scholarly journals Introducing a new category of activity cliffs combining different compound similarity criteria

2020 ◽  
Vol 11 (1) ◽  
pp. 132-141 ◽  
Author(s):  
Huabin Hu ◽  
Jürgen Bajorath
Keyword(s):  
Similarity Criteria ◽  
Structural Isomer ◽  
Molecular Pair ◽  
Similarity Relationships ◽  
Activity Cliffs

Similarity relationships. Shown are matched molecular pair (MMP) and structural isomer relationships, which provide the basis for the introduction of a new category of activity cliffs.

scholarly journals EXPERIENCE WITH MOVABLE BED TIDAL MODELS

1974 ◽  
Vol 1 (14) ◽  
pp. 61
Author(s):  
E. Giese ◽  
H. Harten ◽  
H. Vollmers
Keyword(s):  
Sediment Transport ◽  
Morphological Changes ◽  
Transport Processes ◽  
Similarity Criteria ◽  
Research Institutes ◽  
Special Cases ◽  
Similarity Relationships ◽  
Movable Bed ◽  
Insufficient Knowledge ◽  
Sea Coast

A particular problem, of those arising in the economic development of estuary regions, concerns the maintenance and enlargement of navigation channels. The sediment transport plays an important role in connection with this problem. .Though the hydrodynamical processes are today, with the help of mathematical procedures, fairly exactly grasped, there is still insufficient knowledge about the related transport processes, the formation of ripples and dunes and of longterm periodical morphological changes. Nevertheless, the engineer wants information about sediment transport for his planning. A well-known aid is the movable bed hydraulic model, which has been technically developed to simulate the natural fluid-sediment interaction. Such models are not yet standard in hydraulic research institutes and. furthermore, they are not easy to handle. This is probably due to a lack of suitable similarity criteria for insuring valid experimental results. However, there exist recently developed somewhat compromised similarity relationships, which can be used for distorted movable bed tidal models. The experience gained with the movable bed. model Elbe I at the Bundesanstalt fur Wasserbau (BAW) in Hamburg provides an incentive for investigating special cases in other large tidal models of the German North-Sea coast. These models are presented in Fig. 1.

Two isoprenylated flavonoids from Dorstenia psilurus activate AMPK, stimulate glucose uptake, inhibit glucose production and lower glycemia

2019 ◽  
Vol 476 (24) ◽  
pp. 3687-3704 ◽  
Author(s):  
Aphrodite T. Choumessi ◽  
Manuel Johanns ◽  
Claire Beaufay ◽  
Marie-France Herent ◽  
Vincent Stroobant ◽  
...  
Keyword(s):  
Glucose Uptake ◽  
Human Cancer ◽  
Glucose Production ◽  
Liver Mitochondria ◽  
New Drugs ◽  
Structural Isomer ◽  
Rat Liver Mitochondria ◽  
Ionization Mass ◽  
Ampk Activation ◽  
Starting Point

Root extracts of a Cameroon medicinal plant, Dorstenia psilurus, were purified by screening for AMP-activated protein kinase (AMPK) activation in incubated mouse embryo fibroblasts (MEFs). Two isoprenylated flavones that activated AMPK were isolated. Compound 1 was identified as artelasticin by high-resolution electrospray ionization mass spectrometry and 2D-NMR while its structural isomer, compound 2, was isolated for the first time and differed only by the position of one double bond on one isoprenyl substituent. Treatment of MEFs with purified compound 1 or compound 2 led to rapid and robust AMPK activation at low micromolar concentrations and increased the intracellular AMP:ATP ratio. In oxygen consumption experiments on isolated rat liver mitochondria, compound 1 and compound 2 inhibited complex II of the electron transport chain and in freeze–thawed mitochondria succinate dehydrogenase was inhibited. In incubated rat skeletal muscles, both compounds activated AMPK and stimulated glucose uptake. Moreover, these effects were lost in muscles pre-incubated with AMPK inhibitor SBI-0206965, suggesting AMPK dependency. Incubation of mouse hepatocytes with compound 1 or compound 2 led to AMPK activation, but glucose production was decreased in hepatocytes from both wild-type and AMPKβ1−/− mice, suggesting that this effect was not AMPK-dependent. However, when administered intraperitoneally to high-fat diet-induced insulin-resistant mice, compound 1 and compound 2 had blood glucose-lowering effects. In addition, compound 1 and compound 2 reduced the viability of several human cancer cells in culture. The flavonoids we have identified could be a starting point for the development of new drugs to treat type 2 diabetes.

mmpdb: An Open Source Matched Molecular Pair Platform for Large Multi-Property Datasets

2018 ◽  
Author(s):  
Andrew Dalke ◽  
Jerome Hert ◽  
Christian Kramer
Keyword(s):  
Open Source ◽  
Large Datasets ◽  
Molecular Pair ◽  
New Algorithms

