The Symbiotic Era of AI, Scientists, and Pharma: Seizing Opportunities in Talent and Resources

At the beginning of this year, the AI biopharmaceutical field received significant news: Isomorphic Labs recently signed cooperation agreements with pharmaceutical giants Eli Lilly and Company and Novartis, respectively. The future value of these two collaborations may exceed 3 billion US dollars.

This significant collaboration demonstrates the potential of AI in drug development and also signals that the integration of AI and biotechnology will bring about more transformations. This partnership represents not only a business advancement but also reflects the biopharmaceutical industry's urgent need for efficiency, precision, and innovative technologies.

As AI gradually transforms various industries, how should companies in the biopharmaceutical field view this tool? Compared to large companies, where are the opportunities and challenges for startups? Based on a recent dialogue between Dr. Zang Xiaoyu, founder and CEO of N1 Life, and Dr. Che Xing, founder and CEO of YDS Pharmatech, we can glimpse the various possibilities of the future "AI + biopharmaceuticals."

As technology matures, AI technology is playing an increasingly important role in modern drug research and development.

From shallow neural networks to deep neural networks, AI models have excelled in processing and analyzing massive amounts of biological data. NVIDIA's BioNeMo is a large language model (LLM) and generative AI framework tailored for the biomedical field, designed to accelerate research and discovery in life sciences and healthcare. The underlying infrastructure of platforms like BioNeMo highlights the significant demand AI has for biological data, which serves as the foundation for training advanced AI models.

The core advantage of AI lies in its ability to process large-scale datasets and extract complex patterns from them. This makes AI highly applicable in the biomedical field. For instance, AI can be used to predict the three-dimensional structures of proteins, simulate interactions between drugs and target proteins, and even forecast the efficacy and side effects of potential drugs. Through these applications, AI not only enhances the efficiency of drug development but also significantly reduces costs and time.

Based on this characteristic, many AI biopharmaceutical companies have emerged in recent years. For example, Isomorphic Labs, which was founded in 2021 by DeepMind, a subsidiary of Alphabet, uses DeepMind's AlphaFold 2 technology to predict protein structures. By revealing these structures, researchers hope to identify new target pathways for developing novel drugs or therapies for diseases.

Iambic Therapeutics is a company that uses a generative AI platform to develop novel therapeutic drugs, primarily focusing on small-molecule drugs. Iambic combines physics-based approaches and experimental data to generate new molecules, and its technology is used not only for predicting protein-ligand interactions but also for molecular design. Iambic’s two major projects — IAM-H1 targeting HER2 and IAM-C1 targeting CDK2/CDK4 — are both planned to enter clinical stages next year.

In the rapid development of AI, companies like NVIDIA not only provide powerful computing resources but also promote data standardization and sharing to support the training and application of AI models. By 2025, with the continuous growth of data volume, AI models are likely to encounter a bottleneck in data growth, and Biotech, with massive amounts of data, could become the ultimate driving force for the next growth phase. This is the main reason why companies like NVIDIA continue to invest heavily and even become a major driving force in the Biotech-AI ecosystem. Specific actions include investing in these Biotech companies to support more data generation, application implementation, and computational method development, making deep and extensive long-term strategic layouts.

At the intersection of AI and biotechnology, large Pharma companies and small Biotech companies each face different challenges and opportunities.

Tech giants like DeepMind under Google and Meta (formerly FaceBook) have abundant computational resources and technical reserves, providing them with substantial assets to drive the research of large AI models.

For example, several generations of DeepMind's AlphaFold models have made revolutionary progress in protein structure prediction, becoming a landmark milestone in the field of life sciences. The technological and computational advantages of these companies enable them to achieve significant technological breakthroughs rapidly, push the boundaries of technology, and lead industry development.

In contrast, the accuracy of less resource-intensive technologies, such as the RoseTTAFold All Atom model, has unfortunately fallen behind AlphaFold 3. In a sense, innovation in AI models is increasingly driven by giant companies rather than academic institutions or startups.

Although AI startups are not as resourceful as large companies in terms of human and technical resources and funding, they have unique advantages in flexibility and innovation speed. They can quickly adapt to dynamic market changes and rapidly apply new technologies to practical problems.

