Despite attempts to control the spread of human immunodeficiency virus (HIV) through the use of anti‐HIV medications, the absence of an effective vaccine continues to present a significant obstacle. In addition, the development of drug resistance by HIV underscores the necessity for computational drug discovery methods to identify novel therapies. This investigation specifically focused on employing a long short‐term memory (LSTM) variational autoencoder deep‐learning architecture for computational drug discovery in relation to HIV. Our data set comprised simplified molecular input line entry system (SMILES)–encoded compounds, which were used to train the LSTM autoencoder. Remarkably, our model achieved a training accuracy of 91%, with a data set containing 1377 compounds. Leveraging the generative model derived from the training phase, we generated potential new drugs for combating HIV and assessed their interaction with the virus using a previously developed artificial intelligence model. Lastly, we verified the drug likeliness of our computationally generated compounds in accordance with Lipinski's rule of five. Overall, our study presents a promising approach to computational drug discovery in the ongoing battle against HIV.

The aim of this study was to investigate the performance measurement of supercapacitors using the electrochemical properties of cyclic voltammetry (CV). The use of CV is crucial in evaluating the electrochemical performance of supercapacitors and determining the surface area of the catalyst with regard to the fractal properties of the electrode. The study specifically focused on the CV behavior of a supercapacitor formed by a cobalt-doped ceria/reduced graphene oxide (Co-CeO₂/rGO) fractal nanocomposite, and its assessment was conducted using a machine learning (ML) model with the enhanced XGBoost. The model was trained using an experimental open-source dataset. The results showed that the proposed XGBoost model had a superior ability to predict the CV behavior of the supercapacitor, with nearly perfect results for the MAE, RMSE, and R-squared metrics, which are effective at evaluating the performance of regression models. With the successful design of the proposed intelligent prediction model, the study is expected to provide valuable insights into forming novel nanocomposite forms with high accuracy and minimal need for experiments.

In this study, CdZnO films prepared at different ratios of dopants (CdO:ZnO = 5:5, CdO:ZnO = 6:4, and CdO:ZnO = 8:2) were coated on glass surface by using the sol-gel spin coating technique. After this process, surface structure and optical properties of the CdZnO films was investigated by atomic force microscopy (AFM) and UV-Vis spectroscopy. The surface structure of the CdZnO films depended on the content of ZnO and CdO in the films. Low percentage of CdO films were very similar to the ZnO film but higher amount of CdO resuted in granular structures together with pure structure of ZnO in the films. Eg values of produced CdZnOs depended on the additions of CdO and ZnO. The obtained Eg values of the produced CdO:ZnO = 5:5 (S3), CdO:ZnO = 6:4 (S4), and CdO:ZnO = 8:2 (S5) films are 2.5 eV, 2.49 eV, and 2.4 eV, respectively.