A comparative study of deep learning models and classification algorithms for chemical compound identification and Tox21 prediction

• Published in: Computers & chemical engineering Vol. 189; p. 108805
• Main Authors: Alaca, Yusuf, Emin, Berkay, Akgul, Akif
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2024
• Subjects: Chemical property prediction; Convolutional neural network; Deep learning; Image processing; SMILES format; Deep learning; Chemical property prediction; Image processing; SMILES format; Convolutional neural network
• ISSN: 0098-1354
• DOI: 10.1016/j.compchemeng.2024.108805

•QR codes for the toxicity dataset have been generated.•Four models were compared with RF and KNN.•Compound classification is crucial in the field of chemistry.•The choice of model and algorithm influences the results.•The study presents a comparison of models and algorithms. Chemical compound classification, toxicity prediction, and environmental risk assessments are critically important in various applications within the field of chemistry. Deep learning models provide highly effective tools for extracting features from complex large datasets and performing classification tasks. Four different deep learning models, namely ResNet50V2, VGG19, InceptionV3, and MobileNetV2, have been compared with the random forest (RF) and k-nearest neighbors (KNN) algorithms. The results obtained from experiments conducted using QRCODE images of the Tox21SMILES dataset demonstrate the effectiveness of deep learning models for classifying chemical compounds and showcase the performance of different classification algorithms. The findings of the study thoroughly evaluate the performance of deep learning models and classification algorithms in the task of chemical classification. While ResNet50V2 and VGG19 models achieve high accuracy and precision, InceptionV3 and MobileNetV2 models provide more balanced results. Additionally, in terms of classification algorithms, the k-nearest neighbors (KNN) algorithm generally outperforms the Random Forest (RF) algorithm. Although the RF algorithm achieves good accuracy, the KNN algorithm proves to be more effective in terms of sensitivity and F1 score. These results emphasize the factors to consider when choosing which deep learning model or classification algorithm to use in chemical classification tasks. In conclusion, this study presents a comprehensive analysis comparing the performance of deep learning models and classification algorithms in chemical classification tasks. The selection of the most suitable model and algorithm for a specific task supports achieving better results in the classification of chemical compounds and related applications. [Display omitted]