Yitu Shengke Files IPO Prospectus: AI-Driven Drug Discovery Platform Achieves 90% Targetability/Druggability Accuracy with Six Pipelines in Six Months

Recently,AceMapAI(hereinafter referred to as AceMapAI)Utilizing the self-developed AceMapAI intelligent drug discovery platform, the world's first AI-driven rare disease drug target discovery, lead compound design (PCC), and synthesis were completed within two months.With this breakthrough result,AceMapAI Selected as an Outstanding Project in AstraZeneca's CoSolve Innovation Program in June This Year, laying a solid foundation for subsequent in-depth cooperation and the License Out business model. Meanwhile, the companyAt the same time, AceMapAI completed the world's first fully AI-driven drug optimization for recombinant asparaginase and successfully advanced the drug to the preclinical research stage (IND).。

AceMapAI was founded in October 2021., with self-built "AI + Bioinformatics"Based on the three major AI intelligent drug research and development platforms, we have completed the exploration of pathogenesis, target establishment, drug design, and formulation design; with"AI Drug Discovery + License Out"As the core business model, achieve in a short period of timeProject Implementation of Multiple Pipelines for Anti-Tumor, Anti-Aging, and Rare Diseases, complete all R&D processes before the drug's IND and collaborate with global pharmaceutical companies.

Since the 21st century, the diseases faced by humanity have become increasingly complex, and new drug development worldwide confronts challenges such as "high investment, long cycles, high risks, and low success rates." The advancement of AI technology has provided new technical methods and solutions for new drug development, especially in the research of specific disease treatments and candidate drugs. AI-empowered new drug development has pioneered a new form of drug research. However, in terms of molecular activity sustainability, AI platform operability, and the balance of expertise between AI engineers and pharmaceutical chemistry experts, AI-driven pharmaceutical technology still requires further exploration.

AceMapAI's AI new drug R&D platform effectively integrates bioinformatics research, completing the design of effective active drugs and their synthetic expression within 2-3 months in the context of novel targets—reducing the time by approximately 70% compared to traditional enterprises, while R&D costs are reduced to 10-20% of those for traditional pharmaceutical companies.Such technological breakthroughs are attributed to the senior researchers gathered by AceMapAI from top universities and research institutions both in China and abroad, as well as a professional team with years of experience in many well-known pharmaceutical companies around the world, establishing a world-leading technology高地 for the company.

“AceMapAI Intelligent Drug Discovery PlatformIncluding:AceMapAI Small Molecule Intelligent Drug Discovery Platform、AceMapAI Intelligent Drug (Synthetic Biology) R&D Platform、Shennong Natural Products and Anti-Aging Substance R&D Platform. These three major platforms are condensed intoProfessor Yuzong Chen, Co-founder and Chief Scientific Officer of AceMapAI, and his team have achieved remarkable research results over nearly 30 years, covering approximately 80% of known drug forms.。

Professor Yuzong Chen, Co-founder and Chief Scientist of AceMapAIIn 1985, he obtained a master's degree from the Institute of Theoretical Physics at the Chinese Academy of Sciences, and in 1989, he received a doctoral degree from the University of Manchester in the UK. From 1989 to 1996, he completed his postdoctoral research at Purdue University in the United States. Professor Chen has worked on AI and drug discovery at IONIS Pharmaceuticals in the United States and in the Department of Computational Science at the National University of Singapore.In 2002, AI and bioinformatics were introduced into the fields of biological research and drug research., becomingThe World's First Batch of "AI + Bioinformatics" New Drug R&D Scientists. Professor Chen inventedReverse Docking Target Discovery Method, createdThe internationally renowned TTD drug target database in the pharmaceutical field, andElucidating the Mechanism of Traditional Chinese Medicine in Treating COVID-19 Using AI and BioinformaticsProfessor Chen Yuzong has taught at the Department of Computer Science and the Department of Pharmacy at the National University of Singapore. He is currently a professor at the Shenzhen Graduate School of Tsinghua University and the deputy director of the State Key Laboratory of Oncology and Chemical Genomics.

