Reliable Target Prediction of Bioactive Molecules Based on Chemical Similarity Without Employing Statistical Methods

Natural products are an invaluable source for drug candidates. Currently, plasma metabolome has suggested that compounds present in herbs may exert bioactivity.

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Primary Target Prediction of Bioactive Molecules from Chemical Structure

10.1101/413237 ◽

2018 ◽

Author(s):

Abed Forouzesh ◽

Sadegh Samadi Foroushani ◽

Fatemeh Forouzesh ◽

Eskandar Zand

Keyword(s):

Molecular Structure ◽

Chemical Structure ◽

Target Prediction ◽

Bioactive Molecules ◽

Unknown Primary ◽

Complex Nature ◽

Primary Target ◽

Innovative Method ◽

Digital Material ◽

Machine Readable

AbstractThere are various tools for computational target prediction of bioactive molecules from a chemical structure in a machine-readable material but these tools can’t distinguish a primary target from other targets. Also, due to the complex nature of bioactive molecules, there has not been a method to predict a target and or a primary target from a chemical structure in a non-digital material (for example printed or hand-written documents) yet. In this study, an attempt to simplify primary target prediction from a chemical structure was resulted in developing an innovative method based on the minimum structure which can be used in both formats of non-digital and machine-readable materials. A minimum structure does not represent a real molecule or a real association of functional groups, but is a part of a molecular structure which is necessary to ensure the primary target prediction of bioactive molecules. Structurally related bioactive molecules with the minimum structure were considered as neighbor molecules of the query molecule. The known primary target of the neighbor molecule is used as a reference for predicting the primary target of the neighbor molecule with an unknown primary target. In results, we confirmed the usefulness of our proposed method for primary target prediction in 548 drugs and pesticides involved in four primary targets by eight minimum structures.

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D3Similarity: A Ligand-Based Approach for Predicting Drug Targets and for Virtual Screening of Active Compounds Against COVID-19

10.26434/chemrxiv.11959323 ◽

2020 ◽

Cited By ~ 1

Author(s):

Zhengdan Zhu ◽

Xiaoyu Wang ◽

Yanqing Yang ◽

Xinben Zhang ◽

Kaijie Mu ◽

...

Keyword(s):

Protein Structure ◽

Virtual Screening ◽

Drug Targets ◽

False Negative ◽

Three Dimensional ◽

Target Prediction ◽

Molecular Structures ◽

Bioactive Molecules ◽

Active Compounds ◽

Target Proteins

<p>Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing COVID-19. Protein structure based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by, e.g., various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure based platform for COVID-19, termed as D3Docking in our recent work, we developed the ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses SARS, MERS and SARS-CoV-2, some of which have target or mechanism information but some don’t. Based on the two-dimensional and three-dimensional similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity for drug discovery and target prediction against COVID-19. D3Similarity is available free of charge at <a href="https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php">https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php</a>.</p>

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Improving chemical similarity ensemble approach in target prediction

Journal of Cheminformatics ◽

10.1186/s13321-016-0130-x ◽

2016 ◽

Vol 8 (1) ◽

Cited By ~ 30

Author(s):

Zhonghua Wang ◽

Lu Liang ◽

Zheng Yin ◽

Jianping Lin

Keyword(s):

Target Prediction ◽

Chemical Similarity ◽

Ensemble Approach ◽

Similarity Ensemble Approach

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D3Similarity: A Ligand-Based Approach for Predicting Drug Targets and for Virtual Screening of Active Compounds Against COVID-19

10.26434/chemrxiv.11959323.v1 ◽

2020 ◽

Cited By ~ 2

Author(s):

Zhengdan Zhu ◽

Xiaoyu Wang ◽

Yanqing Yang ◽

Xinben Zhang ◽

Kaijie Mu ◽

...

Keyword(s):

Protein Structure ◽

Virtual Screening ◽

Drug Targets ◽

False Negative ◽

Three Dimensional ◽

Target Prediction ◽

Molecular Structures ◽

Bioactive Molecules ◽

Active Compounds ◽

Target Proteins

<p>Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing COVID-19. Protein structure based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by, e.g., various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure based platform for COVID-19, termed as D3Docking in our recent work, we developed the ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses SARS, MERS and SARS-CoV-2, some of which have target or mechanism information but some don’t. Based on the two-dimensional and three-dimensional similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity for drug discovery and target prediction against COVID-19. D3Similarity is available free of charge at <a href="https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php">https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php</a>.</p>

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Statistics and parallaxes

International Astronomical Union Colloquium ◽

10.1017/s0252921100073875 ◽

1978 ◽

Vol 48 ◽

pp. 7-29

Author(s):

T. E. Lutz

Keyword(s):

Experimental Design ◽

Statistical Methods ◽

Review Paper ◽

Systematic Errors ◽

Past Experience ◽

Random Errors ◽

Trigonometric Parallaxes ◽

Systematic And Random Errors

This review paper deals with the use of statistical methods to evaluate systematic and random errors associated with trigonometric parallaxes. First, systematic errors which arise when using trigonometric parallaxes to calibrate luminosity systems are discussed. Next, determination of the external errors of parallax measurement are reviewed. Observatory corrections are discussed. Schilt’s point, that as the causes of these systematic differences between observatories are not known the computed corrections can not be applied appropriately, is emphasized. However, modern parallax work is sufficiently accurate that it is necessary to determine observatory corrections if full use is to be made of the potential precision of the data. To this end, it is suggested that a prior experimental design is required. Past experience has shown that accidental overlap of observing programs will not suffice to determine observatory corrections which are meaningful.

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