The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.

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Excellent prediction modeling of CO2 solubility in polymers using hybrid computation algorithm.

Solubility is one of the most important physicochemical properties of polymer compounds, which determines the compatibility of blending system. To enhance the performance of artificial neural networks (ANN) and improve the efficiency and correlation of prediction of gas solubility in polymers, in this work, a novel ANN model based on chaos theory, self-adaptive particle swarm optimization (PSO) algorithm and back propagation (BP) algorithm is proposed, hereafter called CSAPSO-BP ANN. In the CSAPSO-BP ANN, the conventional PSO algorithm is modified by using chaos theory and self-adaptive inertia weight factor to overcome its premature convergence problem. Then the CSAPSO-BP ANN trained by hybrid algorithm which combined the modified PSO and BP algorithm has been employed to investigate carbon dioxide (CO 2 ) solubility in polystyrene (PS), polypropylene (PP) and nitrogen (N 2 ) solubility in PS, respectively. The CSAPSO-BP ANN model which consisted of three layers with one hidden layer, two input nodes including temperature and pressure, 8 hidden nodes which obtained by heuristics and one output node that is the solubility of gases in polymers was designed. The model combined the abilities of chaos theory, PSO algorithm and BP algorithm, accelerated the training speed of ANN and improved the prediction accuracy. Results obtained in this work indicate that the CSAPSO-BP ANN is an effective method for prediction of gas solubility in polymers in a wide range of pressure and temperature. The comparison between different neural networks was carried out in detail to reveal the proposed CSAPSO-BP ANN outperforms the traditional BP NN and PSO-BP NN. The values of average absolute deviation (AAD), standard deviation (SD) and squared correlation coefficient (R 2 ) are 0.0058, 0.0198 and 0.9914, respectively. The statistical data demonstrate that the CSAPSO-BP ANN model is a faster, more reliable and accurate method, and has an excellent prediction capability with high-accuracy and has a good correlation between prediction values and experimental data.

A novel model based on a radial basis function neural network (RBF NN), chaos theory, self-adaptive particle swarm optimization (PSO), and a clustering method is proposed to predict the gas solubility in polymers; this model is hereafter called CSPSO-C RBF NN. To develop the CSPSO-C RBF NN, the conventional PSO was modified with chaos theory and a selfadaptive inertia weight factor to overcome its premature convergence problem. The classical k-means clustering method was used to tune the hidden centers and radial basis function spreads, and the modified PSO algorithm was used to optimize the RBF NN connection weights. Then, the CSPSO-C RBF NN was used to investigate the solubility of N 2 in polystyrene (PS) and CO 2 in PS, polypropylene, poly(butylene succinate), and poly(butylene succinate-co-adipate). The results obtained in this study indicate that the CSPSO-C RBF NN was an effective method for predicting the gas solubility in polymers. In addition, compared with conventional RBF NN and PSO neural network, the CSPSO-C RBF NN showed better performance. The values of the average relative deviation, squared correlation coefficient, and standard deviation were 0.1282, 0.9970, and 0.0115, respectively. The statistical data demonstrated that the CSPSO-C RBF NN had excellent prediction capabilities with a high accuracy and a good correlation between the predicted values and the experimental data.

To replace costly and time-consuming experimentation in laboratory, a novel solubility prediction model based on chaos theory, self-adaptive particle swarm optimization (PSO), fuzzy c-means clustering method, and radial basis function artificial neural network (RBF ANN) is proposed to predict CO 2 solubility in polymers, hereafter called CSPSO-FC RBF ANN. The premature convergence problem is overcome by modifying the conventional PSO using chaos theory and self-adaptive inertia weight factor. Fuzzy c-means clustering method is used to tune the hidden centers and radial basis function spreads. The modified PSO algorithm is employed to optimize the RBF ANN connection weights. Then, the proposed CSPSO-FC RBF ANN is used to investigate solubility of CO 2 in polystyrene (PS), polypropylene (PP), poly(butylene succinate) (PBS) and poly(butylene succinate-co-adipate) (PBSA), respectively. Results indicate that CSPSO-FC RBF ANN is an effective method for gas solubility in polymers. In addition, compared with conventional RBF ANN and PSO ANN, CSPSO-FC RBF ANN shows better performance. The values of average relative deviation (ARD), squared correlation coefficient (R 2 ) and standard deviation (SD) are 0.1071, 0.9973 and 0.0108, respectively. Statistical data demonstrate that CSPSO-FC RBF ANN has excellent prediction capability and high-accuracy, and the correlation between prediction values and experimental data is good.

