Abstract:

Drug-target prediction method can effectively reduce costs and accelerate research process compared with traditional drug discovery. However， there are various challenges such as low balance of datasets and low precision of prediction in practical applications. Therefore， a drug-target interaction prediction method based on self-adaptive spherical evolution was proposed， namely ASE-KELM （self-Adaptive Spherical Evolution based on Kernel Extreme Learning Machine）. By the method， negative samples with high confidence were selected based on the principle that drugs with similar structures are likely to interact with targets. And to solve the problem that spherical evolution algorithm tends to fall into local optima， the feedback mechanism of historical memory of search factors and Linear Population Size Reduction （LPSR） were used to balance global and local search， which improved the optimization ability of the algorithm. Then the parameters of Kernel Extreme Learning Machine （KELM） were optimized by the self-adaptive spherical evolution algorithm. ASE-KELM was compared with algorithms such as NetLapRLS （Network Laplacian Regularized Least Square） and BLM-NII （Bipartite Local Model with Neighbor-based Interaction profile Inferring） on gold standard based datasets to verify the performance of the algorithms. Experimental results show that ASE-KELM outperforms comparison algorithms in AUC （Area Under the receiver operating Characteristic curve） and AUPR （Area Under the Precision-Recall curve） for the Enzyme （E）， G-Protein-Coupled Receptor （GPCR）， Ion Channel （IC）， and Nuclear Receptor （NR） datasets. And the effectiveness of ASE-KELM in predicting new drug-target pairs was validated on databases such as DrugBank.

Key words: spherical search, Kernel Extreme Learning Machine (KELM), drug-target interaction, drug discovery, self-adaptive

摘要：

相较于传统药物的研发，药物-靶标的预测方法能够有效降低成本，加快研发进程，但是在实际应用中存在数据集平衡度低、预测精确率不高等问题。基于此，提出一种自适应球形演化的药物-靶标相互作用预测方法ASE-KELM（self-Adaptive Spherical Evolution based on Kernel Extreme Learning Machine）。该方法根据结构相似的药物与靶标更易存在相互作用的原理筛选出高置信度的负样本；并且为了解决球形演化算法易陷入局部最优的问题，利用搜索因子历史记忆的反馈机制及群大小线性递减的策略（LPSR），实现全局搜索和局部搜索的平衡，提高算法的寻优能力；然后利用自适应球形演化算法对核极限学习机（KELM）的参数进行优化。在基于黄金标准的数据集上将ASE-KELM与NetLapRLS（Network Laplacian Regularized Least Square）、BLM-NII（Bipartite Local Model with Neighbor-based Interaction profile Inferring）等算法进行对比，验证算法的性能。实验结果表明，在酶（E）、G-蛋白偶联受体（GPCR）、离子通道（IC）和核受体（NR）数据集中，ASE-KELM的ROC曲线下面积（AUC）与PR曲线下面积（AUPR）均优于对比算法；且基于DrugBank等数据库，ASE-KELM在预测新药物-靶标对的验证过程中表现良好。

关键词: 球形搜索, 核极限学习机, 药物-靶标相互作用, 药物发现, 自适应