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Context: With the wide application of deep learning in drug research and development, de novo molecular design methods based on recurrent neural network (RNN) have strong advantages in drug molecule generation. The RNN model can be used to learn the internal chemical structure of molecules, which is similar to a natural language processing task. Although techniques for generating target-specific molecular libraries based on RNN models are mature, research related to drug design and screening continues around the clock. Research based on de novo drug design methods to generate larger quantities of valid compounds is necessary. Methods: In this study, a molecular generation model based on RNN was designed, which abandoned the traditional way of stacked RNN and introduced the Nested long short-term memory network structure. To enrich the library of focused molecules for specific targets, we fine-tuned the model using active molecules from novel coronavirus pneumonia and screened the molecules using machine learning models. Following rigorous screening, the selected molecules underwent molecular docking with the SARS-CoV-2 M-pro receptor using AutoDock2.4 to identify the top 3 potential inhibitors. Subsequently, 100-ns molecular dynamics simulations were conducted using Amber22. Molecule parameterization involved the GAFF2 force field, while the proteins were modeled using the ff19SB force field, with solvation facilitated by a truncated octahedral TIP3P solvent environment. Upon completion of molecular dynamics simulations, stability of ligand-protein complexes was assessed by analysis of RMSD, H-bonds, and MM-GBSA. Reasonable results prove that the model can complete the task of de novo drug design and has the potential to be ideal drug molecules. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) is a hazardous air pollutant with an aerodynamic diameter of 2.5 μm or less, which can lead to severe health impacts such as cardiovascular disease, respiratory illnesses, and various types of cancer. Therefore, accurate forecasting of PM2.5 concentrations is crucial for public health and policy-making. However, due to the stochastic nature of PM2.5, achieving high prediction accuracy and efficiency remains a challenge. To address this challenge, this study proposes a hybrid deep learning model consisting of principal component analysis (PCA), discrete stationary wavelet transform (DSWT), and Nested LSTM (NLSTM) neural network to predict PM2.5 concentrations. The proposed model aims to leverage the strengths of each technique to achieve better accuracy and efficiency in PM2.5 forecasting. Specifically, PCA is employed as the feature extraction method to reduce the dimensionality of the data and improve computing efficiency. Additionally, DSWT is utilized to decompose the reduced-dimensional data into several sub-signals that are more regular and stable, enabling the NLSTM network to learn each sub-signal separately. Finally, the predicted values of each sub-signal are reconstructed to obtain the final PM2.5 forecast. The proposed model is validated using daily air pollutants and meteorological variables collected in Taiyuan, China, from January 1, 2016, to December 31, 2020. The long-term, medium-term, and short-term forecast results demonstrate that the proposed model achieves better accuracy and efficiency compared to existing models. Overall, the proposed hybrid deep learning model provides a promising solution for accurate and efficient forecasting of PM2.5 concentrations, and the findings of this study have important implications for public health and environmental policy. [ABSTRACT FROM AUTHOR]

Residential electricity consumption forecasting plays a crucial role in the rational allocation of resources reducing energy waste and enhancing the grid-connected operation of power systems. Probabilistic forecasting can provide more comprehensive information for the decision-making and dispatching process by quantifying the uncertainty of electricity load. In this study, we propose a method based on stationary wavelet transform (SWT), quantile regression (QR), Bidirectional nested long short-term memory (BiNLSTM), and Depthwise separable convolution (DSC) combined with attention mechanism for electricity consumption probability prediction methods. First, the data sequence is decomposed using SWT to reduce the complexity of the sequence; then, the combined neural network model with attention is used to obtain the prediction values under different quantile conditions. Finally, the probability density curve of electricity consumption is obtained by combining kernel density estimation (KDE). The model was tested using historical demand-side data from five UK households to achieve energy consumption predictions 5 min in advance. It is demonstrated that the model can achieve both reliable probabilistic prediction and accurate deterministic prediction. [ABSTRACT FROM AUTHOR]

Residential electricity consumption forecasting plays a crucial role in the rational allocation of resources reducing energy waste and enhancing the grid-connected operation of power systems. Probabilistic forecasting can provide more comprehensive information for the decision-making and dispatching process by quantifying the uncertainty of electricity load. In this study, we propose a method based on stationary wavelet transform (SWT), quantile regression (QR), Bidirectional nested long short-term memory (BiNLSTM), and Depthwise separable convolution (DSC) combined with attention mechanism for electricity consumption probability prediction methods. First, the data sequence is decomposed using SWT to reduce the complexity of the sequence; then, the combined neural network model with attention is used to obtain the prediction values under different quantile conditions. Finally, the probability density curve of electricity consumption is obtained by combining kernel density estimation (KDE). The model was tested using historical demand-side data from five UK households to achieve energy consumption predictions 5 min in advance. It is demonstrated that the model can achieve both reliable probabilistic prediction and accurate deterministic prediction.