论文标题
使用CDK描述符预测药物目标相互作用的方法
A Methodology for the Prediction of Drug Target Interaction using CDK Descriptors
论文作者
论文摘要
检测可能的药物靶标相互作用（DTI）是药物发现中的关键任务。常规的DTI研究是昂贵的，劳动密集型的，并且需要大量时间，因此有重要的理由来构建有用的计算技术，这些计算技术可能会成功预期可能的DTI。尽管已经开发出某些方法是为此原因，但尚未发现许多相互作用，预测准确性仍然很低。为了应对这些挑战，我们提出了建立在药物分子结构和靶蛋白序列的DTI预测模型。在提出的模型中，我们使用简化的分子输入线进入系统（Smiles）来创建CDK描述符，分子访问系统（MACC）指纹，电动学状态（势能）指纹（遗产）指纹和靶标的氨基酸序列以获得伪氨基酸组成（PSEAAC）。我们针对使用CDK描述符评估DTI预测模型的性能。为了进行比较，我们使用基准数据并评估模型性能在两个广泛使用的指纹，MACC指纹和遗产指纹上。性能的评估表明，CDK描述符在预测DTI方面表现出色。该提出的方法还胜过其他先前发表的技术。
Detecting probable Drug Target Interaction (DTI) is a critical task in drug discovery. Conventional DTI studies are expensive, labor-intensive, and take a lot of time, hence there are significant reasons to construct useful computational techniques that may successfully anticipate possible DTIs. Although certain methods have been developed for this cause, numerous interactions are yet to be discovered, and prediction accuracy is still low. To meet these challenges, we propose a DTI prediction model built on molecular structure of drugs and sequence of target proteins. In the proposed model, we use Simplified Molecular Input Line Entry System (SMILES) to create CDK descriptors, Molecular ACCess System (MACCS) fingerprints, Electrotopological state (Estate) fingerprints and amino acid sequences of targets to get Pseudo Amino Acid Composition (PseAAC). We target to evaluate performance of DTI prediction models using CDK descriptors. For comparison, we use benchmark data and evaluate models performance on two widely used fingerprints, MACCS fingerprints and Estate fingerprints. The evaluation of performances shows that CDK descriptors are superior at predicting DTIs. The proposed method also outperforms other previously published techniques significantly.
