The computational prediction of ligand-biopolymer affinities is a crucial endeavor in modern drug discovery and one that still poses major challenges. The choice of the appropriate computational method often reveals itself as a trade-off between accuracy and speed, with mathematical devices referred to as scoring functions being the fastest. Among the many shortcomings of scoring functions there is the lack of universal applicability to every molecular system. This is so largely due to their reliance on atom type perception and/or parametrization. This article proposes the use of nonparametric Model of Effective Radii of Atoms descriptors that can be readily computed for the entire Periodic Table and demonstrate that, in combination with machine learning algorithms, they can yield competitive performances and chemically meaningful insights.

中文翻译：

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具有机器学习评分功能的配体-生物聚合物亲和力计算的非参数化学描述符。

配体-生物聚合物亲和力的计算预测是现代药物发现中的一项关键工作，并且仍然构成重大挑战。选择合适的计算方法通常会在准确性和速度之间做出权衡，这是最快的方法，而数学工具被称为得分函数。在评分功能的许多缺点中，缺乏对每个分子系统的普遍适用性。这在很大程度上是由于它们依赖于原子类型的感知和/或参数化。本文提议使用可以很容易地为整个元素周期表计算的非参数原子有效半径模型，并证明与机器学习算法结合使用，它们可以产生具有竞争力的性能和具有化学意义的见解。