File:Small ribosomal subunit identification in (4aRS,5aRS)-Sancycline group.pdf

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The identification of a bioactive molecule with the target of small ribosomal subunit inhibition by the minimum structure in (4aRS,5aRS)-Sancycline group.

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Description	English: Molecules 2, 3, 4, 5 and 6 don’t contain (4aRS,5aRS)-Sancycline (the core). Therefore, the target of molecules 2, 3, 4, 5 and 6 is not small ribosomal subunit inhibition. Reference: Forouzesh, Abed; Samadi Foroushani, Sadegh; Forouzesh, Fatemeh; Zand, Eskandar (2019). "Reliable target prediction of bioactive molecules based on chemical similarity without employing statistical methods". Frontiers in Pharmacology. 10: 835. doi:10.3389/fphar.2019.00835
Date	26 July 2019
Source	Own work
Author	SSamadi15

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current	13:10, 31 August 2019		1,275 × 1,650 (209 KB)	SSamadi15 (talk \| contribs)	Corrected version
	18:29, 27 August 2019		1,275 × 1,650 (1.79 MB)	SSamadi15 (talk \| contribs)	User created page with UploadWizard

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Short title	Reliable target prediction of bioactive molecules based on chemical similarity without employing statistical methods
Image title	The prediction of biological targets of bioactive molecules from machine-readable materials can be routinely performed by computational target prediction tools (CTPTs). However, the prediction of biological targets of bioactive molecules from non-digital materials (e.g., printed or handwritten documents) has not been possible due to the complex nature of bioactive molecules and impossibility of employing computations. Improving the target prediction accuracy is the most important challenge for computational target prediction. A minimum structure is identified for each group of neighbor molecules in the proposed method. Each group of neighbor molecules represents a distinct structural class of molecules with the same function in relation to the target. The minimum structure is employed as a query to search for molecules that perfectly satisfy the minimum structure of what is guessed crucial for the targeted activity. The proposed method is based on chemical similarity, but only molecules that perfectly satisfy the minimum structure are considered. Structurally related bioactive molecules found with the same minimum structure were considered as neighbor molecules of the query molecule. The known target of the neighbor molecule is used as a reference for predicting the target of the neighbor molecule with an unknown target. A lot of information is needed to identify the minimum structure, because it is necessary to know which part(s) of the bioactive molecule determines the precise target or targets responsible for the observed phenotype. Therefore, the predicted target based on the minimum structure without employing the statistical significance is considered as a reliable prediction. Since only molecules that perfectly (and not partly) satisfy the minimum structure are considered, the minimum structure can be used without similarity calculations in non-digital materials and with similarity calculations (perfect similarity) in machine-readable materials. Nine tools (PASS online, PPB, SEA, TargetHunter, PharmMapper, ChemProt, HitPick, SuperPred, and SPiDER), which can be used for computational target prediction, are compared with the proposed method for 550 target predictions. The proposed method, SEA, PPB, and PASS online, showed the best quality and quantity for the accurate predictions. Figure: The identification of a bioactive molecule with the target of small ribosomal subunit inhibition by the minimum structure in (4aRS,5aRS)-Sancycline group. Molecules 2, 3, 4, 5 and 6 don’t contain (4aRS,5aRS)-Sancycline (the core). Therefore, the target of molecules 2, 3, 4, 5 and 6 is not small ribosomal subunit inhibition.
Author	Abed Forouzesh, Sadegh Samadi Foroushani, Fatemeh Forouzesh, Eskandar Zand
Software used	Abed Forouzesh, Sadegh Samadi Foroushani
Conversion program	Sadegh Samadi Foroushani
Encrypted	yes (print:yes copy:yes change:no addNotes:no algorithm:AES-256)
Page size	612 x 792 pts (letter)
Version of PDF format	1.7

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