Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.

Introduction: Hemagglutinin (HA) is responsible for facilitating viral entry and infection by promoting the fusion between the host membrane and the virus. Given its significance in the process of influenza virus infestation, HA has garnered attention as a target for influenza drug and vaccine development. Thus, accurately identifying HA is crucial for the development of targeted vaccine drugs. However, the identification of HA using in-silico methods is still lacking. This study aims to design a computational model to identify HA.Methods: In this study, a benchmark dataset comprising 106 HA and 106 non-HA sequences were obtained from UniProt. Various sequence-based features were used to formulate samples. By perform feature optimization and inputting them four kinds of machine learning methods, we constructed an integrated classifier model using the stacking algorithm.Results and discussion: The model achieved an accuracy of 95.85% and with an area under the receiver operating characteristic (ROC) curve of 0.9863 in the 5-fold cross-validation. In the independent test, the model exhibited an accuracy of 93.18% and with an area under the ROC curve of 0.9793. The code can be found from https://github.com/Zouxidan/HA_predict.git. The proposed model has excellent prediction performance. The model will provide convenience for biochemical scholars for the study of HA.

Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.

Accurate prediction of subcellular localization of viral proteins is crucial for understanding their functions and developing effective antiviral drugs. However, this task poses a significant challenge, especially when relying on expensive and time-consuming classical biological experiments. In this study, we introduced a computational model called E-MuLA, based on a deep learning network that combines multiple local attention modules to enhance feature extraction from protein sequences. The superior performance of the E-MuLA has been demonstrated through extensive comparisons with LSTM, CNN, AdaBoost, decision trees, KNN, and other state-of-the-art methods. It is noteworthy that the E-MuLA achieved an accuracy of 94.87%, specificity of 98.81%, and sensitivity of 84.18%, indicating that E-MuLA has the potential to become an effective tool for predicting virus subcellular localization.

Background: Heart failure (HF) is a leading cause of mortality and represents the final stage of various cardiovascular disorders. Although traditional Chinese herbs have been extensively applied in HF treatment and their clinical efficacy has been investigated, the underlying molecular mechanisms remain insufficiently understood. To address this gap, systematic approaches are required to elucidate the therapeutic basis of herbal interventions. Methods: In this study, we systematically analyzed the bioactive compounds from seven traditional Chinese herbs, Baiguo (Ginkgo biloba), Chishao (Radix Paeoniae Rubra), Biba (Piper longum), Aidicha (Ilex latifolia), Bajiaolian (Dysosma spp.), Beiwuweizi (Schisandra chinensis), and Baiqucai (Sedum sarmentosum) and explored their potential mechanisms in HF by integrating network pharmacology, molecular docking, and molecular dynamics simulations. Result: We identified key targets and pathways implicated in HF pathogenesis and herbal interventions. A total of 63 active compounds were found to regulate 1947 genes. Through integrative analysis of the GSE57338 heart failure dataset from the GEO database, we further identified 265 intersecting targets shared between herb-associated genes and HF-related genes, highlighting their potential involvement in HF progression. Network analysis prioritized three hub proteins, STAT3, SRC, and TP53, which were subsequently subjected to molecular docking with the top bioactive compounds (quercetin, kaempferol, and epigallocatechin-3-gallate). Docking studies revealed strong binding affinities, and molecular dynamics (MD) simulations further validated the stability of these protein compound interactions. Conclusions: This study elucidates key bioactive components and targets involved in HF treatment, with kaempferol and epigallocatechin-3-gallate emerging as promising therapeutic candidates. These results provide a foundation for future experimental validation and the development of targeted HF therapies derived from traditional Chinese medicine.