However, startups are constantly facing the challenges of continuous financing and technological iteration. Compared with large companies, startups lack large-scale R&D teams and infrastructure, which means they face more risks when developing new technologies, especially in terms of data acquisition and model optimization. They must remain competitive despite limited resources.

In addition, AI Biotech companies also have to face competition from traditional pharmaceutical companies — these companies have deep industry experience and strong R&D capabilities. To stand out in the competition, small AI Biotech companies need to find a unique position in the market. A typical example of an AI biotech company is Iambic mentioned above, which has made significant progress in pipeline advancement by leveraging AI technology.

On the other hand, traditional Biotech companies focus more on solving practical scientific and medical problems, leveraging AI tools to accelerate the progress of experimental science and promote the discovery and development of new drugs. N1 Life is a typical Biotech company dedicated to the research and application of biocompatible molecular materials such as peptides for drug delivery carriers. It has currently developed two major models, Absotride and ChARLS, targeting different types of drugs.

N1 Life's N1-109 is an example of such innovation, demonstrating significant therapeutic potential for indications like metastatic ovarian cancer, pancreatic cancer, and gastric cancer. Currently, N1 Life integrates rational design and AI technology to enable targeted property screening and directional optimization, gradually establishing a "dry-wet combined" R&D pipeline from vector design to vector screening and finally to vector drug application. Through platform collaborations and other means, the company has reached technical partnerships with several innovative pharmaceutical enterprises both domestically and internationally, applying its self-developed carrier molecules to various disease areas. The goal is to achieve lower costs and shorter R&D cycles.

These AI biotech companies, each with their own strengths, are facing a booming market: a report by Transparent Market Research predicts that the AI biopharmaceuticals market will grow to $13.1 billion by 2030.

In the AI-driven biotechnology revolution, scientists play a key role.

Despite the powerful capabilities demonstrated by AI models in protein structure prediction and drug design, the development and application of these models cannot be separated from the expertise and experience of scientists.

For example, DeepMind's AlphaFold 2 model integrates research achievements in physics and bioinformatics, utilizing decades of accumulated protein structure data by scientists, which has led to significant breakthroughs in protein structure prediction. The contributions of scientists not only lie in data provision but also in the interpretation and application of AI model results, which are crucial for advancing the model’s applications (e.g., new drug development).

In addition to providing AI with a vast amount of data and research findings, scientists have also participated in the design and optimization of the models. This close collaboration ensures the scientific accuracy and practicality of AI technology in the biotechnology field.

From another perspective, the symbiotic relationship between scientists and AI is not only reflected in data analysis and model optimization but also across all stages of clinical trials and drug development. Beyond target simulation and molecular design, scientists also utilize AI technology to analyze clinical data, design clinical experiments, improve production efficiency, and accelerate regulatory processes. This "you in me, me in you" integration continuously speeds up the entire drug development process and increases the success rate of drug development.

More importantly, the human biological system is so complex and difficult to decipher that it is far more complicated than computers. There are still a large number of problems that need to be defined, have solutions designed, and models argued and optimized by experienced scientists. In the future, the demand for interdisciplinary scientific talents will grow explosively, and the interoperability between biological language and computational language will become an unstoppable trend. From the education system to professional talent cultivation and scientific training, these will also become indispensable tools for humans to harness artificial intelligence.

As AI technology moves towards implementation, one point has become an industry consensus: AI cannot replace scientists. However, AI solutions that enhance scientists' efficiency and are developed with a win-win cooperation mindset will become excellent tools to assist scientists in the biopharmaceutical field.

The Future of AI and Biotechnology Integration in Drug Development and Medical Technology

As AI models continue to improve and applications expand, we can expect to see more precise and personalized healthcare solutions. This transformation not only enhances patient outcomes but also significantly reduces healthcare costs and improves the accessibility of medical services globally.

As large amounts of capital pour in and technological breakthroughs emerge frequently, the integration of AI and biotechnology could be the most significant turning point for the pharmaceutical industry this century. This trend may not only transform our understanding and treatment of diseases but also lead the entire healthcare industry into a new era, bringing profound impacts to human health.

However, the widespread application of AI also brings new challenges, including issues such as data privacy, algorithmic bias, and model transparency. These issues require joint efforts from the industry and regulatory agencies to resolve, ensuring the safe application of AI technology in the fields of medicine and biotechnology.

Author of this article: Zang Xiaoyu, Founder and CEO of N1 Life