AI4S Company AIPher Completes Hundred-Million-Yuan Angel Round, Building an AI-Driven Next-Generation Drug Discovery Engine

AIPher, an AI-native drug discovery company, recently announced the completion of a RMB 100 million angel financing round. The round was led by Xianghe Capital, with participation from LINKX Capital (under Zhipu AI) and a well-known industrial fund. The funds will primarily support the continued development of drug foundation models, represented by DrugCLIP, and the PharmAgents AI-driven drug discovery engine. Additionally, AIPher aims to establish a multimodal closed-loop system integrating computational and experimental drug discovery, while advancing its proprietary and collaborative innovative drug pipelines. The most advanced pipeline has already entered the pre-PCC (pre-preclinical candidate compound nomination) stage.

AIPher is an AI-native pharmaceutical R&D company dedicated to redefining the development of innovative drugs through artificial intelligence. The company’s core technology stems from the years of accumulated expertise of Professor Lan Yanyan’s team at the Institute for Artificial Industry Research (AIR) of Tsinghua University in the fields of AI for Science and drug discovery. The core technologies were developed by Professor Lan Yanyan, Deputy Director and Chief Researcher of the Institute for Artificial Industry Research at Tsinghua University, and the team led by Academician Zhang Yaqin, Chair Professor of Intelligent Science and Founding Director of the Institute for Artificial Industry Research at Tsinghua University.

For a long time, new drug development has often been a protracted journey, consistently facing the core challenges of lengthy cycles, high costs, and low success rates. In recent years, AI has been gradually applied to various stages of R&D, including protein structure prediction, virtual screening, molecular generation, and property prediction. However, most applications remain confined to “using AI to optimize a specific step within the traditional R&D workflow.” Yet, drug development is not essentially a mere aggregation of isolated prediction tasks; rather, it is a continuous, iterative process of exploration, decision-making, validation, and learning.

It is precisely based on this understanding that AIPher anchors its core capabilities in three dimensions:

Exploration—With drug foundation models such as DrugCLIP as the cornerstone, continuously expanding the boundaries of AI’s coverage over the vast chemical space. Related work has been published in Science, marking a key breakthrough in AI’s capability to explore the drug molecular space, and laying the foundation for the subsequent discovery of truly novel candidate molecules.

Decision-Making—Leveraging the AI-driven drug R&D agent PharmAgents, we integrate multi-source scientific evidence with outputs from specialized models to make informed, complex R&D decisions within an immensely vast possibility space, thereby driving real-world experimental validation. In the near term, the team will release a technical report and a systematic benchmark for PharmAgents, marking the first quantitative assessment of its complex decision-making capabilities in real-world drug discovery tasks. Concurrently, multiple real-world project cases—in which PharmAgents independently formulated R&D hypotheses and has advanced to the experimental validation stage—will be disclosed sequentially.

Evolution—Continuously transforming experimental results, R&D processes, and expert feedback into learning signals for models and agents, while proactively identifying the most valuable questions to validate next, thereby enabling the entire R&D system to continuously learn and evolve through real-world projects.

If the algorithms in academic papers fail to genuinely engage with molecular entities in the laboratory and real-world patients, they will remain mere theoretical assumptions on paper. From its inception, AIPher has chosen to embed AI technology within authentic drug discovery and development scenarios, progressing from computational screening to experimental validation and continuous optimization, thereby driving the advancement of its independently innovated drug pipeline.

Currently, the company’s most advanced First-in-Class (FIC) pipeline has entered the Pre-PCC stage. From algorithms described in research papers to the actual identification of promising molecules in the laboratory, and further to the gradual formation of innovative drug pipelines, the team is committed to demonstrating that AI can truly penetrate the core process of drug discovery, rather than merely enhancing the efficiency of a single computational step.

In the long run, AIPher aims to build an AI Agent-driven “Virtual Pharma” — enabling AI to conduct broad exploration, make autonomous decisions, and continuously evolve, thereby shifting the discovery and development of innovative drugs from “labor-intensive” to “intelligence-intensive.”

In terms of business model, AIPher focuses on Co-development as its core strategy. The company engages in deep collaborations with large pharmaceutical companies, biotech firms, and research institutions around innovative targets and pipelines. It integrates AI-driven drug discovery capabilities into the entire process, from molecular discovery and experimental validation to candidate drug optimization. Through milestone payments, License-out agreements, and subsequent revenue-sharing arrangements, it shares the growth value of its pipeline with partners.

To date, AIPher has engaged in co-development with more than five pharmaceutical companies and biotech firms on innovative drug discovery in areas such as metabolism, CNS, autoimmunity, and drug delivery, as well as on R&D tasks including target discovery and reverse target identification. The collaborations span multiple drug modalities, including small molecules and peptides, establishing multi-tiered partnership models ranging from joint development and milestone-based revenue sharing to shared product rights.

Furthermore, the company actively establishes deep collaborations with leading domestic research institutes and renowned hospitals, integrating AI capabilities into real-world clinical needs and the exploration of disease mechanisms, striving to ensure that every technological breakthrough addresses patient expectations earlier and more accurately.