Biopharmaceutical AI is rewriting the 'double ten rule' of new drug development

In the field of new drug development, there has long been a curse known as the '10-10 rule'—it takes 10 years and 1 billion dollars to possibly produce a new drug. Today, the involvement of artificial intelligence is quietly breaking this shackles. When biomedicine and AI deeply integrate, what we are witnessing is not only an improvement in efficiency but also a revolutionary shift from 'blind screening and trial-and-error' to 'precision design'.

Biomedical AI

1. Biomedical AI: From 'Seeing' to 'Foreseeing'

Traditional drug development is like searching for a needle in the vast ocean of molecules, whereas the core breakthrough of biomedical AI lies in enabling scientists to move from 'random discovery' to 'rational design.'

Through deep learning, generative models, and geometric deep networks, AI can learn the hidden sequence-structure-function relationships within massive amounts of biological data. It can accurately predict the dynamic conformations of target proteins, virtually screen compound libraries at the trillion-scale level, and even generate candidate molecules with specific pharmacological properties in an extremely short time. This compresses the traditionally years-long early compound discovery stage into a few months or even weeks.

More importantly, AI is not just an 'accelerator'; it is reshaping the underlying logic of biomedicine—turning the R&D process from experiment-driven to driven by both data and models, greatly reducing the risk of failure.

2. The Intelligent Leap in Protein Design

A Quantum Leap in Protein Design

Among all the applied scenarios of AI in biomedicine, protein design is undoubtedly the most disruptive high ground. Antibodies, enzymes, cytokines... these functional proteins are the core 'molecular machines' for disease treatment and industrial catalysis, and AI gives humans the ability to design these machines on demand.

Early research relied heavily on random mutations and directed evolution. Today, protein language models can 'understand' protein sequence grammar just like natural language, predicting the effects of mutations on stability, affinity, and immunogenicity. More advanced generative AI can directly create entirely new proteins that do not exist in nature, meeting strict druggability requirements such as higher affinity, longer half-life, and lower immunogenicity. This technology is catalyzing the birth of next-generation antibody drugs, intelligent enzyme preparations, and novel vaccines.

3. MatwingsVenus™ Sets Sail: Bringing AI Design into Real Industrial Scenarios

In the process of moving biomedical AI from theory to practice, high-performance computing platforms are a key carrier for technology implementation. Shanghai Matwings Technology’s independently developed MatwingsVenus™ (Xiaowu™) platform is such an innovative engine that deeply integrates advanced AI capabilities with biomedical needs.

The MatwingsVenus™ (Xiaowu™) platform combines large-scale pre-trained protein models with geometric deep learning technology to build end-to-end prediction and generation capabilities from sequence to function. It can accurately assess key druggable properties of proteins, such as stability, solubility, and affinity, and directly generate protein sequences and structures that meet preset functional requirements. Its unique 'dry-wet closed-loop' design seamlessly links AI predictions with high-throughput wet experiment validation, allowing the model to continuously evolve with real-world data feedback.

In antibody drug development, using MatwingsVenus™ (XiaoWu™) for antibody humanization and affinity maturation can reduce the large-scale wet lab screening process, which originally took several months, to just a few weeks, significantly improving the quality of candidate molecules. In industrial enzyme modification scenarios, MatwingsVenus™ (XiaoWu™) can quickly screen enzyme mutants that maintain high activity under extreme conditions such as high temperature and high pH, greatly reducing trial-and-error costs.

4. Toward an Intelligent Future of Precision Medicine

The true value of AI in biomedicine is not to replace scientists but to become an indispensable "precision navigator" in the hands of researchers. When AI can extract interpretable rules from complex biological systems and deeply integrate with automated wet experiments, the "double ten rule" of new drug development may become a thing of the past.

In the future, with the continuous infusion of technologies such as multimodal data, reinforcement learning, and causal inference, AI in biomedicine will advance further, moving from single-point protein design to system-level prediction and regulation of cellular pathways and tissue microenvironments. Shanghai Matwings Technology will also continue to focus its efforts, using the MatwingsVenus™ (XiaoWu™) platform as an anchor to help global partners jointly write a new chapter in precision medicine.