Prediction of Water Diffusion in Wide Varieties of Polymers with All-Atom Molecular Dynamics Simulations and Deep Generative Models.

Permeation through polymer membranes is an important technology in the chemical industry, and in its design, the self-diffusion coefficient is one of the physical quantities that determine permeability. Since the self-diffusion coefficient sensitively reflects intra- and intermolecular interactions, analysis using an all-atom model is required. However, all-atom simulations are computationally expensive and require long simulation times for the diffusion of small molecules dissolved in polymers. MD-GAN, a machine learning model, is effective in accelerating simulations and reducing computational costs. The target systems for MD-GAN prediction were limited to polyethylene melts in previous studies; therefore, this study extended MD-GAN to systems containing copolymers with branches and successfully predicted water diffusion in various polymers. The correlation coefficient between the predicted self-diffusion coefficient and that of the long-time simulation was 1.00. Additionally, we found that incorporating statistical domain knowledge into MD-GAN improved accuracy, reducing the mean-square displacement prediction outliers from 14.6% to 5.3%. Lastly, the distribution of latent variables with embedded dynamics information within the model was found to be strongly related to accuracy. We believe that these findings can be useful for the practical applications of MD-GAN.

MD-GAN with multi-particle input: the machine learning of long-time molecular behavior from short-time MD data.

Molecular dynamics simulation is a method of investigating the behavior of molecules, which is useful for analyzing a variety of structural and dynamic properties and mechanisms of phenomena. However, the huge computational cost of large-scale and long-time simulations is an enduring problem that must be addressed. MD-GAN is a machine learning-based method that can evolve part of the system at any time step, accelerating the generation of molecular dynamics data [Endo et al., Proceedings of the AAAI Conference on Artificial Intelligence, 2018, 32]. For the accurate prediction of MD-GAN, sufficient information on the dynamics of a part of the system should be included with the training data. Therefore, the selection of the part of the system is important for efficient learning. In a previous study, only one particle (or vector) of each molecule was extracted as part of the system. The effectiveness of adding information from other particles to the learning process is investigated in this study. When the dynamics of three particles of each molecule were used in the polyethylene experiment, the diffusion was successfully predicted using the training data with a time length of approximately 40%, compared to the single-particle input. Surprisingly, the unobserved transition of diffusion in the training data was also predicted using this method. The reduced cost for the generation of training MD data achieved in this study is useful for accelerating MD-GAN.

The efficacy of short-term administration of 3 antihistamines vs placebo under natural exposure to Japanese cedar pollen.

BACKGROUND: Japanese cedar pollinosis, a common disease with morbidity of approximately 20% in the Japanese population, is characterized by subjectively irritating symptoms during an annual 3-month period. OBJECTIVE: To investigate the effectiveness of cetirizine hydrochloride, loratadine, and fexofenadine hydrochloride in reducing pollinosis symptoms induced while walking in a park during the pollen season. METHODS: A randomized, double-masked, placebo-controlled trial was conducted in 113 individuals with Japanese cedar pollinosis during 2 days in March 2003 in Osaka Expo Park, Osaka, Japan. Participants (aged 20-57 years) were divided into 4 groups according to treatment assignment: cetirizine hydrochloride, 10 mg/d; fexofenadine hydrochloride, 120 mg/d; loratadine, 10 mg/d; and placebo (lactose), twice daily. Symptoms were recorded hourly during the study. Furthermore, all the patients completed the Japanese version of the Rhinoconjunctivitis Quality of Life Questionnaire before and after the trial. RESULTS: Self-evaluated symptom scores in all 3 active treatment groups showed significant improvements compared with the placebo group. Furthermore, the cetirizine group showed significant improvement in the domains of frequency of nose blowing and nasal obstruction compared with placebo. In addition, improvement in Japanese Rhinoconjunctivitis Quality of Life Questionnaire scores was higher in the cetirizine group than in the loratadine and placebo groups. CONCLUSION: Cetirizine seems to be more effective than fexofenadine and loratadine at reducing subjective symptoms in this study population.