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Data of small peptides in SMILES and three-dimensional formats for virtual screening campaigns.

ABSTRACT: The data presented in this article are structures of dipeptides, tripeptides and tetrapeptides constructed from all possible combinations of 20 natural and common amino acids. In total, the data contains 168400 peptides. The structures are available in their simplified molecular-input line-entry system (SMILES) and three-dimensional (3D) formats. The type of data are text files, which could be accessed and modified either by text editor applications (e.g. Notepad++) or by molecule visualization softwares (e.g., YASARA View). These structures could be used further in virtual screening campaigns in the early stage of drug discovery projects.

SUBMITTER: Prasasty VD

PROVIDER: S-EPMC6806445 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Data of small peptides in SMILES and three-dimensional formats for virtual screening campaigns.

Prasasty Vivitri Dewi VD Istyastono Enade Perdana EP

Data in brief 20191004

The data presented in this article are structures of dipeptides, tripeptides and tetrapeptides constructed from all possible combinations of 20 natural and common amino acids. In total, the data contains 168400 peptides. The structures are available in their simplified molecular-input line-entry system (SMILES) and three-dimensional (3D) formats. The type of data are text files, which could be accessed and modified either by text editor applications (<i>e.g.</i> Notepad++) or by molecule visuali ...[more]

PMID: 31656840

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Project description:BackgroundThere are three main problems associated with the virtual screening of bioassay data. The first is access to freely-available curated data, the second is the number of false positives that occur in the physical primary screening process, and finally the data is highly-imbalanced with a low ratio of Active compounds to Inactive compounds. This paper first discusses these three problems and then a selection of Weka cost-sensitive classifiers (Naive Bayes, SVM, C4.5 and Random Forest) are applied to a variety of bioassay datasets.ResultsPharmaceutical bioassay data is not readily available to the academic community. The data held at PubChem is not curated and there is a lack of detailed cross-referencing between Primary and Confirmatory screening assays. With regard to the number of false positives that occur in the primary screening process, the analysis carried out has been shallow due to the lack of cross-referencing mentioned above. In six cases found, the average percentage of false positives from the High-Throughput Primary screen is quite high at 64%. For the cost-sensitive classification, Weka's implementations of the Support Vector Machine and C4.5 decision tree learner have performed relatively well. It was also found, that the setting of the Weka cost matrix is dependent on the base classifier used and not solely on the ratio of class imbalance.ConclusionsUnderstandably, pharmaceutical data is hard to obtain. However, it would be beneficial to both the pharmaceutical industry and to academics for curated primary screening and corresponding confirmatory data to be provided. Two benefits could be gained by employing virtual screening techniques to bioassay data. First, by reducing the search space of compounds to be screened and secondly, by analysing the false positives that occur in the primary screening process, the technology may be improved. The number of false positives arising from primary screening leads to the issue of whether this type of data should be used for virtual screening. Care when using Weka's cost-sensitive classifiers is needed - across the board misclassification costs based on class ratios should not be used when comparing differing classifiers for the same dataset.

Dataset Information

Data of small peptides in SMILES and three-dimensional formats for virtual screening campaigns.

Publications

Data of small peptides in SMILES and three-dimensional formats for virtual screening campaigns.

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