Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, 76100, Melaka, Malaysia.
Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, 76100, Melaka, Malaysia.
Mol Divers. 2022 Jun;26(3):1609-1619. doi: 10.1007/s11030-021-10289-1. Epub 2021 Aug 2.
Amphetamine-type stimulants (ATS) drug analysis and identification are challenging and critical nowadays with the emergence production of new synthetic ATS drugs with sophisticated design compounds. In the present study, we proposed a one-dimensional convolutional neural network (1DCNN) model to perform ATS drug classification as an alternative method. We investigate as well as explore the classification behavior of 1DCNN with the utilization of the existing novel 3D molecular descriptors as ATS drugs representation to become the model input. The proposed 1DCNN model is composed of one convolutional layer to reduce the model complexity. Besides, pooling operation that is a standard part of traditional CNN is not applied in this architecture to have more features in the classification phase. The dropout regularization technique is employed to improve model generalization. Experiments were conducted to find the optimal values for three dominant hyper-parameters of the 1DCNN model which are the filter size, transfer function, and batch size. Our findings found that kernel size 11, exponential linear unit (ELU) transfer function and batch size 32 are optimal for the 1DCNN model. A comparison with several machine learning classifiers has shown that our proposed 1DCNN has achieved comparable performance with the Random Forest classifier and competitive performance with the others.
安非他命类兴奋剂（ATS）药物分析和鉴定具有挑战性和关键性，因为新的合成 ATS 药物层出不穷，具有复杂的设计化合物。在本研究中，我们提出了一种一维卷积神经网络（1DCNN）模型，作为替代方法来进行 ATS 药物分类。我们研究并探讨了 1DCNN 的分类行为，利用现有的新型 3D 分子描述符作为 ATS 药物的表示形式，成为模型输入。所提出的 1DCNN 模型由一个卷积层组成，以降低模型的复杂性。此外，在该架构中不应用传统 CNN 的标准池化操作，以便在分类阶段具有更多特征。采用辍学正则化技术来提高模型的泛化能力。进行了实验以找到 1DCNN 模型三个主要超参数的最优值，即滤波器大小、转移函数和批处理大小。我们的研究结果发现，对于 1DCNN 模型，核大小 11、指数线性单元（ELU）转移函数和批处理大小 32 是最优的。与几种机器学习分类器的比较表明，我们提出的 1DCNN 与随机森林分类器的性能相当，与其他分类器的性能具有竞争力。
