Exclusive: Elem Biotechnology Completes Tens of Millions in Angel Financing to Accelerate Deployment of Virtual Cell Data Infrastructure

VCBeat learned that in October 2026, Elem Biotechnology completed an angel financing round worth tens of millions of yuan. HONGHUI CAPITAL led the investment, with Xinghua Dingli and Hangzhou Runmiao Fund participating as co-investors. Chuanyue Cycle served as the financial advisor for this round.

Elem Biotechnology specializes in the production of high-throughput cell editing and perturbation sample data, striving to become a data infrastructure provider for the upstream segment of the Artificial Intelligence Virtual Cell (AIVC) industry. Leveraging its independently developed genome-wide arrayed gene perturbation libraries and fully automated drug perturbation cell screening platform, the company provides standardized perturbation samples and experimental data to virtual cell enterprises, innovative pharmaceutical companies, and scientific research institutions.

As the first enterprise in China to achieve large-scale production of arrayed whole-genome editing libraries, the Elem Biotechnology team has completed the construction and wet-lab validation of an arrayed library covering perturbations of 19,883 human genes. By leveraging self-developed core reagents and an independently integrated automation platform, the company has significantly reduced per-edit costs and delivery lead times, thereby advancing arrayed gene perturbation libraries from research applications to large-scale, industrialized production and use.

As the field of virtual cells advances, industry competition is expanding from algorithms and computing power to high-quality experimental data. Currently, many models rely on relatively centralized public databases, which lowers the barrier to entry but also leads to issues such as homogenized training data and insufficient model differentiation, thereby limiting the models' adaptability to specific disease and drug development tasks.

A vast amount of publicly available single-cell data consists of unmanipulated, static observations. While such data can describe cellular states, they struggle to answer questions like "how cells change after a specific gene is knocked out or a particular drug intervention is applied." To address scenarios such as target discovery, drug efficacy prediction, and combination therapy, numerous virtual cell companies are actively supplementing their datasets with proprietary perturbation data that features well-defined intervention conditions, experimental controls, and quality validation. Furthermore, the validity of data and its alignment with specific application scenarios are increasingly becoming critical factors in the commercialization of these models.

In September 2026, the Elem team officially released the large-scale gene perturbation transcriptomic dataset "ELEM-1200RS," comprising transcriptomic data from approximately 1,200 samples across more than 60 wild-type cell line backgrounds, involving 914 independently perturbed genes. The dataset was generated using arrayed CRISPR RNP knockout technology combined with bulk RNA-seq to capture cellular transcriptomic responses under various gene perturbation conditions.

"ELEM-1200RS" links explicit genetic interventions with cellular responses, enabling applications in gene function research, perturbation effect prediction, and model evaluation across diverse cellular contexts. Its standardized format is compatible with the data structure of the 2nd International Virtual Cell Challenge (Virtual Cell Challenge 2026), facilitating model training, validation, and comparison among different teams.

According to the company, since the "ELEM-1200RS" dataset was released as open-source and made freely available on the Hugging Face platform on September 30, 2026, it has received over 300 usage applications from virtual cell research and development teams both in China and abroad within two weeks by October 15, garnering positive feedback. Notably, the top three winners of the First International Virtual Cell Challenge signed a "Priority Use Agreement" with the Elem team prior to the dataset's open-source release and have been assisting the Elem team in continuously optimizing and iterating the dataset.

Elem Biotechnology is advancing the development of "ELEM-600PR," a quantitative proteomics dataset comprising approximately 600 samples with dual perturbations of genes and drugs, to explore comparative analyses across multi-modal data types, including traditional transcriptomics, single-cell sequencing, and proteomics. The company is providing customized, proprietary gene and drug perturbation dataset services to corporate clients and research institutions.

Virtual cell modeling aims not only to identify "which genes are associated with cellular functions" but also to learn "how cells change following genetic intervention." This requires accurate correspondence between gene perturbation conditions and detection results. CRISPR-based gene perturbations primarily follow two approaches: pooled libraries and arrayed libraries. Pooled libraries offer lower costs and higher throughput, making them suitable for large-scale initial screening; they currently represent the predominant sample format for gene perturbation coupled with single-cell sequencing. In contrast, arrayed libraries employ an independent "one well, one gene" perturbation strategy, directly linking targets to experimental outcomes. This approach mitigates issues common in traditional pooled libraries, such as cross-interference between different gene perturbations, which often lead to reduced data quality and elevated noise levels. Furthermore, arrayed libraries facilitate integration with proteomic and metabolomic analyses—where samples cannot be labeled with proteins or small molecules—as well as with high-content imaging that combines simultaneous gene and drug perturbations. This enables the generation of traceable, multidimensional data capturing "gene and/or drug perturbation–cellular response" relationships, thereby helping AI models learn the continuous changes in cellular states post-intervention. While both approaches have their respective application scenarios, arrayed libraries offer unique advantages in complex phenotypic assays and multimodal data acquisition. Amid the growing trend of incorporating mass spectrometry data, such as proteomics, into Artificial Intelligence Virtual Cell (AIVC) model construction, arrayed libraries are becoming the preferred format for gene perturbation samples.

The challenges of array libraries lie in their high construction costs, complex workflows, and difficulties in large-scale production. Through long-term systematic optimization, including the accumulation of whole-genome libraries, in-house production of core reagents, and experimental automation, the Elem team Reduced production costs, enabling array-based perturbation to more efficiently serve the data requirements of virtual cells.

Meanwhile, Elem Biotechnology adopts an independent perturbation technological approach, continuously accumulating validated gene-editing reagents, engineered cells, and supporting raw materials, thereby gradually forming standardized products with reproducible supply capabilities. As the virtual cell industry develops, Elem's cell editing costs continue to decline, meeting the future demands for the preparation and data production of ultra-large-scale, multi-gene perturbation samples.

Following the completion of this funding round, Elem Biotechnology will continue to deepen its strategic layout in high-throughput cell editing services, automated equipment integration and data production, as well as standardized cell line products. The company aims to provide continuous support to virtual cell enterprises and innovative pharmaceutical companies, covering the entire workflow from "perturbation samples and experimental data to subsequent validation." With the ongoing accumulation of high-quality data and off-the-shelf reporter cell assets, Elem Biotechnology seeks to accelerate the practical application of virtual cell technology in drug development.