【斯坦福大学科学家开发新方法：AI加速抗体药物研发进程】
在生物医药领域，寻找理想抗体药物的挑战始终存在。蛋白质，作为生物体内执行各种功能的基石，其设计与优化一直是科学家们关注的焦点。近日，斯坦福大学的科学家们开发了一种基于机器学习的创新方法，显著加速了抗体药物的研发进程，这一成果发表在了国际顶尖学术期刊《科学》（Science）上。
该研究团队融合了蛋白质骨架的三维结构与基于氨基酸序列的大型语言模型，通过这一独特的结合，研究人员能在几分钟内预测并找到那些罕见且理想的蛋白质突变。这一方法不仅极大地缩短了传统的筛选过程，提高了预测的准确性，而且使得在设计更好抗体药物方面取得了突破性的进展。
传统的抗体药物研发过程中，科学家们往往需要在庞大的氨基酸序列空间中进行反复试验，以寻找能够赋予蛋白质特定功能的突变。然而，这一过程耗时耗力，成本高昂，且充满不确定性。面对这一挑战，斯坦福大学的科学家们提出了一种更为高效、智能的解决方案。
研究团队的领导者，斯坦福大学化学工程助理教授兼 Arc 研究所创新研究员 Brian L. Hie 表示：“许多智能算法的目标是消除其中的猜测。我们开发的方法不仅能够减少不确定性，还能够显著加速抗体药物的研发进程。”通过将蛋白质的三维结构与基于氨基酸序列的大型语言模型相结合，该方法能够更准确地预测哪些突变可能产生更佳的药物效果，从而在极短的时间内找到理想的目标。
这一创新不仅为抗体药物的研发带来了革命性的变革，也为整个生物医药领域开辟了新的可能性。通过AI技术的辅助，科学家们能够在更短的时间内探索更多的可能性，加速新药的开发，最终惠及全球的患者。
斯坦福大学的这一研究成果不仅展示了人工智能在生命科学领域的重要应用，也为未来的药物研发提供了全新的思路和工具。随着技术的不断进步，我们有理由期待，未来的药物研发将更加高效、精准，为人类健康带来更大的福祉。
英语如下：
News Title: “AI Boosts Discovery of Ideal Antibody Drugs, Affinity Increases 37 Times”
Keywords: AI Optimization, Protein Mutations, Drug Design
News Content: [“Stanford Scientists Unveil New Method: AI Accelerates Antibody Drug Development”]
In the field of biomedicine, the quest for ideal antibody drugs remains a formidable challenge. Proteins, the building blocks that perform various functions within organisms, have always been a central focus for scientists when it comes to their design and optimization. Recently, scientists at Stanford University have developed an innovative method based on machine learning that significantly accelerates the development of antibody drugs. This groundbreaking achievement was published in the prestigious international academic journal, Science.
The research team combined the three-dimensional structure of protein frameworks with large language models based on amino acid sequences. By this unique integration, researchers can predict and identify rare and ideal protein mutations within minutes. This approach not only drastically shortens the traditional screening process and boosts the accuracy of predictions, but also marks a significant advancement in the design of better antibody drugs.
Traditionally, in the development of antibody drugs, scientists often need to conduct numerous trials in the vast space of amino acid sequences to find mutations that confer specific functions to proteins. However, this process is time-consuming, labor-intensive, and financially costly, with high uncertainty. To tackle this challenge, scientists at Stanford University have proposed a more efficient and intelligent solution.
Leading the research team, Assistant Professor and Arc Innovation Fellow in Chemical Engineering at Stanford University, Brian L. Hie, says, “Many intelligent algorithms aim to eliminate guesswork. Our method not only reduces uncertainty but also significantly accelerates the development of antibody drugs.” By combining the three-dimensional structure of proteins with large language models based on amino acid sequences, this method can more accurately predict which mutations might yield superior drug effects, thus enabling the identification of ideal targets within a fraction of the time.
This innovation has brought a revolutionary change to the development of antibody drugs and opened up new possibilities for the entire biomedicine field. With the aid of AI technology, scientists can explore more possibilities within a shorter timeframe, accelerating the development of new drugs that ultimately benefit global patients.
Stanford University’s research findings not only highlight the pivotal role of artificial intelligence in the life sciences but also provide a new perspective and tool for future drug development. As technology continues to advance, we can expect that the development of drugs will become more efficient and precise, bringing greater benefits to human health.
【来源】https://www.jiqizhixin.com/articles/2024-07-15-17
Views: 2