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Ligand-based drug design methods are thought to require large experimental datasets to become useful for virtual screening. In this work, we propose a computational strategy to design novel inhibitors of coronavirus main protease, Mpro. The pipeline integrates publicly available screening and binding affinity data in a two-stage machine-learning model using the recent MACAW embeddings. Once trained, the model can be deployed to rapidly screen large libraries of molecules in silico. Several hundred thousand compounds were virtually screened and 10 of them were selected for experimental testing. From these 10 compounds, 8 showed a clear inhibitory effect on recombinant Mpro, with half-maximal inhibitory concentration values (IC50) in the range 0.18–18.82 μM. Cellular assays were also conducted to evaluate cytotoxic, haemolytic, and antiviral properties. A promising lead compound against coronavirus Mpro was identified with dose-dependent inhibition of virus infectivity and minimal toxicity on human MRC-5 cells. [ABSTRACT FROM AUTHOR]

Due to the rising environmental concerns, particularly air quality, the emission regulations for non-road mobile machinery are becoming increasingly strict. Real-time emission prediction from diesel engines is significant for emission control and regional pollution estimation. This study aims to develop a machine learning model and optimize its hyperparameters by using a hyperparameter optimization method to NO X emission. Firstly, we collected NO X emission data from test under the non-road transient test cycle (NRTC) and built a significant dataset to choose a best model. Then, the model was trained by dataset and the hyperparameters were automatically optimized by combining Bayesian and Population based training. The accuracy of the optimized was indicated by an R2 value of 0.9784 with the 8 input features. The relative error in the cycle level was 1.3%. Lastly, the quality of NO X emissions during the cycle and the effect of each parameter on NO X emissions were analyzed. The results show that the model is able to predict the real-time concentration changes of NO X more accurately. It can provide a reference for the research and development of emission control technology for non-road mobile machinery. [Display omitted] • Construct a highly accurate NOx emission prediction model based on data-driven methods. • The combination of Population Based Training and Bayesian optimization method improves the model accuracy. • The relative error in the cycle level was 1.3%. • NOx emission has a good correlation with rotational speed, torque and exhaust temperature. [ABSTRACT FROM AUTHOR]

Biomarker selection and cancer classification play an important role in knowledge discovery using genomic data. Successful identification of gene biomarkers and biological pathways can significantly improve the accuracy of diagnosis and help machine learning models have better performance on classification of different types of cancer. In this paper, we proposed a LogSum + L2 penalized logistic regression model, and furthermore used a coordinate decent algorithm to solve it. The results of simulations and real experiments indicate that the proposed method is highly competitive among several state-of-the-art methods. Our proposed model achieves the excellent performance in group feature selection and classification problems. [ABSTRACT FROM AUTHOR]

Motivation De novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins. However, owing to the low ion coverage in tandem mass spectra, the order of certain consecutive amino acids cannot be determined if all of their supporting fragment ions are missing, which results in the low precision of de novo sequencing. Results In order to solve this problem, we developed pNovo 3, which used a learning-to-rank framework to distinguish similar peptide candidates for each spectrum. Three metrics for measuring the similarity between each experimental spectrum and its corresponding theoretical spectrum were used as important features, in which the theoretical spectra can be precisely predicted by the pDeep algorithm using deep learning. On seven benchmark datasets from six diverse species, pNovo 3 recalled 29–102% more correct spectra, and the precision was 11–89% higher than three other state-of-the-art de novo sequencing algorithms. Furthermore, compared with the newly developed DeepNovo, which also used the deep learning approach, pNovo 3 still identified 21–50% more spectra on the nine datasets used in the study of DeepNovo. In summary, the deep learning and learning-to-rank techniques implemented in pNovo 3 significantly improve the precision of de novo sequencing, and such machine learning framework is worth extending to other related research fields to distinguish the similar sequences. Availability and implementation pNovo 3 can be freely downloaded from http://pfind.ict.ac.cn/software/pNovo/index.html. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

No observed adverse effect level (NOAEL) is an identified dose level which used as a point of departure to infer a safe exposure limit of chemicals, especially in food additives and cosmetics. Recently, in silico approaches have been employed as effective alternatives to determine the toxicity endpoints of chemicals instead of animal experiments. Several acceptable models have been reported, yet assessing the risk of repeated-dose toxicity remains inadequate. This study established robust machine learning predictive models for NOAEL at different exposure durations by constructing high-quality datasets and comparing different kinds of molecular representations and algorithms. The features of molecular structures affecting NOAEL were explored using advanced cheminformatics methods, and predictive models also communicated the NOAEL between different species and exposure durations. In addition, a NOAEL prediction tool for chemical risk assessment is provided (available at: https://github.com/ifyoungnet/NOAEL). We hope this study will help researchers easily screen and evaluate the subacute and sub-chronic toxicity of disparate compounds in the development of food additives in the future. [Display omitted] • Proposed a comprehensive in silico NOAEL prediction scheme. • Datasets of NOAEL under different species and exposure durations were constructed. • Established successful models by comparing different algorithms and descriptors. • Identified important sub-structures and indications for NOAEL. • Provided an easy-to-use pipeline for virtual screening based on KNIME software. [ABSTRACT FROM AUTHOR]

[Display omitted] • Long-term daily NO 2 are derived for post-policy evaluation and exposure assessment. • A common modeling approach (Base-RF) gives biased estimation in back-extrapolation. • We propose a novel approach named RBE-RF for the bias correction. • Average NO 2 levels for China in 2011 can be underestimated by 22.4% by Base-RF. • National population exposed to NO 2 > 40 µg/m3 is 18.5% by Base-RF and 33.0% by RBE-RF. Long-term surface NO 2 data are essential for retrospective policy evaluation and chronic human exposure assessment. In the absence of NO 2 observations for Mainland China before 2013, training a model with 2013–2018 data to make predictions for 2005–2012 (back-extrapolation) could cause substantial estimation bias due to concept drift. This study aims to correct the estimation bias in order to reconstruct the spatiotemporal distribution of daily surface NO 2 levels across China during 2005–2018. On the basis of ground- and satellite-based data, we proposed the robust back-extrapolation with a random forest (RBE-RF) to simulate the surface NO 2 through intermediate modeling of the scaling factors. For comparison purposes, we also employed a random forest (Base-RF), as a representative of the commonly used approach, to directly model the surface NO 2 levels. The validation against Taiwan's NO 2 observations during 2005–2012 showed that RBE-RF adequately corrected the substantial underestimation by Base-RF. The RMSE decreased from 10.1 to 8.2 µg/m3, 7.1 to 4.3 µg/m3, and 6.1 to 2.9 µg/m3 in predicting daily, monthly, and annual levels, respectively. For North China with the most severe pollution, the population-weighted NO 2 ([NO 2 ] pw) during 2005–2012 was estimated as 40.2 and 50.9 µg/m3 by Base-RF and RBE-RF, respectively, i.e., 21.0% difference. While both models predicted that the national annual [NO 2 ] pw increased during 2005–2011 and then decreased, the interannual trends were underestimated by >50.2% by Base-RF relative to RBE-RF. During 2005–2018, the nationwide population that lived in the areas with NO 2 > 40 µg/m3 were estimated as 259 and 460 million by Base-RF and RBE-RF, respectively. With RBE-RF, we corrected the estimation bias in back-extrapolation and obtained a full-coverage dataset of daily surface NO 2 across China during 2005–2018, which is valuable for environmental management and epidemiological research. [ABSTRACT FROM AUTHOR]