对谈 Chai-2 核心科学家乔卓然：抗体生成成功率提升百倍，分子生成平台是药物研发的 GPU｜Best Minds

Core Viewpoint - Chai Discovery is building an "AI-native drug discovery" platform that transforms scientific problems into engineering challenges, with the Chai-2 model representing a significant advancement in drug design capabilities, particularly in zero-shot molecular design [4][9]. Group 1: Diffusion Model and Structural Design - The Diffusion Model has fundamentally changed the modeling paradigm in drug prediction, enabling a transition from prediction to generation, allowing for the direct generation of biologically active antibodies without training samples [4][10]. - Structural prediction is a foundational capability that largely determines the upper limits of model performance, with the long-term vision of molecular generation platforms serving as the new productivity infrastructure for the pharmaceutical industry [4][9][10]. - The Chai-2 model has improved the drug development cycle from several months to just two weeks, achieving a success rate of 16% in generating active antibodies, significantly outperforming traditional methods [4][52][58]. Group 2: Zero-shot Molecular Design - Zero-shot molecular design allows for the generation of new proteins with binding activity without relying on any prior experimental data, representing a major leap in drug design methodologies [4][43][56]. - The success rate of Chai-2 in antibody design is 100 times higher than previous methods, with a 60% success rate in mini protein designs, showcasing the model's effectiveness in practical applications [4][52][61]. - Traditional antibody design methods often require extensive time and resources, while Chai-2 can generate viable candidates in a fraction of that time, demonstrating a significant efficiency improvement [4][58][60]. Group 3: Future of Drug Discovery - The future of drug discovery is expected to be shaped by AI-native platforms that can integrate experimental data and biological theories, leading to new business models where platforms themselves become intellectual property [4][9]. - The ability to generate new molecular structures directly from computational models is anticipated to redefine current drug development processes, particularly in the design of therapeutic proteins and antibodies [4][43][56]. - The integration of AI in drug discovery is seen as a transformative force, with the potential to accelerate the entire process from hypothesis generation to clinical application [4][35][37].