基于自然语言处理的蛋白质小分子亲和力值预测

Protein Small Molecule Affinity Prediction Based on Natural Language Processing

蛋白质与小分子的相互作用研究对药物的研发非常重要,而现有的蛋白质小分子亲和力值的预测方法存在成本高、精度低等问题.为此提出了一种新的蛋白质小分子亲和力值的预测方法,利用自然语言处理技术对蛋白质结构数据与小分子指纹数据进行处理,并利用梯度提升决策树模型进行预测.实验表明,该方法的精度较原有方案有较大提高.

The interaction between proteins and small molecules plays a very important role in drug research and development. However, the existing methods for predicting the affinity of small molecules have some problems, such as high cost and low accuracy. In this paper, a new protein small molecule affinity prediction method is proposed based on natural language processing(NLP) technology, which using NLP to analysis the protein structure data and small molecule fingerprint data, as well as using gradient boosting decision tree(GBDT) model to predict the affinity. Experiments show that the proposed method has performance over the exiting methods in terms of accuracy.