Ailomics Therapeutics Advances First-in-Class Drug Pipeline for Immune Diseases Powered by AI-Driven Target Discovery

In recent years, the AI pharmaceutical industry has gained renewed momentum, capturing significant attention from the capital market. Tech giants such as Microsoft, Google, and Nvidia are now viewing biotechnology as the next frontier for AI. According to incomplete statistics, in 2023 alone, Nvidia has invested in more than 10 AI pharmaceutical startups. World-renowned international pharmaceutical companies are also embracing AI, highlighting its crucial role in the research, production, and commercialization of innovative drugs.

Especially this year, the NVIDIA GTC 2024 conference has once again brought "AI + pharmaceuticals" into the spotlight, with AI drug discovery being labeled as the "next golden track." Additionally, Google DeepMind and Isomorphic Labs launched AlphaFold 3, a new generation of AI biomolecular structure models. The advent of this tool has provided tremendous momentum for drug development. AI drug discovery, now in the limelight, is once again demonstrating unprecedented developmental potential.

Dr. Qing Zhang, Founder and CEO of Ailomics Therapeutics Co., Ltd ("Ailomics")"One of the key questions for AI pharmaceutical companies to consider is what kind of problems, if solved, would truly allow AI to create value."

As a company布局AI制药领域,Ailomics is leveraging innovative technologies such as AI to overcome the core challenges in drug discovery—identifying the key mechanisms and targets driving disease progression—and focusing on the development of novel drugs for immunological diseases with significant clinical needs.。

To date, Ailomics has established a new drug research and development center in Shanghai's Zhangjiang Pharm Valley and built five technology platforms. Currently, the company has discovered multiple new mechanisms and new targets driving disease progression and has initiated new drug R&D projects. It is actively advancing the PCC (Preclinical Candidate Compound) confirmation for one of its self-developed projects.

Ailomics was founded in July 2022. Just two months later,The company announced the completion of an angel round of financing worth tens of millions of RMB led by Qiming Venture Partners.Behind Ailomics' rapid gain of recognition from renowned capital lies an outstanding core team with over a decade of experience in new drug research and development at leading international pharmaceutical companies.

Dr. Qing Zhang, Founder and CEO of AilomicsDr. Zhang Qing has worked for 14 years at GlaxoSmithKline (GSK), Amgen, and Roche, supporting the advancement of multiple drug development projects and is a co-inventor of three clinical candidate drug molecules. Importantly, Dr. Zhang has over 24 years of experience in computational biology research and is familiar with the application of bioinformatics, AI, and molecular modeling in disease biology research.

Based on years of industry observation, Dr. Zhang Qing found that the vast majority of pharmaceutical companies search for new targets through traditional methods such as reviewing literature, which leads to high target homogeneity and low clinical success rates. Meanwhile, innovative technologies like sequencing and AI have been thriving in recent years.

"So I want to change the approach and establish a company that utilizes advanced sequencing and AI technologies to systematically understand disease biology, thereby discovering new targets and developing therapeutic drugs." Consequently, Dr. Zhang Qing successively reached out to Dr. Ding Yao and Dr. Ban Ting, who also have deep expertise in the biopharmaceutical field. The three of them co-founded Ailomics.

Among them,Dr. Ding Yao, Co-founder of the CompanyPreviously worked at Hengrui, Amgen, and BeiGene, with over 10 years of experience in antibody development, and was one of the early R&D leaders for the PDL-1-targeted therapeutic antibody (SHR-1316).Co-founder of the company, Dr. Ban TingPreviously worked at Roche and Amgen, with 10 years of experience in biology and translational medicine research. Additionally, has experience in early-stage equity investment and incubation in biopharmaceutical companies.

Core Team Members of Ailomics Source: Ailomics

In general, AI pharmaceutical companies have diverse data sources, including external open resources, data shared by partners, and self-developed data. Among these, published literature data, open target libraries, and other public data resources are commonly used data sources for companies.

