Core Viewpoint - Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) represent about 60% of the human proteome and are crucial for various cellular functions and disease progression. Recent advancements in artificial intelligence (AI) have enabled the design of specific binding agents for these previously considered "undruggable" targets, unlocking new therapeutic possibilities [1][2][20]. Group 1: Importance of IDPs and IDRs - IDPs and IDRs play significant roles in cellular signaling, stress responses, and disease progression, making them valuable targets for clinical diagnostics and drug development [2][8]. - Traditional drug design struggles with IDPs due to their lack of stable structure, which complicates the development of targeted therapies [6][7]. Group 2: AI Breakthroughs in Drug Design - The research led by David Baker's team utilized generative AI to design proteins that can accurately bind to IDPs and IDRs, achieving atomic-level precision [2][11]. - The AI model, RFdiffusion, allows for dynamic matching without pre-setting structures, enabling the generation of binding proteins that can adapt to the flexible nature of IDPs [11][12]. Group 3: Experimental Results and Applications - The studies published in Nature and Science demonstrated the successful design of binding proteins for various IDPs, with binding affinities ranging from 3 to 100 nanomolar [15][18]. - These binding proteins have shown potential in therapeutic applications, such as inhibiting amyloid fiber formation related to type 2 diabetes and disrupting stress granule formation in neurodegenerative diseases [16][18]. Group 4: Future Implications - The new design strategies developed could lead to innovative treatment methods and diagnostic tools for diseases associated with IDPs and IDRs, marking a significant advancement in precision medicine [20][24]. - The complementary strategies of RFdiffusion and logos provide a robust framework for targeting both structured and unstructured protein regions, enhancing the versatility of drug design [21][22].

Group 1 - The design of profit-sharing mechanisms in project financing is crucial, impacting both the interests of investors and founding teams, as well as the company's future financing capabilities and development momentum [2][4] - A core principle of profit-sharing design is "dynamic matching," where the profit-sharing logic differs significantly between seed rounds and later stages, with seed rounds focusing on protecting founders [2][4] - In growth-stage financing, a "ladder adjustment" mechanism should be introduced, where profit-sharing ratios automatically adjust based on valuation milestones, reflecting the balance of risk and reward [2][4] Group 2 - Clear delineation of responsibilities is essential for profit-sharing design, as many startups face disputes due to unclear boundaries of roles and responsibilities [4][6] - The inclusion of anti-dilution clauses in the profit-sharing framework is necessary to protect early investors from dilution of their profit-sharing rights during subsequent financing rounds [4][6] - Establishing a "dynamic incentive pool" of 10%-15% for core employees is a long-term safeguard, allowing for equity incentives without excessively diluting the founders' shares [6] Group 3 - The ultimate goal of profit-sharing design is to deeply bind the interests of all parties with the growth of the project, fostering a "symbiotic mindset" that ensures motivation for founders and reasonable risk for investors [6]