Evaluation of the performance of various machine learning methods on the discrimination of the active compounds

Digital identification of Aucklandiae radix, Vladimiriae radix, and Inulae radix based on multivariate algorithms and UHPLC-QTOF-MS analysis

INTRODUCTION

The identification of Aucklandiae Radix (AR), Vladimiriae Radix (VR), and Inulae Radix (IR) based on traits and microscopic features is susceptible to the state of samples and the subjective awareness of personnel, and the identification based on a few or single chemical compositions is a cumbersome and time-consuming procedure and fails to rationally and effectively utilize the information of unknown components and is not specificity enough.

OBJECTIVES

This study aimed to improve the identification efficiency, strengthen supervision, and realize digital identification of three Chinese medicines. Ultra-high-performance liquid chromatography with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) combined with multivariate algorithms was used to explore the digital identification of AR, VR, and IR.

MATERIALS AND METHODS

UHPLC-QTOF-MS was used to analyze AR, VR, and IR. The MS data combined with multivariate algorithms such as partial least squares discrimination analysis (PLS-DA) and artificial neural networks (ANNs) was used to filter important variables and data modeling. Finally, the optimal model was selected for the digital identification of three herbs.

RESULTS

The results showed that three herbs can be distinguished on the whole level, and through feature screening, 591 characteristic variables combined with multivariate algorithms to construct data models. The ANN model was the best with accuracy = 0.983, precision = 0.984, and external verification showed the reliability and practicability of ANN model.

CONCLUSION

ANN model combined with MS data is of great significance for tdigital identification of AR, VR, and IR. It is an important reference for developing the digital identification of traditional Chinese medicines at the individual level based on UHPLC-QTOF-MS and multivariate algorithms.

Tracing the pathways and mechanisms involved in the anti-breast cancer activity of glycyrrhizin using bioinformatics tools and computational methods

A complete investigation to understand the pathways that could be affected by glycyrrhizin (licorice), as anti-breast cancer (BC) agent, has not been performed to date. This study aims to investigate the pathways involved in the anti-cancer activity of glycyrrhizin against BC. For this purpose, the target genes of glycyrrhizin were obtained from the ChEMBL database. The BC-associated genes for three types of BC (breast carcinoma, malignant neoplasm of breast, and triple-negative breast neoplasms) were retrieved from DisGeNET. The target genes of glycyrrhizin and the BC-associated genes were compared, and the genes with disease specificity index (DSI) > 0.6 were selected for further evaluation using in silico methods. The protein-protein interaction (PPI) network was constructed, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed. The potential complexes were further evaluated using molecular dynamics (MD) simulation. The results revealed that among 80 common genes, ten genes had DSI greater than 0.6, which included POLK, TACR2, MC3R, TBXAS1, HH1R, SLCO4A1, NPY2R, ADRA2C, ADRA1A, and SLCO2B1. The binding affinity of glycyrrhizin to the cognate proteins and binding characteristics were assessed using molecular docking and binding free energy calculations (MM/GBSA). POLK, TBXAS1, and ADRA1A showed the highest binding affinity with -8.9, -9.3, and -9.6 kcal/mol, respectively. The final targets had an association with BC at several stages of tumor growth. By affecting these targets, glycyrrhizin could influence and control BC efficiently. MD simulation suggested the pathways triggered by the complex glycyrrhizin-ADRA1A were more likely to happen.Communicated by Ramaswamy H. Sarma.

Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation

Background

Continuously growing of HIV incidence among men who have sex with men (MSM), as well as the low rate of HIV testing of MSM in China, demonstrates a need for innovative strategies to improve the implementation of HIV prevention. The use of machine learning algorithms is an increasing tendency in disease diagnosis prediction. We aimed to develop and validate machine learning models in predicting HIV infection among MSM that can identify individuals at increased risk of HIV acquisition for transmission-reduction interventions.

Methods

We extracted data from MSM sentinel surveillance in Zhejiang province from 2018 to 2020. Univariate logistic regression was used to select significant variables in 2018-2019 data (P < 0.05). After data processing and feature selection, we divided the model development data into two groups by stratified random sampling: training data (70%) and testing data (30%). The Synthetic Minority Oversampling Technique (SMOTE) was applied to solve the problem of unbalanced data. The evaluation metrics of model performance were comprised of accuracy, precision, recall, F-measure, and the area under the receiver operating characteristic curve (AUC). Then, we explored three commonly-used machine learning algorithms to compare with logistic regression (LR), including decision tree (DT), support vector machines (SVM), and random forest (RF). Finally, the four models were validated prospectively with 2020 data from Zhejiang province.

Results

A total of 6,346 MSM were included in model development data, 372 of whom were diagnosed with HIV. In feature selection, 12 variables were selected as model predicting indicators. Compared with LR, the algorithms of DT, SVM, and RF improved the classification prediction performance in SMOTE-processed data, with the AUC of 0.778, 0.856, 0.887, and 0.942, respectively. RF was the best-performing algorithm (accuracy = 0.871, precision = 0.960, recall = 0.775, F-measure = 0.858, and AUC = 0.942). And the RF model still performed well on prospective validation (AUC = 0.846).

Conclusion

Machine learning models are substantially better than conventional LR model and RF should be considered in prediction tools of HIV infection in Chinese MSM. Further studies are needed to optimize and promote these algorithms and evaluate their impact on HIV prevention of MSM.

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

The ability to estimate the probability of a drug to receive approval in clinical trials provides natural advantages to optimizing pharmaceutical research workflows. Success rates of clinical trials have deep implications for costs, duration of development, and under pressure due to stringent regulatory approval processes. We propose a machine learning approach that can predict the outcome of the trial with reliable accuracies, using biological activities, physicochemical properties of the compounds, target-related features, and NLP-based compound representation. In the above list, biological activities have never been used as an independent variable towards the prediction of clinical trial outcomes. We have extracted the drug-disease pair from clinical trials and mapped target(s) to that pair using multiple data sources. Empirical results demonstrate that ensemble learning outperforms independently trained, small-data ML models. We report results and inferences derived from a Random forest classifier with an average accuracy of 93%, and an F1 score of 0.96 for the "Pass" class. "Pass" refers to one of the two classes (Pass/Fail) of all clinical trials, and the model performed well in predicting the "Pass" category. Through the analysis of feature contributions to predictive capability, we have demonstrated that bioactivity plays a statistically significant role in predicting clinical trial outcome. A significant effort has gone into the production of the dataset that, for the first time, integrates clinical trial information with protein targets. Cleaned, organized, integrated data and code to map these entities, created as a part of this work, are available open-source. This reproducibility and the freely available code ensure that researchers with access to deep curated and proprietary clinical trial databases (we only use open-source data in this study) can further expand the scope of the results.

Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade

Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.