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The radical chain-growth polymerization mechanism consists of three steps: initiation, propagation, and termination of polymerization. The polymerization initiates when a free radical generated from the radical initiator adds to the unsaturated bond in the monomer. The unpaired electron of the free radical and one π electron in the unsaturated bond creates a σ bond between the free radical and the monomer. As a result, the other π electron in the unsaturated bond converts this...
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    这项研究引入了一种新的"排序损失"功能,以加速制药药物发现. 这种新方法比传统方法更有效地优化分子特性,尤其是在有限的数据的情况下.

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    科学领域:

    • 计算化学是一种计算化学.
    • 机器学习在药物发现中的作用
    • 生物信息学是一种生物信息学.

    背景情况:

    • 制药药物发现是一个漫长而昂贵的过程.
    • 机器学习和生物信息学正在加速药物发现.
    • 目前的方法使用神经网络来优化分子表示,但与未知的属性配方和有限的数据作斗争.

    研究的目的:

    • 提出一种新的损失函数,用于优化分子发现中的化学性质.
    • 解决现有方法在处理未知属性函数和数据稀缺方面的局限性.

    主要方法:

    • 开发了一个新的"排序损失"函数,该函数强制执行基于属性值的分子排序.
    • 将订单损失的性能与传统的平均平方误差 (MSE) 对属性优化进行了比较.
    • 利用神经网络来优化代表分子的潜在空间向量.

    主要成果:

    • 订单损失在优化黑子属性功能方面显著超过MSE.
    • 拟议的方法通过反射黑子函数的模式,有效地捕捉化学性质的变化.
    • 在数据稀缺限制下表现出卓越的性能.

    结论:

    • 顺序损失为分子发现优化建立了一个新的最先进的框架.
    • 这种方法提供了一种更有效的方法来优化分子性质,特别是当数据有限时.
    • 显示了黑盒功能优化中更广泛应用的潜力.