In addition,The Core Team of AceMapAIAlso includes: rich experience in AI-driven health industry and enterprise managementCo-founder and CEO Li Xiaoran; Participated in capital operations for over 100 listed companies, presided over and participated in the investment incubation of more than 60 emerging industry startup projects, and possesses over 20 years of experience in mergers and acquisitions of listed companies.Co-founder Dr. Xiaolei Yu；Chief Scientific AdvisorDr. Di Shuo, who has served as a researcher at Microsoft Research Asia, CTO at Google, senior researcher at Lernout & Hauspie in the United States, R&D manager at Overtone in the United States, and CTO at Truition in the United States;Chief Medical Officer, former R&D Department Manager of Sinovac Biotech, participant in the development and industrial production of inactivated hepatitis A vaccine under China's "Ninth Five-Year Plan" key scientific and technological project, participant in the development of Class I anti-tumor new drug CPT under the "863" Program, and master's supervisor at the Chinese Academy of Sciences.Professor Junsheng Cui; andChief Medicinal Chemistry Expert, currently serving as the Vice Chair of the Chemistry Department at the School of Science, China Pharmaceutical University, and doctoral supervisorProfessor Zhang Xiaojin, etc.

AceMapAI's Three Intelligent Drug Research and Development Platforms and Their Technical Services

AceMapAI's R&D team has comprehensive experience in the entire process of First in Class drug development, from research to market launch., and its independently built R&D platform alsoCovering all the technical environments required for drug IND, willModern computer AI technology is applied in every aspect of drug research and development., includingTarget Screening、Lead Compound (Small and Large Molecules) Discovery、Drug Target Structure Exploration Analysis、Molecular Dynamics Simulation of Drugs and Target Molecules、Pharmacokinetic Characteristics Prediction、Adverse Reaction Analysis、Immunogenicity Assessmentetc.

The "AceMapAI AceMap Intelligent Drug (Small Molecule) R&D Platform" can complete De Novo small molecule design, drug-likeness/targetability evaluation, high-throughput virtual molecule screening, ADME/T prediction, and compound synthesis services.

First, the platform utilizes AI technology forWhole Genome Sequencing (WGS) Analysis, identify potential disease-causing pathological pathways, and simultaneously analyze changes in protein pathways associated with the pathology,Final Selection of Driver Targets。

Among them, in the validation of drugsTarget Validation, AceMapAI will fully leverage its capabilities in AI and genetic engineering,Inducing targeted gene mutations in animals through genetic manipulation, built onGene-edited models that can stably reproduce diseases at the phenotypic level，Recreate the Pathogenesis of Diseases from the Source。 The team used this technology to lay a solid experimental foundation for the pharmaceutical process, achieving animal efficacy evaluations that are as close to real-world effects as possible in the early stages.Meet the most important "Fail Fast" requirement of modern pharmaceutical industry, while the experimental results are fed back to the AI design platform for continuous iteration and updates.AceMapAI has currently applied this advanced technology to complete the world's first animal model for a rare disease and is in related commercial negotiations with AstraZeneca.

Then, after completing the target evaluation, startCompound Structure Design Phase。

Traditional AI drug discovery generally employs structure-activity relationship or pharmacophore methods, using machine learning to analyze and predict various atoms, molecules, and their physicochemical properties. This process involves synthesizing a large number of candidate compounds, conducting high-throughput experiments, and performing molecular screening based on these results. While this method has shown some effectiveness, many physicochemical properties, as well as the interactions between different physicochemical properties, are often overlooked. Additionally, the learning algorithms are typically limited to traditional machine learning approaches, resulting in limited screening efficiency and an inability to perform De Novo drug design.

In the international market trading environment targeted by AceMapAI,The most competitive First In Class drug pipeline, all of which were carried out in the absence of any referenceable active substances, with extremely limited historical research information on targets, and even without a clear understanding of the pathogenesis and disease pathways. Therefore, the comprehensively utilizable drug target information, the physicochemical properties of the drug essence, multi-dimensional drug structures, as well asEffective Utilization of Deeper-Level Modern AI Technology, which is the current need.

Professor Chen and his team based on Manifold theory, pioneeredA Novel AI-based Method for Characterizing Drug Features Using Ultra-high Dimensional Physical and Chemical Properties: MolMap, andInnovative AI Learning Algorithm MolMapNet。By deeply integrating bioinformatics, the disordered physicochemical property information is transformed into ordered, specifically structured 2D images, fully leveraging modern AI visual imaging technology, thereby significantly improving the efficiency of compound screening.This research achievement was published in Nature Machine Intelligence in April 2021.

