Introduction: Machine learning methods, coupled with a tremendous increase in computer power in recent years, are promising tools in modern drug design and drug repurposing. Methods: Machine learning predictive models, publicly available at chemosophia. com, were used to predict the bioactivity of recently synthesized platinum(IV) complexes against different kinds of diseases and medical conditions. Two novel QSAR models based on the BiS algorithm are developed and validated, capable to predict activities against the SARS-CoV virus and its RNA dependent RNA polymerase. Results: The internal predictive power of the QSAR models was tested by 10-fold cross-validation, giving cross-R 2 from 0. 863 to 0. 903. 38 different activities, ranging from antioxidant, antibacterial, and antiviral activities, to potential anti-inflammatory, anti-arrhythmic and anti-malarial activity were predicted for a series of eighteen platinum(IV) complexes. Conclusion: Complexes 1, 3 and 13 have high generalized optimality criteria and are predicted as potential SARS-CoV RNA dependent RNA polymerase inhibitors.

Background: Coronavirus (CoV) is a group of viruses that cause disease in humans and animals. These viruses contain crown-shaped spike glycoproteins on their surface. Objective: We conducted a quantitative structure-activity relationship (QSAR) study on a series of 36 compounds of allicin to assess their antiviral activities against the main protease of COVID-19. Methods: In the present descriptive-analytic study, the information on the structure of compounds, the COVID-19 protease enzyme, and the Allicin derivatives was obtained from the databases such as the Research Collaboratory for Structural Bioinformatics’,Protein Data Bank (PDB) and PubChem. The QSAR method, analysis of correlations and multiple linear regressions were carried out. Six molecular descriptors such as constitutional and molecular topology descriptors were selected for the model. Finally, molecular docking was performed in iGEMDOCK 2. 1 software. Results: The obtained multi-parametric model reported a correlation coefficient of about 0. 89, indicating that the model was able to satisfactory predict the antiviral activity of allicin compounds. Conclusion: The findings obtained can be valuable in designing, synthesizing, and developing novel antiviral agents with allicin-based scaffold.

Background: Plasma protein binding is a key component in drug therapy as it affects the pharmacokinetics and pharmacodynamics of drugs. Objectives: This study aimed to predict the fraction of plasma protein binding. Methods: A quantitative structure-activity relationship, convolutional neural network, and feed-forward neural network (QSAR-CNN-FFNN) methodology was used. CNN was used for feature selection, which is known as a difficult task in QSAR studies. The values of the descriptors acquired without the preprocessing procedures were rearranged into matrices, and features from a deep fully connected layer of a pre-trained CNN (ALEXNET) were extracted. Then, the latest features learned from the CNN layers were flattened out and passed through an FFNN to make predictions. Results: The external accuracy of the validation set (Q2=0. 945, RMSE=0. 085) showed the performance of this methodology. Another extremely favorable circumstance of this method is that it does not take a lot of time (only a few minutes) compared to the QSAR-Wrapper-FFNN method (days of hard work and concentration) and it automatically gives us the characteristics that are the best representations of our input. Conclusion: We can say that this model can be used to predict the fraction of human plasma protein binding for drugs that have not been tested to avoid chemical synthesis and reduce expansive laboratory tests.

Considering the diversified pharmacological importance of thiazole and triazole heterocyclic moieties, a unique series of S-aralkylated bi-heterocyclic hybrids, 7a-l, was synthesized in a convergent manner. The structures of newly synthesized compounds were characterized by 1 H-NMR, 13 C-NMR, IR, and EI-MS spectral studies. The structure-activity relationship of these compounds was envisaged by analyzing their inhibitory effects against tyrosinase, whereby all these molecules exhibited potent inhibitory potentials relative to the standard used. The Kinetics mechanism was ascertained by Lineweaver-Burk plots, which revealed that 7g inhibited tyrosinase non-competitively by forming an enzyme-inhibitor complex. The inhibition constants Ki calculated from Dixon plots for this compound was 0. 0057µ M. These bi-heterocyclic molecules also disclosed good binding energy values (kcal/mol) when assessed computationally. So, these molecules can be considered promising medicinal scaffolds for the treatment of skin disorders.

Densely functionalized 3-(4-chlorophenyl)-5-(3- hydroxy-4-etoxyphenyl)-4,5-dihydro-1H-pyrazole-1- carboxamide was synthesized in an expedient manner through specification and transamidation respectively, of ester-functionalized pyrazoles. This synthetic protocol allowed for three diversifying steps in which appendages on the pyrazole scaffold were adjusted to optimize inhibition of protein kinases. Computational design and study of novel 3-(4-chlorophenyl)-5- (3hydroxy-4-etoxyphenyl)-4,5-dihydro-1H-pyrazole-1- carboxamide is reported. This computational prediction analysis will improve the understanding of candidate drugs and help in identifying its properties and effects on the human body. Simulation analysis of candidate drugs is necessary for providing clues about regulatory mechanisms, biochemical pathways and broader drug functions.

Water Water deficit especially after heading is one of the most effective factors of wheat yield loss in Iran and across the world and identifying the physiological traits related to plant resilience is of great importance in screening for tolerant varieties. The objective of this study was to evaluate the variation in stem reserves accumulation capacity and its relationship with remobilization in some wheat cultivars. 22 wheat cultivars were cultivated in a split plot design with three replications, full irrigation and 40% of field capacity being main plots. The amount of accumulation and remobilization was estimated by measuring internodes weight and stem soluble carbohydrates content (WSC). Significant genotypic variation among cultivars was observed under both normal and drought conditions. Maximum accumulation was found in lower internodes followed by peduncle and penultimate using dry weight measurement method. In WSC measurement method, however, maximum accumulation was observed in penultimate followed by lower internodes and peduncle. Cultivars ranking based on accumulation was also different in two methods. Drought stress caused decline in internodes weight, WSC and yield and remobilization efficiency. The specific stem weight had the highest correlation with remobilization. Significant correlation was found between grain yield and remobilization, specific stem weight and WSC.

Purpose: Food security is a critical global challenge that is influenced by research and innovation in the field. Therefore, the objective of this study is to analyze the scientific output of developing countries in food security and examine its relationship with patents and Gross Domestic Product (GDP).Methodology: This applied research utilized the Scientometric approach. A total of 8,416 papers published between 1992-2023 in the field of food security by developing countries were included in the study using citation databases from Clarivate Analytics. Additionally, patent registrations from the WIPO database and GDP data from the World Bank were analyzed. Information was collected through note-taking, and the data was analyzed using Pearson's correlation coefficient.Findings: The findings reveal an upward trend in the publication and citation of scientific outputs related to food security in developing countries. China has higher numbers of papers, patents, GDP, and food production index compared to Iran, Japan, and South Korea. There is also a positive correlation observed between population and the number of papers, gross production and the number of papers, food production and the number of published papers, as well as the number of patents and papers citing scientific outputs of countries.Conclusion: These results highlight the significant relationship between increasing scientific output, GDP, the number of patents, and food security. Greater emphasis on food security contributes to enhanced scientific output, GDP, and innovation. Similarly, increasing scientific output, GDP, and innovation positively impact food security in countries.Value: This study emphasizes the importance of scientific outputs in driving technological advancements, innovations, and ultimately, ensuring food security in developing countries.