Database Organization and Similarity Searching with E-State Indices

2001 ◽  
pp. 33-49 ◽  
Author(s):  
Lemond B. Kier ◽  
Llowell H. Hall
Keyword(s):  
Similarity Searching ◽  
Database Organization

ChemInform Abstract: Database Organization and Similarity Searching with E-state Indices

ChemInform ◽  
2010 ◽  
Vol 33 (40) ◽  
pp. no-no
Author(s):  
Lemond B. Kier ◽  
Llowell H. Hall
Keyword(s):  
Similarity Searching ◽  
Database Organization

scholarly journals ReactionCode: a new versatile format for searching, analysis, classification, transform, and encoding/decoding of reactions

2020 ◽  
Author(s):  
Victorien Delannée ◽  
Marc Nicklaus
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Exchange ◽  
Similarity Searching ◽  
Classification Analysis ◽  
The Past ◽  
Machine Learning Applications ◽  
Database Organization ◽  
Machine Readable ◽  
Condensed Graph Of Reaction

In the past two decades a lot of different formats for molecules and reactions have been created. These formats were mostly developed for the purposes of identifiers, representation, classification, analysis and data exchange. A lot of efforts have been made on molecule formats but only few for reactions where the endeavors have been made mostly by companies leading to proprietary formats. Here, we developed a new open-source format which allows to encode and decode a reaction into multi-layers machine readable code, which aggregates reactants and products into a condensed graph of reaction (CGR). This format is flexible and can be used in a context of reaction similarity searching and classification. It is also designed for database organization, machine learning applications and as a new transform reaction language.

scholarly journals ReactionCode: Format for Reaction Searching, Analysis, Classification, Transform, and Encoding/Decoding

2020 ◽  
Author(s):  
Victorien Delannée ◽  
Marc Nicklaus
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Exchange ◽  
Similarity Searching ◽  
Classification Analysis ◽  
The Past ◽  
Machine Learning Applications ◽  
Database Organization ◽  
Machine Readable ◽  
Condensed Graph Of Reaction

In the past two decades a lot of different formats for molecules and reactions have been created. These formats were mostly developed for the purposes of identifiers, representation, classification, analysis and data exchange. A lot of efforts have been made on molecule formats but only few for reactions where the endeavors have been made mostly by companies leading to proprietary formats. Here, we developed a new open-source format which allows to encode and decode a reaction into multi-layers machine readable code, which aggregates reactants and products into a condensed graph of reaction (CGR). This format is flexible and can be used in a context of reaction similarity searching and classification. It is also designed for database organization, machine learning applications and as a new transform reaction language.

scholarly journals ReactionCode: format for reaction searching, analysis, classification, transform, and encoding/decoding

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Victorien Delannée ◽  
Marc C. Nicklaus
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Exchange ◽  
Similarity Searching ◽  
Classification Analysis ◽  
The Past ◽  
Machine Learning Applications ◽  
Database Organization ◽  
Machine Readable ◽  
Condensed Graph Of Reaction

AbstractIn the past two decades a lot of different formats for molecules and reactions have been created. These formats were mostly developed for the purposes of identifiers, representation, classification, analysis and data exchange. A lot of efforts have been made on molecule formats but only few for reactions where the endeavors have been made mostly by companies leading to proprietary formats. Here, we present ReactionCode: a new open-source format that allows one to encode and decode a reaction into multi-layer machine readable code, which aggregates reactants and products into a condensed graph of reaction (CGR). This format is flexible and can be used in a context of reaction similarity searching and classification. It is also designed for database organization, machine learning applications and as a new transform reaction language.

scholarly journals ReactionCode: Format for Reaction Searching, Analysis, Classification, Transform, and Encoding/Decoding

2020 ◽  
Author(s):  
Victorien Delannée ◽  
Marc C. Nicklaus
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Exchange ◽  
Similarity Searching ◽  
Classification Analysis ◽  
The Past ◽  
Machine Learning Applications ◽  
Database Organization ◽  
Machine Readable ◽  
Condensed Graph Of Reaction

Abstract In the past two decades a lot of different formats for molecules and reactions have been created. These formats were mostly developed for the purposes of identifiers, representation, classification, analysis and data exchange. A lot of efforts ha ve been made on molecule formats but only few for reactions where the endeavors have been made mostly by companies leading to proprietary formats. Here, we developed a new open-source format which allows to encode and decode a reaction into multi-layers machine readable code, which aggregates reactants and products into a condensed graph of reaction (CGR). This format is flexible and can be used in a context of reaction similarity searching and classification. It is also designed for database organization, machine learning applications and as a new transform reaction language.

scholarly journals ReactionCode: a new versatile format for searching, analysis, classification, transform, and encoding/decoding of reactions

2020 ◽  
Author(s):  
Victorien Delannée ◽  
Marc Nicklaus
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Exchange ◽  
Similarity Searching ◽  
Classification Analysis ◽  
The Past ◽  
Machine Learning Applications ◽  
Database Organization ◽  
Machine Readable ◽  
Condensed Graph Of Reaction

In the past two decades a lot of different formats for molecules and reactions have been created. These formats were mostly developed for the purposes of identifiers, representation, classification, analysis and data exchange. A lot of efforts have been made on molecule formats but only few for reactions where the endeavors have been made mostly by companies leading to proprietary formats. Here, we developed a new open-source format which allows to encode and decode a reaction into multi-layers machine readable code, which aggregates reactants and products into a condensed graph of reaction (CGR). This format is flexible and can be used in a context of reaction similarity searching and classification. It is also designed for database organization, machine learning applications and as a new transform reaction language.

scholarly journals ReactionCode: Format for Reaction Searching, Analysis, Classification, Transform, and Encoding/Decoding

2020 ◽  
Author(s):  
Victorien Delannée ◽  
Marc Nicklaus
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Exchange ◽  
Similarity Searching ◽  
Classification Analysis ◽  
The Past ◽  
Machine Learning Applications ◽  
Database Organization ◽  
Machine Readable ◽  
Condensed Graph Of Reaction

In the past two decades a lot of different formats for molecules and reactions have been created. These formats were mostly developed for the purposes of identifiers, representation, classification, analysis and data exchange. A lot of efforts have been made on molecule formats but only few for reactions where the endeavors have been made mostly by companies leading to proprietary formats. Here, we developed a new open-source format which allows to encode and decode a reaction into multi-layers machine readable code, which aggregates reactants and products into a condensed graph of reaction (CGR). This format is flexible and can be used in a context of reaction similarity searching and classification. It is also designed for database organization, machine learning applications and as a new transform reaction language.

Medicinal Chemistry Database GDBMedChem

2019 ◽  
Author(s):  
Mahendra Awale ◽  
Finton Sirockin ◽  
Nikolaus Stiefl ◽  
Jean-Louis Reymond
Keyword(s):  
Small Molecules ◽  
Natural Product ◽  
Medicinal Chemistry ◽  
3D Visualization ◽  
Molecular Size ◽  
Similarity Searching ◽  
Complex Molecules ◽  
Synthetic Accessibility ◽  
Simple Chemical ◽  
Reduced Complexity

<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>

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

2019 ◽  
Vol 14 (7) ◽  
pp. 628-639 ◽  
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.

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