AI pharmaceutical company endorsed by Huang Renxun, English Silicon Intelligence, updates prospectus | Jianzhi Research

On March 27, 2024, InSilico Medicine, an AI pharmaceutical company founded in 2014, updated its IPO prospectus with the Hong Kong Stock Exchange, bringing it one step closer to becoming the first listed AI pharmaceutical company in the Asia-Pacific region.

InSilico Medicine's outstanding performance in the field of AI-assisted drug development has earned high praise from NVIDIA CEO Jensen Huang. As a company that has grown alongside AI technology for 10 years, InSilico Medicine's recent research published in the prestigious academic journal "Nature Biotechnology" has been particularly inspiring to Jensen Huang.

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The Generative AI Revolution is Driving a New Paradigm in Drug Development

At the recent NVIDIA GTC conference, CEO Jensen Huang emphasized the importance of AI in the pharmaceutical field.

Jensen Huang has always believed in the tremendous potential of AI + healthcare. 15 years ago, he recognized the ability of computer-aided drug discovery. Recently, Jensen Huang boldly predicted that in the future, everyone will not need to learn programming, and human biology will be the most useful subject.

In his keynote address at the NVIDIA GTC conference, Jensen Huang pointed out that in 10 years, AI will not only be able to understand text and videos, but also interpret proteins, genes, and brain waves, making generative AI the most revolutionary field.

Traditional drug development is costly and time-consuming. According to statistics, global pharmaceutical companies invest over $250 billion in drug discovery each year.

Wall Street News·Jianzhi Research mentioned in an article titled "The $61 Billion Bottleneck in the Pharmaceutical Industry: Can AI Solve It? | Jianzhi Research" that a study published in October 2023 showed that the average cost for a large pharmaceutical company to bring a drug to market from scratch has exceeded $61 billion. This figure was previously estimated in the market to be around $26-28 billion.

NVIDIA Vice President Kimberly Powell predicted at the recently concluded GTC conference that with the development of AI technology, pharmaceutical research and development expenditures will increasingly shift towards computing power and software. AI is expected to significantly reduce the cost of new drug development and shorten the time to market.

InSilico Medicine's Landmark Paper in Nature Subjournal: How to Develop Pharmaceuticals Using Generative AI

In March 2024, InSilico Medicine published a landmark paper in the top academic journal "Nature Biotechnology," comprehensively detailing its first potential "world-first" discovered and designed by generative AI The development process of INS018-055, a TNIK inhibitor for the treatment of Idiopathic Pulmonary Fibrosis (IPF), by TNIK, the first in class, is detailed. This candidate drug, from development using artificial intelligence algorithms to completion of Phase 2 clinical trials, has its preclinical and clinical trial data disclosed for the first time.

The paper details the discovery and optimization process of this drug:

1. Utilizing the target discovery engine PandaOmics under the Pharma.AI platform, a series of potential anti-fibrotic targets were nominated through deep feature synthesis, causal inference, and pathway reconstruction.
2. The NLP model of PandaOmics analyzed massive text data, identifying TNIK as a novel target for the treatment of IPF.
3. The Chemistry42 platform combined over 40 generative chemistry algorithms and more than 500 pre-trained reward models to generate and optimize novel compounds based on structural drug design strategies, resulting in the candidate drug molecule INS018_055.
4. Throughout the process, less than 80 molecules were synthesized and tested, significantly improving efficiency. INS018_055 demonstrated good efficacy, safety, and pharmacokinetic characteristics in preclinical and Phase 1 trials.

From the nomination of the TNIK target by PandaOmics to the nomination of INS018-055 as a preclinical candidate compound, it only took 18 months, highlighting the efficiency of AI-driven drug discovery.

Currently, this drug is undergoing Phase 2 clinical studies in China and the United States, making it the world's first drug to enter Phase 2 clinical trials using generative AI for discovery and design. According to relevant data, there are only 2 drugs on the market for the treatment of IPF, with the global IPF market expected to reach $5 billion by 2025.

The prospectus also discloses several upgrades to Pharma.AI, further accelerating AI drug discovery.

The main updated features include:

1. Copilot Architecture: Deep integration of large language models, allowing users to string together multiple platform functions through natural language commands to perform tasks such as target identification and molecule generation.
2. ADMET Predictor: Integration of machine learning models to predict key absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of small molecules.
3. AIChemistry: Using deep learning to predict binding free energy based on molecular structure and simulation features.

With the support of Pharma.AI, YingSi Intelligence has recently nominated multiple innovative target preclinical candidate compounds, covering multiple indications such as anti-tumor and anti-fibrosis In December 2023, Insilico Medicine nominated two preclinical candidate compounds:

• Second-generation DGKA inhibitor: Pharma.AI empowers further optimization of selectivity and safety, expected to be used in the treatment of anti-PD-1/PD-L1 resistant solid tumors;
• Oral highly selective covalent FGFR2/3 inhibitor: can be used for the treatment of "unrestricted cancer types" solid tumors, demonstrating superior efficacy in multiple FGFR2/3-driven drug efficacy models and high-frequency mutation resistance models.

In February 2024, Insilico Medicine once again nominated preclinical candidate compounds using its self-developed generative chemistry engine Chemistry42 for molecule generation and optimization:

• A potential "best in class" highly selective KIF18A inhibitor with a unique molecular scaffold; this compound selectively kills chromosomally unstable cancer cells, providing an innovative strategy for cancer treatment.

In addition to new drug development, Insilico Medicine also released the PaperGPT paper interpretation engine developed based on ChatGPT-4 Turbo and self-developed large language models (LLM). Through language dialogue functionality, the engine can provide professional answers to questions related to papers, allowing ordinary readers to easily understand cutting-edge research results.

