PerturbMind AI Completes Tens of Millions in Funding, Releases Virtual Cell Model AlphaCell 1.0 Pro

Using world models for AI drug discovery, Tongji professor raises tens of millions of yuan. ZhiDongXi Download the client for exclusive early access. October 30, 2026, 16:26:36 From Beijing

ZhiDongXi, October 30 — Yesterday, PerturbMind AI, a rising domestic AI for Science company, announced the completion of tens of millions of yuan in angel and angel+ funding, led by Qingsong Capital, with Tongyang Fund and several pharmaceutical companies participating, officially emerging from stealth mode.

The company focuses on a next-generation AIDD (AI drug discovery) innovation paradigm driven by geometric world models, exploring an R&D model centered on inferring the states of living systems.

In this announcement, PerturbMind AI also released AlphaCell 1.0 Pro, the world's first virtual cell state inference model under its digital life world model ecosystem PerturbSeek.

The model uses a geometric world model as its underlying architecture, with a standard parameter count of 1B that can be adjusted according to application needs. It can be trimmed down to a 300M lightweight model, AlphaCell 1.0 Flash, or scaled up to a 50B ultra-large-scale model, AlphaCell 1.0 Mega, adapting to the different deployment needs of research institutions, small and medium-sized pharmaceutical companies, and large multinational pharmaceutical companies.

Unlike traditional AI drug discovery, PerturbMind AI hopes to use geometric world models to learn the dynamic changes of living systems under different intervention conditions, infer disease state transitions and system responses after drug intervention, and thereby provide technical support for the analysis of complex disease mechanisms, new target discovery, and R&D of novel-modality drugs.

1. Shifting from target screening to state inference, releasing a virtual cell model

AlphaCell 1.0 Pro, released by PerturbMind AI this time, is an upgraded version of the company's virtual cell model AlphaCell 1.0 Preview.

On March 5 this year, the PerturbMind AI team led by founder and CEO Chuo Guohui published an AlphaCell-related preprint paper on bioRxiv, introducing the model's method for simulating perturbation-induced dynamic changes in cells based on a geometric world model architecture.

▲ The PerturbMind AI founding team published the AlphaCell 1.0 Preview paper, source: bioRxiv

The newly released AlphaCell 1.0 Pro has a standard parameter count of 1B and features elastic parameter adjustment capability, allowing the model scale to be dynamically adjusted according to customers' computing power conditions and R&D needs.

The model can be trimmed down to 300M parameters to form the lightweight version AlphaCell 1.0 Flash, meeting the lightweight and private deployment needs of hospitals, research institutions, and small and medium-sized pharmaceutical companies; it can be scaled up to 50B parameters as AlphaCell 1.0 Mega, providing large multinational pharmaceutical companies (MNCs) with system-level perturbation trajectory inference capabilities in complex scenarios.

On the commercialization side, PerturbMind AI plans to offer on-device service subscriptions, private deployments, and joint drug discovery pipeline development around models of different scales.

In terms of technical approach, PerturbMind AI hopes to change the target-centric R&D model of traditional AI-driven Drug Discovery (AIDD).

Traditional AIDD usually searches for candidate drug molecules that can bind to known targets, similar to finding the right key based on a known lock. But for complex diseases, a single target may not explain the entire process of disease development, and drug intervention may also trigger changes at other biological system levels.

PerturbMind AI said that disease is essentially a state shift produced after the original homeostasis of a living system is disrupted. Therefore, drug discovery requires not only finding molecules that can act on specific targets, but also understanding how disease states form and what systemic changes different interventions may bring.

Based on this idea, the company has shifted its R&D focus from individual targets to state changes across the entire living system, hoping to use model inference to identify interventions that can restore disease states to normal.

2. Geometric world models build the technical foundation, exploring inference of living systems

Around the above technical approach, PerturbMind AI has built the PerturbSeek digital life world model ecosystem, with perturbation omics, geometric deep learning, and geometric world model theory as its three major technical pillars.

Among them, perturbation omics mainly studies the changes produced in biological objects such as genes and proteins after different interventions, providing a data foundation for understanding the dynamic responses of living systems.

