Evaluation of a Bayesian inference network for ligand-based virtual screening

Background Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query. Here, we modify the approach to enable a Bayesian inference network to be used for chemical similarity searching, where a database is ranked in order of decreasing probability of bioactivity. Results Bayesian inference networks were implemented using two different types of network and four different types of belief function. Experiments with the MDDR and WOMBAT databases show that a Bayesian inference network can be used to provide effective ligand-based screening, especially when the active molecules being sought have a high degree of structural homogeneity; in such cases, the network substantially out-performs a conventional, Tanimoto-based similarity searching system. However, the effectiveness of the network is much less when structurally heterogeneous sets of actives are being sought. Conclusion A Bayesian inference network provides an interesting alternative to existing tools for ligand-based virtual screening

Simulation Based Predictive Analysis of Indian Airport Transportation System Using Computational Intelligence Techniques

Normally, flight delays and cancellations have significant impact on airlines operations and passenger’s satisfaction. Flight delays reduce the performance of airline operations and make significant effect on airports on time performance. Previously statistical models have been used for flight delays analysis. This study was applied in Indian aviation industry and it has given statistical analysis of domestic airlines. In this research paper, we have applied Machine Learning models with the help of computational intelligence techniques for predicting airport transport management system. We have also applied computational intelligence techniques such as Particle Swarm Optimization (PSO) and Ant Colonization Optimization (ACO) to optimize the prediction model for delay period time and calculating the most optimal dependability. We have made comprehensive analysis of Data Efficiency Model for different airlines with various approaches as well as comparative analysis of accuracy for predicting airport model by using various machine learning models. In this study we have presented invaluable insights for the analysis of flight delay models

Advanced material against human (Including Covid‐19) and plant viruses: nanoparticles as a feasible strategy

The SARS‐CoV‐2 virus outbreak revealed that these nano‐pathogens have the ability to rapidly change lives. Undoubtedly, SARS‐CoV‐2 as well as other viruses can cause important global impacts, affecting public health, as well as, socioeconomic development. But viruses are not only a public health concern, they are also a problem in agriculture. The current treatments are often ineffective, are prone to develop resistance, or cause considerable adverse side effects. The use of nanotechnology has played an important role to combat viral diseases. In this review three main aspects are in focus: first, the potential use of nanoparticles as carriers for drug delivery. Second, its use for treatments of some human viral diseases, and third, its application as antivirals in plants. With these three themes, the aim is to give to readers an overview of the progress in this promising area of biotechnology during the 2017–2020 period, and to provide a glance at how tangible is the effectiveness of nanotechnology against viruses. Future prospects are also discussed. It is hoped that this review can be a contribution to general knowledge for both specialized and non‐specialized readers, allowing a better knowledge of this interesting topic.REDES‐ANID. Grant Number: 180003 Universidad de La Frontera. Grant Number: DI20‐1003 FAPESP. Grant Numbers: 2018/08194‐2, 2018/02832‐7 CNPq. Grant Numbers: 404815/2018‐9, 313117/2019‐5 CONICYT/FAPESP. Grant Number: 2018/08194‐2 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior. Grant Numbers: 001, ANID/FONDAP/15130015 FCT. Grant Number: PTDC/CTM‐TEX/28295/2017 FEDER POCI Portugal 2020 program COMPETE. Grant Number: UID/CTM/00264/2019 FCT/MCTE

Comparative 3D QSAR study on β1-, β2-, and β3-adrenoceptor agonists

A quantitative structure–activity relationship study of tryptamine-based derivatives of β1-, β2-, and β3-adrenoceptor agonists was conducted using comparative molecular field analysis (CoMFA). Correlation coefficients (cross-validated r2) of 0.578, 0.595, and 0.558 were obtained for the three subtypes, respectively, in three different CoMFA models. All three CoMFA models have different steric and electrostatic contributions, implying different requirements inside the binding cavity. The CoMFA coefficient contour plots of the three models and comparisons among these plots provide clues regarding the main chemical features responsible for the biological activity variations and also result in predictions which correlate very well with the observed biological activity. Based on the analysis, a summary regeospecific description of the requirements for improving β-adrenoceptor subtype selectivity is given

Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR

Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR) which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR) analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses). The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators) were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5α-androstan-3β-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches

Application of Consensus Scoring and Principal Component Analysis for Virtual Screening against β-Secretase (BACE-1)

BACKGROUND: In order to identify novel chemical classes of β-secretase (BACE-1) inhibitors, an alternative scoring protocol, Principal Component Analysis (PCA), was proposed to summarize most of the information from the original scoring functions and re-rank the results from the virtual screening against BACE-1. METHOD: Given a training set (50 BACE-1 inhibitors and 9950 inactive diverse compounds), three rank-based virtual screening methods, individual scoring, conventional consensus scoring and PCA, were judged by the hit number in the top 1% of the ranked list. The docking poses were generated by Surflex, five scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, and PMF_Score) were used for pose extraction. For each pose group, twelve scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, PMF_Score, LigScore1, LigScore2, PLP1, PLP2, jain, Ludi_1, and Ludi_2) were used for the pose rank. For a test set, 113,228 chemical compounds (Sigma-Aldrich® corporate chemical directory) were docked by Surflex, then ranked by the same three ranking methods motioned above to select the potential active compounds for experimental test. RESULTS: For the training set, the PCA approach yielded consistently superior rankings compared to conventional consensus scoring and single scoring. For the test set, the top 20 compounds according to conventional consensus scoring were experimentally tested, no inhibitor was found. Then, we relied on PCA scoring protocol to test another different top 20 compounds and two low micromolar inhibitors (S450588 and 276065) were emerged through the BACE-1 fluorescence resonance energy transfer (FRET) assay. CONCLUSION: The PCA method extends the conventional consensus scoring in a quantitative statistical manner and would appear to have considerable potential for chemical screening applications