Generare – the techbio startup generating novel, high-quality molecular data for drug development by decoding what life wrote – has raised €20M in Series A funding co-led by Alven and Daphni with participation from all existing investors, including Galion.exe, Teampact Ventures and VIVES Partners. The funding will be used to increase Generare's .

Generare——这家通过解读生命密码来生成新颖、高质量分子数据以用于药物开发的技术生物初创公司——在由Alven和Daphni共同领投的A轮融资中筹集了2000万欧元，所有现有投资者均参与其中，包括Galion.exe、Teampact Ventures和VIVES Partners。这笔资金将用于增强Generare的 。

drug discovery

药物发现

compound library, grow its world-class team and scale its proven discovery platform to meet growing demand from pharmaceutical giants and agrochemical companies.

化合物库，壮大其世界级团队，并扩展其经过验证的发现平台，以满足制药巨头和农化公司不断增长的需求。

Decoding what life wrote

解码生命所写下的信息

Founded in 2023 by CEO Guillaume Vandenesch and CSO Dr Vincent Libis, Generare is building the highest quality dataset of untapped molecular chemistry: a growing library of novel compounds characterized for structure, biological activity and drug potential.

Generare 由首席执行官纪尧姆·范德内什 (Guillaume Vandenesch) 和首席科学官文森特·利比斯 (Vincent Libis) 博士于 2023 年创立，正在构建一个高质量的未开发分子化学数据集：一个不断增长的新化合物库，这些化合物已经过结构、生物活性和药物潜力的表征。

Drug discovery has operated inside the same narrow chemical space for decades, not by choice but because the data outside it didn't exist. Encoded in microbial genomes is a reservoir of molecular chemistry that evolution has spent three billion years writing – approximately 97% of which remains unread.

药物发现几十年来一直在相同狭窄的化学空间内进行，这不是出于选择，而是因为外部的数据并不存在。微生物基因组中蕴藏着一个由进化历经三十亿年写就的分子化学宝库，其中大约97%仍未被解读。

Generare is generating that missing data, at a quality and scale the field has never seen, making it genuinely usable for drug development..

生成缺失的数据，其质量和规模是该领域前所未见的，真正可用于药物开发。

A new AI model can accurately predict human response to novel drug compounds

一种新的人工智能模型可以准确预测人类对新型药物化合物的反应。

The technique could significantly accelerate drug discovery and precision medicine

该技术可以显著加速药物发现和精准医学的发展。

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Generare's proprietary high-throughput cloning and sequencing technology, validated through ERC-funded academic research and multiple peer-reviewed publications, decodes this cryptic chemistry at scale. It screens tens of thousands of microbial genomes, identifies gene sequences most likely to produce bioactive molecules, expresses them, and characterizes the resulting compounds for structure, biological activity and drug potential..

Generare公司专有的高通量克隆和测序技术，通过欧洲研究委员会（ERC）资助的学术研究及多篇同行评审出版物验证，能够大规模解码这种隐秘的化学机制。该技术筛选数万个微生物基因组，识别最有可能产生生物活性分子的基因序列，将其表达并表征所得化合物的结构、生物活性和药物潜力。

Generare specialises in small molecules – the class of compounds behind the world’s best-known medicines – and each new small molecule it uncovers becomes a new data point, feeding a dataset that grows with every cycle.

Generare 专注于小分子——这一世界最知名药物背后的化合物类别——它发现的每一个新的小分子都成为一个新的数据点，为随着每个周期不断增长的数据集提供支持。

Guillaume Vandenesch, CEO and co-founder, Generare said: ”Drug discovery has a data problem. The entire field trains its models on the same recycled chemistry and expects different outcomes. The bottleneck is not algorithms, it is the absence of genuinely novel, high-quality molecular data and we’re solving that by building the largest proprietary dataset.

纪尧姆·范德内施，Generare的首席执行官兼联合创始人表示：“药物发现面临数据问题。整个领域都在使用相同的老化化学数据训练模型，却期望得到不同的结果。瓶颈不是算法，而是缺乏真正新颖、高质量的分子数据，我们正在通过构建最大的专有数据集来解决这一问题。”

of cryptic small molecules. These molecules, shaped by 3 billion years of evolution and with drug properties no synthetic library can match, will fuel the next century of drug discovery.”

这些分子经过30亿年的进化，具有任何合成库都无法匹敌的药物特性，将推动下一个世纪的药物发现。

Molecular data no AI model has ever been trained on

分子数据，从未有AI模型接受过训练。

This compounding cycle is what makes Generare's dataset so valuable to partners, including some of the world's most recognizable pharmaceutical companies, and is increasingly critical as AI reshapes drug discovery. AI can accelerate the search for new medicines, but it can’t create truly novel chemistry to learn on.

这种复合循环使得 Generare 的数据集对包括一些全球最知名的制药公司在内的合作伙伴极具价值，并且随着人工智能重塑药物发现，这一数据集变得越来越重要。人工智能可以加速新药的搜索，但无法创造真正新颖的化学结构供其学习。

Without novel data, models will keep converging on the same outcomes, regardless of their sophistication. Feed them genuinely new structures selected by evolution with the corresponding biological activity, and the impact multiplies..

