Dataset Information

Novel Development of Predictive Feature Fingerprints to Identify Chemistry-Based Features for the Effective Drug Design of SARS-CoV-2 Target Antagonists and Inhibitors Using Machine Learning.

ABSTRACT: A unique approach to bioactivity and chemical data curation coupled with random forest analyses has led to a series of target-specific and cross-validated predictive feature fingerprints (PFF) that have high predictability across multiple therapeutic targets and disease stages involved in the severe acute respiratory syndrome due to coronavirus 2 (SARS-CoV-2)-induced COVID-19 pandemic, which include plasma kallikrein, human immunodeficiency virus (HIV)-protease, nonstructural protein (NSP)5, NSP12, Janus kinase (JAK) family, and AT-1. The approach was highly accurate in determining the matched target for the different compound sets and suggests that the models could be used for virtual screening of target-specific compound libraries. The curation-modeling process was successfully applied to a SARS-CoV-2 phenotypic screen and could be used for predictive bioactivity estimation and prioritization for clinical trial selection; virtual screening of drug libraries for the repurposing of drug molecules; and analysis and direction of proprietary data sets.

SUBMITTER: Cooper K

PROVIDER: S-EPMC7905939 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Novel Development of Predictive Feature Fingerprints to Identify Chemistry-Based Features for the Effective Drug Design of SARS-CoV-2 Target Antagonists and Inhibitors Using Machine Learning.

Cooper Kelvin K Baddeley Christopher C French Bernie B Gibson Katherine K Golden James J Lee Thiam T Pierre Sadrach S Weiss Brent B Yang Jason J

ACS omega 20210205 7

A unique approach to bioactivity and chemical data curation coupled with random forest analyses has led to a series of target-specific and cross-validated predictive feature fingerprints (PFF) that have high predictability across multiple therapeutic targets and disease stages involved in the severe acute respiratory syndrome due to coronavirus 2 (SARS-CoV-2)-induced COVID-19 pandemic, which include plasma kallikrein, human immunodeficiency virus (HIV)-protease, nonstructural protein (NSP)5, NSP ...[more]

PMID: 33644594

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Project description:Background: Electronic health records (EHR) play an important role for the redefinition of phenotypes in view of the wealth and heterogeneity of information now available from disparate data sources. A recent cross-sectional retrospective study has described the potential of EHR toward type 2 diabetes mellitus (T2D) screening when ad hoc models are used. About 10,000 US patients have been analyzed through a variety of inference techniques applied to all records with a variable degree of completeness. The analyses conducted in the reference study have indicated that EHR phenotypes significantly improved T2D detection. Methods: With these US patients and the T2D data evidenced in the above study, we propose an integrative inference approach that leverages the prediction power of EHR features selected by two well-known methods, Random Forests and Lasso. The goal is 2-fold: reducing the Big Data redundancies potentially harmful to the predictive learning task and exploiting the interconnectivity of EHR features. A mutual information (MI) network is the inference tool used to identify communities useful to prioritize significant T2D features underlying the similarity between patients. Results: Endowed with a different degree of granularity, the communities detected after the application of both methods were centered especially on T2D comorbidities and risk factors. As such, they appear very relevant for assessment of two main issues, T2D disease burden, and prevention. Conclusions: Our analytical approach offers a solution for managing the EHR scale factor in a complex disease context. EHR are rich sources of phenotypic diversity through which novel stratifications of patients are expected. To enable these results, both pre-screening of variables and calibration of risk prediction methods become necessary steps in EHR analyses. We have presented networks identifying major T2D communities. The specific significance assigned to comorbidities and risk factors in relation to T2D can be inferred with accuracy from just a suitably reduced number of EHR features.

Project description:ObjectiveTo identify predictors of prediabetes using feature selection and machine learning on a nationally representative sample of the US population.Materials and methodsWe analyzed n = 6346 men and women enrolled in the National Health and Nutrition Examination Survey 2013-2014. Prediabetes was defined using American Diabetes Association guidelines. The sample was randomly partitioned to training (n = 3174) and internal validation (n = 3172) sets. Feature selection algorithms were run on training data containing 156 preselected exposure variables. Four machine learning algorithms were applied on 46 exposure variables in original and resampled training datasets built using 4 resampling methods. Predictive models were tested on internal validation data (n = 3172) and external validation data (n = 3000) prepared from National Health and Nutrition Examination Survey 2011-2012. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Predictors were assessed by odds ratios in logistic models and variable importance in others. The Centers for Disease Control (CDC) prediabetes screening tool was the benchmark to compare model performance.ResultsPrediabetes prevalence was 23.43%. The CDC prediabetes screening tool produced 64.40% AUROC. Seven optimal (≥ 70% AUROC) models identified 25 predictors including 4 potentially novel associations; 20 by both logistic and other nonlinear/ensemble models and 5 solely by the latter. All optimal models outperformed the CDC prediabetes screening tool (P < 0.05).DiscussionCombined use of feature selection and machine learning increased predictive performance outperforming the recommended screening tool. A range of predictors of prediabetes was identified.ConclusionThis work demonstrated the value of combining feature selection with machine learning to identify a wide range of predictors that could enhance prediabetes prediction and clinical decision-making.

Dataset Information

Novel Development of Predictive Feature Fingerprints to Identify Chemistry-Based Features for the Effective Drug Design of SARS-CoV-2 Target Antagonists and Inhibitors Using Machine Learning.

Publications

Novel Development of Predictive Feature Fingerprints to Identify Chemistry-Based Features for the Effective Drug Design of SARS-CoV-2 Target Antagonists and Inhibitors Using Machine Learning.

Similar Datasets

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