Generative AI has played a unique role in multiple businesses of Insilico Medicine.

AI Pharmaceutical Companies, Improving Financial Metrics

New drug development mainly includes drug discovery, preclinical, and clinical phases.

The success rate of drug discovery is relatively higher compared to drug development, but the overall success rate of drug discovery is only 51% from target to lead compound to lead compound optimization.

Preclinical research typically involves animal model studies to evaluate toxicology and other parameters, optimization of chemical synthesis and drug formulation, and other studies to ensure approval to start clinical trials.

Clinical trials need to be conducted in healthy volunteers and patients to determine the safety and efficacy of the drug, ensuring regulatory approval. The overall success rate is only 12.9%.

Faced with the dual challenges of high R&D investment and low return on investment, many pharmaceutical companies are turning to AI-assisted drug discovery to reduce costs and increase efficiency.

However, looking at the financial data of globally representative AI pharmaceutical companies, the industry is still in a period of rapid growth, but there is a positive trend.

Taking Recursion, which received a $50 million investment from NVIDIA, as an example:

• Since its establishment, the company has focused on building advantages through data accumulation. It currently owns over 25PB of medical and biochemical data, which has become its core asset.
• In an automated warehouse, Recursion conducts millions of experiments every week to continuously enrich its database.
• CEO Chris Gibson revealed that their goal is to establish a foundational model describing the interaction between biology and chemistry, with the potential to fundamentally change the entire drug development process.

Recursion currently has the highest market value, reaching $4.388 billion. However, from the perspective of revenue and cash flow, the company is still in the investment phase, although the trend of revenue growth is already evident.

Jianzhi Research once introduced in "The $6.1 Billion Bottleneck in the Pharmaceutical Industry: Can AI Solve It?" that there are three different business models in the AI pharmaceutical industry:

1. **AI SaaS: Providing AI drug research platforms and software for private deployment to utilize customer data.
2. **AI CRO: Directly providing drug discovery results based on proprietary data AI models.
3. **AI biotech: Independently developing new drugs using proprietary data and AI technology.

Similar to Recursion, Insilico Medicine relies on a closed-loop business model from software licensing, collaborative research to pipeline licensing, and has reached a turning point in revenue and cash flow.

Currently, the Pharma.AI localized SaaS software subscription fee is up to $500,000 per year. External drug licensing also shows development potential, and the company is expected to continue to strengthen its hematopoietic capacity with more milestone revenues in the future.

Financial data shows that Insilico Medicine achieved revenue of $51.18 million in 2023, a year-on-year increase of 70%. Among them, $39 million comes from external drug development projects, which is the main source of revenue.

Compared to 2022, Insilico Medicine's operating indicators have significantly improved:

• Net loss reduced by approximately $10.19 million.
• Adjusted loss narrowed by approximately $3.44 million.
• Operating cash flow reached $92 million, achieving a significant inflow.
• Cash reserves are sufficient, reaching $177 million.
• Net cash consumption for the fiscal year was $30.7 million, lower than the previous year.

The rise of AI pharmaceuticals reflects the reshaping of global drug development by the AI revolution.

Driving drug approval is the next milestone for AI pharmaceuticals

Although AI pharmaceutical companies are developing rapidly, the real threshold is still the validation of going public. However, the day for this has been getting closer and closer.

The fastest progressing product of English Silicon Intelligence, ISM001-055 mentioned earlier for the treatment of idiopathic pulmonary fibrosis (IPF), has entered Phase 2 clinical trials. This is also the fastest drug globally discovered and designed using generative AI.

In June 2023, the drug completed its first dosing in IPF patients. This randomized, double-blind, placebo-controlled study aims to evaluate its safety, tolerability, pharmacokinetics, and preliminary efficacy, and is planned to be conducted simultaneously in nearly 40 research centers in China and the United States.

At the same time, English Silicon Intelligence has also achieved two license out collaborations, totaling over $1.5 billion.

Exclusive global licensing agreement with Exelixis for USP1 inhibitors (September 2023)

• An initial payment of $80 million for the collaboration, marking the first external authorization of English Silicon Intelligence's AI drugs
• English Silicon Intelligence grants Exelixis the exclusive global license to develop and commercialize ISM3091 and other USP1-targeted compounds
• Potential future milestone payments for clinical development, commercialization, and sales, as well as product sales royalties

Major collaboration with Menarini Group for KAT6 inhibitors (January 2024)

• Collaboration totaling over $500 million, granting Menarini exclusive global development and commercialization rights to a novel KAT6 inhibitor
• This novel KAT6 inhibitor is expected to be used in the treatment of ER+/HER2- breast cancer (accounting for approximately 70% of breast cancer patients) and other cancers, with preclinical studies demonstrating outstanding efficacy and good safety profile

Furthermore, several self-developed investigational new drugs are progressing smoothly, with two products entering Phase 1 clinical trials and expanding into more indications.

1. The QPCTL small molecule inhibitor developed in collaboration with Fosun Pharma has been approved for clinical trials and achieved its first clinical milestone. (August 2023)
2. The oral PHD inhibitor has initiated Phase 1 clinical trials for the treatment of inflammatory bowel disease. This intestine-restricted oral small molecule inhibitor with a novel molecular scaffold and unique binding mode has shown good safety and significant anti-colitis efficacy in preclinical studies. (November 2023)

AI is reshaping the global landscape of new drug development, and the future of AI pharmaceuticals has arrived. As Huang Renxun said, the future of human biology will become the most important discipline. AI will significantly reduce the cost of new drug development and accelerate the emergence of innovative drugs.

At the same time, with more AI pharmaceutical companies entering the capital market, the market is expected to gain richer and more multidimensional company operational data. This will greatly expand industry awareness boundaries and provide investors with a clearer understanding of AI pharmaceutical applications