两篇新研究论文提出了在药物发现中使用扩散模型的先进方法。第一篇FTDiff采用强化学习来精调扩散模型，以生成具有特定类药特性和与目标蛋白结构兼容性的分子。第二篇论文介绍了一种基因型条件扩散模型，该模型基于预测的药物敏感性、可合成性和结合合理性，并以实验性癌细胞系数据为依据，来优化分子候选物。 AI
影响 这些方法通过改进靶向性和有效治疗分子的生成，推动了AI在加速药物发现中的作用。
排序理由 两篇在arXiv上发表的学术论文，提出了使用扩散模型进行分子生成的新颖方法。
在 arXiv cs.MA (Multiagent) 阅读 →
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
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