PLS-DA - Docking Optimized Combined Energetic Terms (PLSDA-DOCET) Protocol: A Brief Evaluation

Docking studies have become popular approaches in drug design, where the binding energy of the ligand in the active site of the protein is estimated by a scoring function. Many promising techniques were developed to enhance the performance of scoring functions including the fusion of multiple scoring functions outcomes into a so-called consensus scoring function. Hereby, we evaluated the target oriented consensus technique using the energetic terms of several scoring functions. The approach was denoted PLSDA-DOCET. Optimization strategies for consensus energetic terms and scoring functions based on ROC metric were compared to classical rigid docking and to ligand-based similarity search methods comprising 2D fingerprints and ROCS. The ROCS results indicate large performance variations depending on the biological target. The AUC-based strategy of PLSDA-DOCET outperformed the other docking approaches regarding simple retrieval and scaffold-hopping. The superior performance of PLSDA-DOCET protocol relative to single and combined scoring functions was validated on an external test set. We found a relative low mean correlation of the ranks of the chemotypes retrieved by the PLSDA-DOCET protocol and all the other methods employed here.对接研究现已成为药物设计领域的主流研究范式，其通过打分函数对配体在蛋白质活性位点中的结合能进行估算。学界已开发出诸多极具潜力的技术以提升打分函数的性能，其中包括将多个打分函数的结果融合为所谓的共识打分函数。本研究中，我们借助多个打分函数的能量项，对面向靶点的共识技术进行了评估，该方法被命名为PLSDA-DOCET。我们将基于受试者工作特征（Receiver Operating Characteristic，ROC）指标的共识能量项与打分函数优化策略，与经典刚性对接方法，以及包含二维指纹（2D fingerprints）与ROCS的基于配体的相似性搜索方法进行了对比。ROCS的结果显示，模型性能随生物学靶点的不同存在显著差异。以受试者工作特征曲线下面积（Area Under Curve，AUC）为优化目标的PLSDA-DOCET策略，在简单检索与骨架跃迁任务上的表现优于其他对接方法。PLSDA-DOCET方案相较于单一打分函数与组合打分函数的优异性能，已在外部测试集上得到验证。我们发现，PLSDA-DOCET方案检索得到的化学型排序与本研究中其余所有方法的排序之间，平均相关性相对较低。