We present mmpdb, an open source Matched Molecular Pair (MMP) platform to create, compile, store, retrieve, and use MMP rules. mmpdb is suitable for the large datasets typically found in pharmaceutical and agrochemical companies and provides new algorithms for fragment canonicalization and stereochemistry handling. The platform is written in Python and based on the RDKit toolkit. mmpdb is freely available.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

Mobile-Friendly Dashboards to Display Associations Between Cited-by and Similar Journals on Mobile Health Research: A Bibliometric Study (Preprint)

2018 ◽  
Author(s):  
Tsair-Wei Chien ◽  
Hsien-Yi Wang ◽  
Yang Shao ◽  
Willy Chou
Keyword(s):  
Social Network ◽  
Mobile Health ◽  
Health Research ◽  
Similarity Criteria ◽  
Network Density ◽  
Deep Insight ◽  
Density Indices ◽  
The Subject ◽  
The Given ◽  
Insight Into

BACKGROUND Researchers often spend a great deal of time and effort retrieving related journals for their studies and submissions. Authors often designate one article and then retrieve other articles that are related to the given one using PubMed’s service for finding cited-by or similar articles. However, to date, none present the association between cited-by and similar journals related to a given journal. Authors need one effective and efficient way to find related journals on the topic of mobile health research. OBJECTIVE This study aims (1) to show the related journals for a given journal by both cited-by and similarity criteria; (2) to present the association between cited-by and similarity journals related to a given journal; (3) to inspect the patterns of network density indices among clusters classified by social network analysis (SNA); (4) to investigate the feature of Kendall's coefficient(W) of concordance. METHODS We obtained 676 abstracts since 2013 from Medline based on the keywords of ("JMIR mHealth and uHealth"[Journal]) on June 30, 2018, and plotted the clusters of related journals on Google Maps by using MS Excel modules. The features of network density indices were examined. The Kendall coefficient (W) was used to assess the concordance of clusters across indices. RESULTS This study found that (1) the journals related to JMIR mHealth and uHealth are easily presented on dashboards; (2) a mild association(=0.14) exists between cited-by and similar journals related to JMIR mHealth and uHealth; (3) the median Impact Factor were 3.37 and 2.183 based on the representatives of top ten clusters grouped by the cited-by and similar journals, respectively; (4) all Kendall’s coefficients(i.e., 0.82, 0.89, 0.92, and 0.75) for the four sets of density centrality have a statistically significant concordance (p < 0.05). CONCLUSIONS SNA provides deep insight into the relationships of related journals to a given journal. The results of this research can provide readers with a knowledge and concept diagram to use with future submissions to a given journal in the subject category of Mobile Health Research. CLINICALTRIAL Not available

scholarly journals A hybrid recommender system based-on link prediction for movie baskets analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mohammadsadegh Vahidi Farashah ◽  
Akbar Etebarian ◽  
Reza Azmi ◽  
Reza Ebrahimzadeh Dastjerdi
Keyword(s):  
Recommender System ◽  
Link Prediction ◽  
Mean Squared Error ◽  
Spatial Clustering ◽  
Randomized Algorithm ◽  
Similarity Criterion ◽  
Absolute Error ◽  
Similarity Criteria ◽  
Online Systems ◽  
Squared Error

AbstractOver the past decade, recommendation systems have been one of the most sought after by various researchers. Basket analysis of online systems’ customers and recommending attractive products (movies) to them is very important. Providing an attractive and favorite movie to the customer will increase the sales rate and ultimately improve the system. Various methods have been proposed so far to analyze customer baskets and offer entertaining movies but each of the proposed methods has challenges, such as lack of accuracy and high error of recommendations. In this paper, a link prediction-based method is used to meet the challenges of other methods. The proposed method in this paper consists of four phases: (1) Running the CBRS that in this phase, all users are clustered using Density-based spatial clustering of applications with noise algorithm (DBScan), and classification of new users using Deep Neural Network (DNN) algorithm. (2) Collaborative Recommender System (CRS) Based on Hybrid Similarity Criterion through which similarities are calculated based on a threshold (lambda) between the new user and the users in the selected category. Similarity criteria are determined based on age, gender, and occupation. The collaborative recommender system extracts users who are the most similar to the new user. Then, the higher-rated movie services are suggested to the new user based on the adjacency matrix. (3) Running improved Friendlink algorithm on the dataset to calculate the similarity between users who are connected through the link. (4) This phase is related to the combination of collaborative recommender system’s output and improved Friendlink algorithm. The results show that the Mean Squared Error (MSE) of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. In addition, Mean Absolute Error (MAE) of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and Root Mean Squared Error (RMSE) of the proposed method decreased by 6.05 % compared to SVD and approximately 6.02 % compared to ApproSVD.

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