Geometric deep learning is used to learn the complex structures behind biological data. PerturbMind AI said that by building geometric deep learning algorithms, it learns and models the Biological Latent Manifold, mapping complex biological data into latent space to represent different cell states and their intrinsic relationships.

On this basis, the geometric world model further learns the states of living systems and the laws of their changes. It incorporates cell states and external interventions into a unified model framework, simulating changes in cell states under the action of different factors such as genes and drugs, and thereby inferring the dynamic evolution of living systems.

Simply put, this technical approach aims for its model not only to identify correlations between a certain intervention and biological changes, but also to learn how living systems change under different conditions and infer the possible outcomes of specific interventions.

On this basis, PerturbSeek aims to combine specific diseases and clinical constraints to identify interventions with potential therapeutic value, rather than merely screening candidate molecules around known targets.

For model training, PerturbMind does not advocate simply scaling up parameter size and data volume. The company notes that model training in the life sciences may not be able to directly apply the scaling laws of the general AI field, and that model parameter counts need to match actual task requirements, predictive performance, and computational cost.

Therefore, PerturbMind seeks to find the balance point between model performance and R&D cost, namely the parameter sweet spot, and to improve the model's generalization ability under limited-sample conditions by optimizing the underlying architecture and training efficiency.

III. A startup founded by a Tongji research team, working with multiple pharmaceutical companies to advance technology implementation

Dr. Chuo Guohui, founder and CEO of PerturbMind, graduated from the Digital Life Agent Laboratory at Tongji University. Chuo Guohui said that the company's underlying technological innovation is to some extent inspired by the AI scientific research models in Google's DeepMind Alpha series, and that it hopes to translate cutting-edge world model technology into platform solutions that pharmaceutical companies and clinical settings can use.

▲ Chuo Guohui, founder and CEO of PerturbMind; source: Tongji University official website

In his view, living systems have complex dynamic characteristics, and models need not only to understand the relationships between different biological objects but also to characterize the state transitions of the system after external intervention. PerturbMind hopes to use geometric world models to establish state representations and state transition models of digital living systems, and to translate inference results into drug R&D leads.

Professor Liu Qi, chief scientist of the company and head of the Digital Life Agent Laboratory at Tongji University, said that R&D of new-modality drugs requires holistic understanding and modeling of complex living systems. Traditional AIDD has accumulated considerable technology in molecular design for known targets, but still faces challenges in deciphering complex disease mechanisms and discovering new targets.

▲ Liu Qi, chief scientist of PerturbMind and head of the Digital Life Agent Laboratory at Tongji University; source: Tongji University official website

Liu Qi said that PerturbMind hopes to advance drug R&D from mere molecular-level modeling to understanding and inference at the level of living system states, and then form a closed loop through experimental validation and data feedback, establishing a connection between digital models and real living systems.

In terms of research accumulation, the PerturbMind team has collaborated with domestic and international pharmaceutical companies and contract research organizations (CROs) such as Roche, AstraZeneca, and WuXi AppTec. The team's related research results have been published in journals such as Nature Methods, Nature Machine Intelligence, Nature Computational Science, and Cancer Cell, and have received the WuXi AppTec Life Chemistry Research Award and the Wu Wenjun Artificial Intelligence Natural Science Award, among others.

On the industrial collaboration front, PerturbMind said it has signed a strategic cooperation agreement with a leading domestic Chinese chip company, and the PerturbSeek engine has completed full-stack adaptation to domestic GPU chips.

In addition, the company has signed cooperation agreements with pharmaceutical R&D companies in specific domestic fields, is developing and deploying multiple prototype systems, and is building data and experimental feedback loops around specific application scenarios.

Conclusion: As the living-world model moves toward industry, when will its actual R&D value materialize?

However, whether world models can truly understand complex living systems still needs to be tested through continuous experimental validation and actual drug R&D projects.

For PerturbMind, how to translate model predictions into verifiable drug R&D leads and further advance new target discovery and new-modality drug R&D is the question it needs to answer for subsequent commercialization.

Source: PerturbMind

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