论文标题
EISA得分：蛋白质 - 配体结合亲和力预测的元素交互式表面积评分
EISA-Score: Element Interactive Surface Area Score for Protein-Ligand Binding Affinity Prediction
论文作者
论文摘要
分子表面表示已被宣传为研究蛋白质结构和功能的重要工具，包括蛋白质 - 配体结合亲和力建模。但是，常规的基于表面区域的方法无法在能源评分任务上提供竞争性能。主要原因是缺乏在分子表面世代编码的至关重要的物理和化学相互作用。我们提出了新颖的分子表面表示，并以元素相互作用的不同尺度嵌入，具有巨大的尺寸还原和准确的物理和生物学特性编码器。那些基于表面的低维描述符已准备好与任何先进的机器学习算法配对，以探索基本的结构活性关系，从而引起元素互动互动表面积的评分功能（EISA-SCORE）。新开发的EISA得分在标准PDBBIND基准中优于许多最先进的模型，包括各种与表面相关的各种形式。
Molecular surface representations have been advertised as a great tool to study protein structure and functions, including protein-ligand binding affinity modeling. However, the conventional surface-area-based methods fail to deliver a competitive performance on the energy scoring tasks. The main reason is the lack of crucial physical and chemical interactions encoded in the molecular surface generations. We present novel molecular surface representations embedded in different scales of the element interactive manifolds featuring the dramatically dimensional reduction and accurately physical and biological properties encoders. Those low-dimensional surface-based descriptors are ready to be paired with any advanced machine learning algorithms to explore the essential structure-activity relationships that give rise to the element interactive surface area-based scoring functions (EISA-score). The newly developed EISA-score has outperformed many state-of-the-art models, including various well-established surface-related representations, in standard PDBbind benchmarks.