AceMapAI's New AI Drug Recognition Algorithm Framework

"AI + Bioinformatics" integration has led to another revolutionary breakthrough, enabling computers to make more comprehensive and precise judgments about drugs.One of the classic cases is the method's prediction of the druggability of new targets published in top journals over the past decade and subsequent tracking. By applying this technology to the target discovery process, the team currently hasThe accuracy of targetability and drugability evaluation has reached 90%.。

With the help of macromolecule/synthetic biology R&D platform, AceMapAI can alsoBy simulating different biological environments through AI, the alkali resistance, acid resistance, stability, and other properties of macromolecular drugs can be accurately evaluated, and the docking of drugs with targets in the human body as well as drug efficacy can be fully simulated and assessed., throughPrediction of Immunogenicity and Site-Directed Mutagenesis to Reduce Adverse Reactions of Macromolecular Drugs in the Human BodyAt the same time, AceMapAI's research team has been collecting and organizing a very unique database of effective substructures, adverse structures, and side effects since the 1990s, further enhancing the technical strength of its AI applications. Currently,The MolMapNet algorithm and feature processing technology have constructed over 8.5 billion features and spatial matrices across various datasets., aiming to allow AI to perform detailed zoning of important spatial and chemical features, therebySignificantly improve the accuracy of AI at all application stages。

For the natural product anti-aging substance R&D platform,AceMapAI has currently utilized AI technology to complete machine learning for 300,000 natural products, establishing the world's first database for the quantitative research of natural products in traditional Chinese medicine., with unique advantages in the quantitative analysis of natural products and AI design technology. AceMapAI hopes to disseminate the scientific validation methods of Traditional Chinese Medicine to the world through its own natural product platform.

AceMapAI adopts the core business model of "AI Drug Discovery + License Out," allowing customers to fully experience its industry-leading efficiency and cost advantages in AI-driven drug research and development.Currently, more than a dozen pharmaceutical enterprises, research institutes, and hospitals have signed relevant cooperation agreements or reached cooperative intentions with it, including: Beijing Neurosurgical Institute, China Pharmaceutical University, Tsinghua University, AstraZeneca, Xiangya Hospital of Central South University, China Biologic Products, etc.

AceMapAI New Drug R&D Pipeline Overview

AceMapAI currently has 6 pipelines under research. Since the establishment of the first pipeline at the end of 2021, the company has completed the screening of preclinical candidate compounds for 4 drugs., and has entered the commercial negotiation stage. ItsThe first pipeline ETB-01 has entered the preclinical IND trial stage.，The project will be declared as a Class I biological innovative drug to the CDE, with plans to complete relevant IND trials by December this year and submit applications to both the FDA and CDE for dual filings in China and the US.. This pipeline is expected to become the first AI-driven large molecule innovative drug in China to complete the process from drug validation to IND application within one year.

An average of 2-3 months per pipeline for positive feedback, which originated from AceMapAI's relentless efforts to establishAceMap Intelligent Drug Research and Development Platform, IterationAI-Wet Lab Integrated R&D Process。In the future, AceMapAI will continueFocusing on anti-tumor, anti-aging, and rare disease pipelines, at the same timeAI-driven Drug Development for Neurological and Metabolic Diseases. Company PlanAt least 5-8 new pipelines are opened each year.，Add 10 project collaborations for joint R&D or special commissioning., ensureAt least one drug completes IND and is sold each year., ultimately achieving the commercial cycle of the License Out model.

Currently,AceMapAI has completed a series of preliminary laboratory result evaluations, ranging from drug synthesis and macromolecular recombinant expression to animal model construction.。

The AceMapAI Intelligent Drug Discovery Platform of the team will continue to be updated and iterated in the future. After iteration, the platform can enhance the sensitivity to amino acid sequences, in order to improve...Homology Comparison in the Prediction of Related Drug StructuresProfessor Chen Yuzong will subsequently lead the team to addMolecular Dynamics + AI Force Field and Evolutionary Algorithm Platform、siRNA Nucleic Acid Drug Computing Platform、Quantum Computing + AI Computing Platform、Quantum Computing + Molecular Dynamics Computing Platform、Cell and Human Environment Simulation Computing Platform、Gene-Protein-Cell-Organs-on-Chip Computational Platform and other algorithm platforms. After establishing the corresponding database based on the above algorithm platform, AceMapAI will continue to buildNucleic Acid Drugs、Automated Living Bacteria、Gene Chip/Organ Chip、Quantum Computing and Protein Expression Design and R&D Platform。

In an interview with VCBeat, Professor Chen Yuzong called on various research institutions and major pharmaceutical companies to share scattered drug development data by cleaning and integrating the data., allowing the iteratively developed AI algorithms to learn from richer data resources, thereby achieving more efficient and precise AI-driven new drug research and development.

AceMapAI Co-Founder Li Xiaoran BelievesAceMapAI is optimistic about the AI-driven new drug R&D field and will continuously upgrade its algorithm platform, database, and design platform.Continue to enhance the application effects of AI in the process "from target confirmation to IND completion," demonstrating the technical, speed, and cost advantages of AI in new drug development., striving to achieve the company's goal of going public in the next three years!