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English(EN) Computational Design and Experimental Validation of Photoactive PARP1 Inhibitors

机器学习助力设计光激活癌症药物

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种计算方法，用于设计用于癌症治疗的光敏性PARP1抑制剂。通过使用机器学习和原子模拟筛选了500万个假设的配体，他们确定了有希望的候选药物，这些药物在光照和黑暗条件下对PARP1表现出差异性结合。合成了十种化合物并进行了测试，其中一种候选药物在暴露于绿光时显示出PARP1抑制作用增加了15倍。 AI

影响 推动了计算药物发现的进步，有可能加速靶向光激活疗法的发展。

排序理由 学术论文，详细介绍了药物发现的一种新计算方法。

在 arXiv cs.LG 阅读 →

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机器学习助力设计光激活癌症药物

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Simon Axelrod, Miroslav Ka\v{s}par, Krist\'yna Jel\'inkov\'a, Mark\'eta \v{S}m\'idkov\'a, Erika Bart\r{u}\v{n}kov\'a, Sille \v{S}t\v{e}p\'anov\'a, Eugene Shakhnovich, V\'aclav Ka\v{s}i\v{c}ka, Martin Dra\v{c}\'insk\'y, Zlatko Janeba, Rafael G\'omez-Bombar ·

    Computational Design and Experimental Validation of Photoactive PARP1 Inhibitors

    arXiv:2604.24634v1 Announce Type: cross Abstract: Light-activated drugs are a promising way to treat localized diseases for which existing treatments have severe side effects. However, their development is complicated by the set of photophysical and biological properties that mus…

  2. arXiv cs.LG TIER_1 English(EN) · Rafael Gómez-Bombarelli ·

    Computational Design and Experimental Validation of Photoactive PARP1 Inhibitors

    Light-activated drugs are a promising way to treat localized diseases for which existing treatments have severe side effects. However, their development is complicated by the set of photophysical and biological properties that must be simultaneously optimized. Here we used comput…

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