Despite the progress made in studying protein-ligand interactions and the widespread application of docking and affinity prediction tools, improving their precision and efficiency still remains a challenge. Computational approaches based on the scoring of docking conformations with statistical potentials constitute a popular alternative to more accurate but costly physics-based thermodynamic sampling methods. In this context, a minimalist and fast sidechain-free knowledge-based potential with a high docking and screening power can be very useful when screening a big number of putative docking conformations.

中文翻译：

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KORP-PL：蛋白质-配体相互作用的基于知识的粗粒度评分功能。

尽管在研究蛋白质-配体相互作用方面取得了进展，并且对接和亲和力预测工具得到了广泛应用，但提高其精确度和效率仍然是一个挑战。基于对接构象的得分和统计潜力的计算方法构成了更准确但昂贵的基于物理的热力学采样方法的流行替代方法。在这种情况下，筛选大量假定的对接构象时，极简且快速的无侧链无知识基础的潜力具有很高的对接和筛选能力，将非常有用。