如果没有新颖的数据，无论模型多么复杂，它们都会不断收敛到相同的结果。如果提供由进化选择的真正新结构及其相应的生物活性，影响就会倍增。

Market-leading traction

市场领先的牵引力

In just three years, Generare has become the prominent platform generating more evolution-based molecular data than anyone else. In 2025, all players combined discovered a few dozen new molecules; Generare alone revealed over 200. These molecules are now being used by research laboratories to develop new medicines for life threatening diseases..

仅仅三年时间，Generare 已成为生成基于进化的分子数据的领先平台，其数据量超过其他任何人。2025 年，所有参与者合计发现了几十种新分子；而 Generare 仅凭一己之力就揭示了 200 多种。这些分子现正被研究实验室用于开发治疗危及生命的疾病的新药物。

The funding will enable Generare to scale this capacity 10-fold by 2027 to 2,000+ molecules, with a view of growing past 10,000 over time and effectively compressing what took a century of discovery into a matter of years. It will enable the near-doubling of Generare's 25-strong team of computational biologists, chemists, synthetic biologists, technicians and engineers, drawn from France, the UK, the US, Germany, Australia and beyond.

这笔资金将使Generare到2027年将其能力扩大10倍，达到2000多个分子，并计划随着时间的推移突破10,000个分子，从而有效地将一个世纪的发现压缩到短短几年内完成。此外，这还将使其由计算生物学家、化学家、合成生物学家、技术员和工程师组成的25人团队几乎翻倍，团队成员来自法国、英国、美国、德国、澳大利亚及其他地区。

Among those advising the company are Dr Frank Petersen, former Executive Director of Novartis's Natural Products Chemistry Department, and Professor Nadine Ziemert, one of Europe's foremost experts of microbial biosynthetic gene clusters..

公司顾问包括诺华天然产物化学部前执行董事弗兰克·彼得森博士，以及欧洲微生物生物合成基因簇领域的顶尖专家之一纳丁·齐默尔曼教授。

Dr Vincent Libis, CSO and Co-Founder at Generare said: 'Nature has been the n°1 source of innovative modes of action for drugs and we’ve only scratched the surface of its potential. We’re building the infrastructure to change that with the largest commercial library of evolution-derived molecules in the world, a dataset that improves with every cycle, and a platform that can supply companies with genuinely new starting points for drug discovery.'.

Generare的首席科学官兼联合创始人文森特·利比斯博士表示：“大自然一直是药物创新作用模式的首要来源，而我们仅仅触及了其潜力的表面。我们正在建设基础设施以改变这一现状，拥有全球最大的商业进化衍生分子库、一个在每个周期中不断改进的数据集，以及一个能够为公司提供真正全新药物发现起点的平台。”

Maria Tahri, Bio Investor at Alven said: 'While techbio is accelerating drug discovery, Generare is pushing it further, reopening nature’s pharmacy at industrial scale. By harnessing genomics, synthetic biology and machine learning, the company generates proprietary datasets on unknown mechanisms of action, unlocking drug opportunities others cannot see and building a moat that is already attracting strong industry interest.”.

玛丽亚·塔赫里，阿尔文的生物投资者表示：“虽然技术生物正在加速药物发现，但Generare更进一步，以工业规模重新开启自然的药房。通过利用基因组学、合成生物学和机器学习，该公司生成关于未知作用机制的专有数据集，解锁其他公司无法看到的药物机会，并建立起已经吸引强烈行业兴趣的护城河。”

Paul Bazin, Partner at daphni said: 'From penicillin onwards, modern pharma was born from natural products. Yet for decades the industry has been constrained by its inability to access new evolution-derived molecules. Generare removes this fundamental bottleneck. By unlocking the remaining 97% of natural products encoded in microbial genomes, they’re not just expanding chemical space, they’re redefining it.

保罗·巴赞，达芙妮公司的合伙人表示：“从青霉素开始，现代制药业就源于天然产物。然而，几十年来，该行业一直受限于无法获取新的进化衍生分子。Generare解决了这一根本瓶颈。通过解锁微生物基因组中编码的剩余97%的天然产物，他们不仅在拓展化学空间，更是在重新定义它。”

This marks a profound shift for the industry: the challenge is no longer designing new molecules, but understanding how to best translate this reservoir of biology into breakthroughs across healthcare and agriculture.”.

这标志着该行业的一个深刻转变：挑战不再是如何设计新分子，而是如何理解将这一生物学宝库转化为医疗保健和农业领域的突破的最佳方式。”

https://www.bionity.com/en/news/1188482/generare-raises-20m-with-molecular-data-no-ai-model-in-the-world-has-ever-seen.html

https://www.bionity.com/zh/news/1188482/generare-raises-20m-with-molecular-data-no-ai-model-in-the-world-has-ever-seen.html

Artificial intelligence identifies anti-aging drug candidates targeting ‘zombie’ cells

人工智能识别出针对“僵尸”细胞的抗衰老药物候选者

New platform has potential to fuel advances in senolytic anti-aging compounds and longevity research

新平台有望推动衰老抑制化合物和长寿研究的进展

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我们到底多大了？

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Series A financing

A轮融资

drug discovery

药物发现

artificial intelligence

人工智能

molecules

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molecular chemistry

分子化学

synthetic biology

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machine learning

机器学习

genomics

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