A four-layer fuzzy neural network (FNN) model combining particle swarm optimization (PSO) algorithm and clustering method is proposed to predict the solubility of gases in polymers, hereafter called the CPSO-FNN, which combined fuzzy theory's better adaptive ability, neural network's capability of nonlinear and PSO algorithm's global search ability. In this article, the CPSO-FNN model has been employed to investigate solubility of CO 2 in polystyrene, N 2 in polystyrene, and CO 2 in polypropylene, respectively. Results obtained in this work indicate that the proposed CPSO-FNN is an effective method for the prediction of gases solubility in polymers. Meanwhile, compared with traditional FNN, this method shows a better performance on predicting gases solubility in polymers. The values of average relative deviation, squared correlation coefficient (R 2 ) and standard deviation are 0.135, 0.9936, and 0.0302, respectively. The statistical data demonstrate that the CPSO-FNN has an outstanding prediction accuracy and an excellent correlation between prediction values and experimental data.Recent years, the interdiscipline of information science and intelligent technology has a broad application perspective. 4,12,13 With the popularization of artificial neural networks (ANN), the determination of ANN structure, parameters and bias becomes the most crucial factors because the training process of ANN could be considered as a classical optimization problem. 12 Recently, researchers discovered that many intelligent algorithms such as genetic algorithm, 13 simulated annealing algorithm, 14 fuzzy logic theory, 15 gravitational search algorithm, 16 wavelet analysis, 17 ant colony optimization algorithm, 18 particle swarm optimization algorithm (PSO), 12,19-21 chaos theory, 22 and so on, can all be used for this determination. Therefore, ANN combined with intelligent optimization algorithms namely hybrid neural network has become one of the most active subject.So far as solubility of gases in polymers is concerned, it is affected by temperature, pressure, and sometimes it can also be affected by the interactions with the groups of the macromolecular chains. 23 As a result of the nonlinear relationship of these

Flavonoids from natural products are well-identified as potential antiviral agents in the treatment of SARS-CoV-2 (COVID-19) infection and related diseases. However, some major species of flavonoids from Chinese traditional folk medicine, such as of Artemisia argyi (A. argyi), have not been evaluated yet. Here, we choose five major flavonoids obtained from A. argyi, namely, Jaceosidin (1), Eupatilin (2), Apigenin (3), Eupafolin (4), and 5,6-Dihydroxy-7,3′,4′-trimethoxyflavone (5), compared to the well-studied Baicalein (6), as potential inhibitors analogs for COVID-19 by computational modeling strategies. The frontier molecular orbitals (FMOs), chemical reactivity descriptors, and electrostatic surface potential (ESP) were performed by density functional theory (DFT) calculations. Additionally, these flavonoids were docked on the main protease (PDB: 6LU7) of SARS-CoV-2 to evaluate the binding affinities. Computational analysis predicted that all of these compounds show a high affinity and might serve as potential inhibitors to SARS-CoV-2, among which compound (5) exhibits the least binding energy (−155.226 kcal/mol). The high binding affinity could be enhanced by increasing the electron repulsion due to the valence shell electron pair repulsion model (VSEPR). Consequently, the major flavonoids in Artemisia argyi have a significant ability to reduce the deterioration of COVID-19 in the terms of DFT calculations and molecular docking.