Drug-target interaction prediction by learning from local information and neighbors
Drug-target interaction prediction by learning from local information and neighbors
复制标题
DOI：
10.1093/bioinformatics/bts670
复制
发表时间：
2013-01-15
期刊：
影响因子：
5.8
通讯作者：
Zheng, Jie
中科院分区：
文献类型：
--
作者：
Mei, Jian-Ping;Kwoh, Chee-Keong;Zheng, Jie
动机：计算机模拟方法提供了预测药物和靶标之间可能相互作用的有效方法。监督学习方法，二分局部模型（BLM），最近被证明是有效的预测药物-靶标相互作用。然而，对于药物候选化合物或目标候选蛋白，目前没有已知的相互作用，其纯“本地”模型是无法学习，因此BLM可能无法作出正确的预测时，涉及这样的新candides.Results：我们提出了一个简单的程序，称为基于邻居的相互作用轮廓推断（NII），并将其集成到现有的BLM方法来处理新的候选人问题。具体地，推断的交互简档被视为标签信息，并用于新候选的模型学习。该功能在实践中对于寻找新的候选药物化合物的靶点和鉴定新的候选靶点蛋白质的靶向药物特别重要。新的BLM-NII方法在预测药物与四类靶蛋白之间相互作用的实验中观察到了一致的良好性能。特别是对于核受体，BLM-NII实现了最显著的改进，因为该数据集包含许多药物/靶标，在交叉验证中没有相互作用。这证明了NII策略的有效性，也显示了BLM-NII用于预测化合物-蛋白质相互作用的巨大潜力。
Motivation: In silico methods provide efficient ways to predict possible interactions between drugs and targets. Supervised learning approach, bipartite local model (BLM), has recently been shown to be effective in prediction of drug-target interactions. However, for drug-candidate compounds or target-candidate proteins that currently have no known interactions available, its pure 'local' model is not able to be learned and hence BLM may fail to make correct prediction when involving such kind of new candidates.Results: We present a simple procedure called neighbor-based interaction-profile inferring (NII) and integrate it into the existing BLM method to handle the new candidate problem. Specifically, the inferred interaction profile is treated as label information and is used for model learning of new candidates. This functionality is particularly important in practice to find targets for new drug-candidate compounds and identify targeting drugs for new target-candidate proteins. Consistent good performance of the new BLM-NII approach has been observed in the experiment for the prediction of interactions between drugs and four categories of target proteins. Especially for nuclear receptors, BLM-NII achieves the most significant improvement as this dataset contains many drugs/targets with no interactions in the cross-validation. This demonstrates the effectiveness of the NII strategy and also shows the great potential of BLM-NII for prediction of compound-protein interactions.