• ETRI Journal
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• 제46권2호
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• pp.290-306
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• 2024

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transferlearning methodology and a multilabel convolutional neural network for COVID (MLCNN-COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer-learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN-COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three-dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN-COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer-learning model's performance. It shows that three-dimensional conformers adequately identify negative COVID medicine responses.

• Bulletin of the Korean Chemical Society
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• 제34권7호
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• pp.1972-1984
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• 2013

Microtubules play an important role in intracellular transport, mobility, and particularly mitosis. Paclitaxel (Taxol$^{TM}$) and paclitaxel-like compounds have been shown to be anti-tumor agents useful for various human tumors. Paclitaxel-like compounds operate by stabilizing microtubules through interface binding at the interface between two ${\beta}$-tubulin monomers in adjacent protofilaments. In this paper we present the elucidation of the structural features of paclitaxel and paclitaxel-like compounds (e.g., epothilones) with microtubule stabilizing activities, and relate their activities to spatial and chemical features of the molecules. CATALYST program was used to generate three-dimensional quantitative structure activity relationships (3D-QSARs) resulting in 3D pharmacophore models of epothilone- and paclitaxel-derivatives. Pharmacophore models were generated from diverse conformers of these compounds resulting in a high correlation between experimental and predicted biological activities (r = 0.83 and 0.91 for epothilone and paclitaxel derivatives, respectively). On the basis of biological activities of the training sets, five- and four-feature pharmacophore hypotheses were generated in the epothilone and paclitaxel series. The validation of generated hypotheses was achieved by using twelve epothilones and ten paclitaxels, respectively, which are not in the training sets. The clustering (grouping) and merging techniques were used in order to supplement spatial restrictions of each of hypothesis and to develop more comprehensive models. This approach may be of use in developing novel inhibitor candidates as well as contributing a better understanding of structural characters of many compounds useful as anticancer agents targeting microtubules.