「智源深澜」获天使轮融资，构建数据驱动的AI生物分子设计平台

Core Insights - "Zhiyuan Shenlan" recently completed several million yuan in angel round financing, led by Woyan Capital, with participation from Tianfeng Capital and other angel investors, as well as existing shareholders [1] - The funding will primarily be used for the development of a generative AI platform for biomolecules and a self-driving molecular function evolution platform, as well as market expansion [1] - Founded in 2024 and incubated by Megia Technology, Zhiyuan Shenlan focuses on data-driven biomolecular design and manufacturing, led by Dr. Wang Chengzhi, who has over 20 years of experience in the life sciences [1] Industry Trends - Generative AI is causing profound changes in the life sciences sector, transitioning from being an auxiliary tool to an autonomous platform, shifting the research paradigm from "large-scale trial and error" to "precise design and creation" [1][2] - The emergence of AlphaFold 2 has predicted over 200 million protein structures, covering most known proteins on Earth, but the industry is more focused on protein functionality rather than just structure [1] Company Strategy - Zhiyuan Shenlan aims to optimize "function" by exploring functional needs in practical application scenarios, constructing a self-driving automated experimental platform to efficiently generate functional data [2] - The company's biomolecular design system combines the automated experimental platform with AI algorithms, allowing for rapid iteration based on real functional feedback, thereby enhancing research and development efficiency [2] - The goal is to create a data-driven platform for biomolecular engineering and design, evolving AI for Science from a 2.0 "navigational design engine" to a 3.0 "scientific intelligent autonomous platform" [2] Future Vision - In the future 3.0 era, AI will autonomously design, execute, and iterate entire research experiment loops, with human scientists focusing on key questions and strategic directions [3] - This evolution will democratize and equalize technology in life sciences research, similar to the development of apps in the internet era and intelligent agents in the AI era [3] Key Breakthroughs - The autonomous intelligent evolution platform requires three key breakthroughs: a unified coordinate system for AI comprehension, an autonomous decision-making AI agent for complex problem-solving, and an automated intelligent experimental platform for large-scale, reliable research [4] - Zhiyuan Shenlan proposes a "ten-step" roadmap for AI4S 3.0, from learning existing human knowledge to making scientific discoveries that surpass human intuition across multiple scientific fields [4] - In the field of biomolecular generation and prediction, generative AI enables researchers to identify new targets, optimize molecular structure design, and accelerate drug development and new material design, enhancing efficiency and innovation across the entire industry chain [4]