Turbo Similarity Searching: Effect of Partial Ranking and Fusion Rules on ChEMBL Database

<div>The generated database GDB17 enumerates 166.4 billion possible molecules up to 17 atoms of C, N, O, S and halogens following simple chemical stability and synthetic feasibility rules, however medicinal chemistry criteria are not taken into account. Here we applied rules inspired by medicinal chemistry to exclude problematic functional groups and complex molecules from GDB17, and sampled the resulting subset evenly across molecular size, stereochemistry and polarity to form GDBMedChem as a compact collection of 10 million small molecules.</div><div><br></div><div>This collection has reduced complexity and better synthetic accessibility than the entire GDB17 but retains higher sp 3 - carbon fraction and natural product likeness scores compared to known drugs. GDBMedChem molecules are more diverse and very different from known molecules in terms of substructures and represent an unprecedented source of diversity for drug design. GDBMedChem is available for 3D-visualization, similarity searching and for download at http://gdb.unibe.ch.</div>

Download Full-text

Faculty Opinions recommendation of Fate-mapping of the epithelial seam during palatal fusion rules out epithelial-mesenchymal transformation.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1028749.341347 ◽

2005 ◽

Author(s):

Patrick Tam

Keyword(s):

Fate Mapping ◽

Fusion Rules ◽

Palatal Fusion ◽

Mesenchymal Transformation

Download Full-text

A Similarity Searching System for Biological Phenotype Images Using Deep Convolutional Encoder-decoder Architecture

Current Bioinformatics ◽

10.2174/1574893614666190204150109 ◽

2019 ◽

Vol 14 (7) ◽

pp. 628-639 ◽

Cited By ~ 10

Author(s):

Bizhi Wu ◽

Hangxiao Zhang ◽

Limei Lin ◽

Huiyuan Wang ◽

Yubang Gao ◽

...

Keyword(s):

Neural Network ◽

Retrieval System ◽

Sequence Similarity ◽

Local Alignment ◽

Similarity Searching ◽

Loss Of Function ◽

Biological Images ◽

The Neural Network ◽

Convolutional Autoencoder ◽

Biological Phenotype

Background: The BLAST (Basic Local Alignment Search Tool) algorithm has been widely used for sequence similarity searching. Analogously, the public phenotype images must be efficiently retrieved using biological images as queries and identify the phenotype with high similarity. Due to the accumulation of genotype-phenotype-mapping data, a system of searching for similar phenotypes is not available due to the bottleneck of image processing. Objective: In this study, we focus on the identification of similar query phenotypic images by searching the biological phenotype database, including information about loss-of-function and gain-of-function. Methods: We propose a deep convolutional autoencoder architecture to segment the biological phenotypic images and develop a phenotype retrieval system to enable a better understanding of genotype–phenotype correlation. Results: This study shows how deep convolutional autoencoder architecture can be trained on images from biological phenotypes to achieve state-of-the-art performance in a phenotypic images retrieval system. Conclusion: Taken together, the phenotype analysis system can provide further information on the correlation between genotype and phenotype. Additionally, it is obvious that the neural network model of image segmentation and the phenotype retrieval system is equally suitable for any species, which has enough phenotype images to train the neural network.

Download Full-text

LINGO-DL: a text-based approach for molecular similarity searching

Journal of Computer-Aided Molecular Design ◽

10.1007/s10822-021-00383-9 ◽

2021 ◽

Author(s):

Ammar Abdo ◽

Maude Pupin

Keyword(s):

Molecular Similarity ◽

Similarity Searching

Download Full-text

Fusion Rules Effects on Lifetime Maximization of Multi-channel Cooperative Spectrum Sensing

Wireless Personal Communications ◽

10.1007/s11277-021-08326-1 ◽

2021 ◽

Author(s):

Asma Bagheri ◽

Ataollah Ebrahimzadeh ◽

Maryam Najimi

Keyword(s):

Spectrum Sensing ◽

Cooperative Spectrum Sensing ◽

Lifetime Maximization ◽

Fusion Rules

Download Full-text

Evaluation of Online Teaching Quality: An Extended Linguistic MAGDM Framework Based on Risk Preferences and Unknown Weight Information

Symmetry ◽

10.3390/sym13020192 ◽

2021 ◽

Vol 13 (2) ◽

pp. 192

Author(s):

Hui Lin ◽

Jianxin You ◽

Tao Xu

Keyword(s):

Online Teaching ◽

Virus Disease ◽

Risk Preferences ◽

Teaching Quality ◽

Indicator System ◽

Partial Ranking ◽

Evaluation Indicator ◽

Linguistic Term ◽

Evaluation Indicator System ◽

Two Stages

Evaluation of online teaching quality has become an important issue because many universities are turning to online classes due to the Corona Virus Disease 2019 (COVID-19) pandemic. In this paper, online teaching quality evaluation is considered as a linguistic multi-attribute group decision-making (MAGDM) problem. Generally, the evaluation sematic information can be symmetrically or asymmetrically distributed in linguistic term sets. Thus, an extended linguistic MAGDM framework is proposed for evaluating online teaching quality. As the main contribution, the proposed method takes into account the risk preferences of assessment experts (AEs) and unknown weight information of attributes and sub-attributes. To be specific, the Delphi method is employed to establish a multi-level evaluation indicator system (EIS) of online teaching quality. Then, by introducing the group generalized linguistic term set (GLTS) with two risk preference parameters, a two-stage optimization model is developed to calculate the weights of attributes and sub-attributes objectively. Subsequently, the linguistic MAGDM framework was divided into two stages. The first stage maximizes the group comprehensive rating values of teachers on different attributes to obtain partial ranking results for teachers on each attribute. The latter stage maximizes the group comprehensive rating values of teachers to evaluate the overall quality. Finally, a case study is provided to illustrate how to apply the framework to evaluate online teaching quality.

Download Full-text

FUSION RULES FOR THE FRACTIONAL LEVEL $\widehat{{\rm sl}(2)}$ ALGEBRA

Modern Physics Letters A ◽

10.1142/s0217732392003645 ◽

1992 ◽

Vol 07 (13) ◽

pp. 1185-1195 ◽

Cited By ~ 48

Author(s):

HIDETOSHI AWATA ◽

YASUHIKO YAMADA

Keyword(s):

Null Vector ◽

Primary Field ◽

Fusion Rules ◽

Weyl Transformation ◽

Interesting Structure

We derive, as the condition for the null vector decoupling, the fusion rules for the [Formula: see text] algebra with fractional level, which have an interesting structure related to affine Weyl transformation. It is shown that, to get non-trivial fusion rules, we must include the primary field which belongs to neither the highest nor the lowest weight representations.

Download Full-text