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Pushing the limits of solubility prediction via quality-oriented data selection

ABSTRACT: Summary Accurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved. Graphical Abstract Highlights • Consensus machine learning models perform better than singular models• Quality-oriented data selection yields better results than using all data• The uncertainty of test data determines the theoretical limit of a model's performance• The concepts of actual and observed performances of solubility models are introduced Chemistry; Analytical Reagents; Computational Chemistry; Artificial Intelligence

SUBMITTER: Sorkun M

PROVIDER: S-EPMC7788089 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Project description:Selection of the translational initiation site in most eukaryotic mRNAs appears to occur via a scanning mechanism which predicts that proximity to the 5' end plays a dominant role in identifying the start codon. This "position effect" is seen in cases where a mutation creates an AUG codon upstream from the normal start site and translation shifts to the upstream site. The position effect is evident also in cases where a silent internal AUG codon is activated upon being relocated closer to the 5' end. Two mechanisms for escaping the first-AUG rule--reinitiation and context-dependent leaky scanning--enable downstream AUG codons to be accessed in some mRNAs. Although these mechanisms are not new, many new examples of their use have emerged. Via these escape pathways, the scanning mechanism operates even in extreme cases, such as a plant virus mRNA in which translation initiates from three start sites over a distance of 900 nt. This depends on careful structural arrangements, however, which are rarely present in cellular mRNAs. Understanding the rules for initiation of translation enables understanding of human diseases in which the expression of a critical gene is reduced by mutations that add upstream AUG codons or change the context around the AUG(START) codon. The opposite problem occurs in the case of hereditary thrombocythemia: translational efficiency is increased by mutations that remove or restructure a small upstream open reading frame in thrombopoietin mRNA, and the resulting overproduction of the cytokine causes the disease. This and other examples support the idea that 5' leader sequences are sometimes structured deliberately in a way that constrains scanning in order to prevent harmful overproduction of potent regulatory proteins. The accumulated evidence reveals how the scanning mechanism dictates the pattern of transcription--forcing production of monocistronic mRNAs--and the pattern of translation of eukaryotic cellular and viral genes.

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Pushing the limits of solubility prediction via quality-oriented data selection

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