ 平台，提供模块化硬件和软件，将不同乐器连接成协调的端到端自动化工作流程。Dash Bio 正在利用机器人技术成为一个更快、更自动化的 CRO，提供内部自动化所需的速度和一致性。Lila Sciences 代表了最垂直整合的方法之一，打造全自动化实验室，实现端到端的药物发现与开发。
Life sciences will run on AI 生命科学将基于人工智能运行
We believe that companies building large biology-native datasets, AI-centric development stacks, and lab automation platforms that power rapid closed-loop experimentation will enable and define the next generation of life science companies. 我们相信，构建大型生物学原生数据集、以人工智能为中心的开发栈和支持快速闭环实验的实验室自动化平台的公司，将推动并定义下一代生命科学公司。
We see this market organized across three interdependent layers. At the top sit companies generating data at the scale, modality, and fidelity AI requires to produce meaningful discoveries across the drug development continuum. Beneath them lie the physical and software infrastructure layers, including workflow and lab automation platforms, that compress timelines at every stage. Together, these three layers represent much of the value chain taking shape in AI-driven drug development, a core area of investment where we believe the next generation of life science companies will be built. 我们认为该市场分为三个相互依赖的层面。顶尖企业拥有 AI 所需的规模、模态和保真度数据，以实现药物开发全过程中有意义的发现。在这些层面之下，是物理和软件基础设施层，包括工作流程和实验室自动化平台，在每个阶段压缩时间表。这三层构成了人工智能驱动药物开发中正在形成的价值链，而我们相信下一代生命科学公司将在这里构建核心投资领域。
If you are building in any of these categories, or more broadly at the intersection of AI and the life sciences, we want to connect with the expert scientists, founders, and leaders in the industry. Reach out to Andrew Hedin (ahedin@bvp.com), Marla Jalbut (mjalbut@bvp.com), or Grace Dai (gdai@bvp.com). 如果你正在这些类别中的任何一个领域，或者更广泛地在人工智能与生命科学的交叉领域建设，我们希望与行业中的专家科学家、创始人和领导者建立联系。请联系 Andrew Hedin（ahedin@bvp.com 年）、Marla Jalbut（mjalbut@bvp.com 年）或 Grace Dai（gdai@bvp.com 年）。
https://www.bvp.com/atlas/building-biology-native-data-infrastructure-for-the-ai-era