As a startup dedicated to the research and development of innovative drugs, Ailomics has taken a unique path in drug development.From bedside to bench and to bedside againThis is the new drug R&D model practiced by Ailomics. It starts with patient samples and clinical data, using AI-enhanced bioinformatics analysis to identify key signaling pathways that drive disease progression and potential innovative drug targets. Subsequently, in vitro 3D disease models derived from clinical samples are constructed, and candidate targets are validated and molecules screened using both human-derived and animal disease models. First-in-class candidate drugs are then developed and tested in clinical trials. Ultimately, this process leads to the development of innovative, safe, and effective drugs for patients.

The realization of this new drug R&D model is inseparable from the support of technology platforms. After completing its angel round of financing, Ailomics established five technology platforms—computational, organ-on-a-chip, in vivo pharmacology, in vitro pharmacology, and biologics. Among these, the computational platform is the most crucial. Based on this platform, the R&D team analyzes multi-omics data such as single-cell transcriptomes from patients to identify key cell types and intercellular communication responsible for disease clinical manifestations, selecting important, safe, and novel targets from them.

Relying on the technical platform and team advantages,Ailomics has currently discovered multiple new targets for immune diseases, and the self-developed projects underway have already produced macromolecules with biological functions, which have been preliminarily validated in animal models.。

In order to accelerate the R&D and commercialization process of candidate products, Ailomics has formulated a "multiple approaches" development strategy. Dr. Zhang Qing mentioned, "Ailomics is positioned as a pipeline company based on multiple technology platforms, so we have both self-developed pipelines and actively expand external collaborations."

Therefore, in addition to independently developing and commercializing drugs,Ailomics actively seeks business cooperation opportunities with multinational pharmaceutical companies to co-develop therapeutic drugs and with in vitro diagnostic companies to co-develop companion diagnostics, exploring and developing innovative therapeutic drugs through pipeline BD, collaboration licensing, and other methods.。

In recent years, major pharmaceutical companies have been addressing challenges such as the patent cliff, intensified market competition, and rising R&D costs by laying off staff, closing plants, halting clinical trials, and cutting pipelines. "Cost reduction and efficiency enhancement" has become a key development phrase for almost all pharmaceutical enterprises. Against this backdrop, the significant role of AI in innovative drug development has once again come into focus. According to a research report released by Huachuang Securities, all of the world’s top 20 pharmaceutical companies have already positioned themselves in AI + new drug R&D. From 2023 to February 2024, multinational corporations (MNCs) with disclosed amounts in projects involving AI pharmaceutical companies showed a total potential cooperation value exceeding 12 billion US dollars, with an average cooperation amount of 840 million US dollars.

Despite the fact that no drug primarily driven by AI has been approved for marketing so far, with the continuous iteration of algorithms and models, as well as the adjustment and optimization of the AI drug development operational model, AI will continue to improve the efficiency and quality of drug discovery. Boston Consulting Group (BCG) recently published a study. The study conducted a quantitative analysis of the clinical pipelines of 100+ AI pharmaceutical companies, with a particular focus on the clinical success rate of AI-generated molecules. Based on the statistical data from the research,Researchers found that the probability of AI molecules achieving success in all clinical stages will increase from 5%-10% to 9%-18%, nearly doubling the success rate.。

Regarding the future development of AI in the field of drug research and development, Dr. Zhang Qing said, "'"Data, Algorithms, Validation, and Implementation" Are Four Essential Elements in the Development of AI-Driven Pharmaceutical EnterprisesAmong them, validation and implementation, that is, achieving a commercial closed loop, are the embodiment of AI's real value. Based on proprietary patient multi-omics data and AI algorithms combined with professional knowledge, analyzing and understanding complex disease mechanisms from a systems biology perspective are key for Ailomics to improve the success rate of clinical translation and rapidly advance commercial implementation."

Therefore,To accelerate the advancement of candidate projects into clinical stages and promote the iteration of the computing platform, Ailomics has recently initiated its Pre-A round of financing.。