{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"萌喵读文献-生物信息学","title":"今日生物信息学最高分文献 - 2025-10-22","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/5d13d50a\"></iframe>","width":"100%","height":180,"duration":157,"description":"科研喵使用ai读文献，祝你效率百倍，访问labcat.com.cn下载。\n\n今天为您带来发表在《Chemical Society reviews》(影响因子40.4)上的重要论文\"Modeling protein-ligand interactions for drug discovery in the era of deep learning\"。这篇综述探讨了深度学习如何彻底改变药物发现领域。传统物理计算方法如分子动力学模拟和分子对接虽理论严谨，却面临计算成本高、可扩展性有限和预测准确性不足等挑战。研究指出，深度学习通过五种互补方式推动了药物发现：增强分子动力学、提升分子对接与虚拟筛选、端到端建模蛋白质复合物、基于结构的从头药物设计，以及基于序列的相互作用预测。关键发现是，物理驱动与数据驱动方法的结合不仅能提高预测效率和准确性，还能探索现代药物发现中庞大的化学和生物空间，为开发更有效的治疗方案开辟新途径。","thumbnail_url":"https://img.transistorcdn.com/38WU46uKrju37cyiLrKcVSktFL4vxBb_oTgbpM_2CRw/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8zOGU2/ZmEyYWE0MDZkNGFj/NWMzNGY5ZmU4YTk0/ZTBlNS5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}