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AIMS: The machine learning-based QSAR modeling procedure, molecular generations, and molecular dynamic simulations were applied to virtually screen the DNA polymerase theta inhibitors. BACKGROUND: The DNA polymerase theta (Polθ or POLQ) is an attractive target for treatments of homologous recombination deficient (such as BRCA deficient) cancers. There are no approved drugs for targeting POLQ, and only one inhibitor is in Phase Ⅱclinical trials; thus, it is necessary to develop novel POLQ inhibitors. OBJECTIVES: To build machine learning models that predict the bioactivities of POLQ inhibitors. To build molecular generation models that generate diverse molecules. To virtually screen the generated molecules by the machine learning models. To analyze the binding modes of the screening results by molecular dynamic simulations. METHODS: In the present work, 325 inhibitors with POLQ polymerase domain bioactivities were Collected. Two machine learning methods, random forest and deep neural network, were used for building the ligand- and structure-based quantitative structure-activity relationship (QSAR) models. The substructure replacement-based method and transfer learning-based deep recurrent neural network method were used for molecular generations. Molecular docking and consensus QSAR models were carried out for virtual screening. The molecular dynamic simulations and MM/GBSA binding free energy calculation and decomposition were used to further analyze the screening results. RESULTS: The MCC values of the best ligand- and structure-based consensus QSAR models reached 0.651 and 0.361 for the test set, respectively. The machine learning-based docking scores had better-predicted ability to distinguish the highly and weakly active poses than the original docking scores. The 96490 molecules were generated by both molecular generation methods, and 10 molecules were retained by virtual screening. Four favorable interactions were concluded by molecular dynamic simulations. CONCLUSION: We hope that the screening results and the binding modes are helpful for designing the highly active POLQ polymerase inhibitors and the models of the molecular design workflow can be used as reliable tools for drug design.

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Tire wear particles (TWPs) enter aquatic ecosystems through various pathways, such as rainwater and urban runoff. Additives in TWPs can harm aquatic organisms in these ecosystems. Therefore, it is essential to investigate their toxicity to aquatic organisms. In our study, we initially recorded the median effective concentrations of 21 TWP-derived compounds on Chlorella vulgaris growth, ranging from 0.04 to 8.60 mg/L. Subsequently, through an extensive review of the literature, we incorporated 112 compounds with specific toxicity endpoints to construct the QSAR model using genetic algorithm and multiple linear regression techniques, followed by the construction of the consensus model and the quantitative read-across structure-activity relationship (q-RASAR) model. Meanwhile, we employed rigorous internal and external validation measures to assess the performance of the model. The results indicated that the developed q-RASAR model exhibited strong adaptation, robustness, and reliable prediction, with q-RASAR indicators of Q2LOO = 0.7673, R2tr = 0.8079, R2test = 0.8610, Q2Fn = 0.8285-0.8614, and CCCtest = 0.9222. Based on an external dataset containing 128 emerging TWP-derived compounds, the model's applicability domain coverage was 90.6 %. The q-RASAR model predicted that the structure of diphenylamine was associated with higher toxicity, possibly liked to the SpMax2_Bhm and LogBCF descriptors. The established model reliably provides prediction and fills a critical data gap. These findings highlight the potential risks posed by emerging TWP-derived compounds to aquatic organisms.

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Methanol-gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol-gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol-gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)-PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol-gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition.

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Cancer is a complex gene mutation disease that derives from the accumulation of mutations during somatic cell evolution. With the advent of high-throughput technology, a large amount of omics data has been generated, and how to find cancer-related driver genes from a large number of omics data is a challenge. In the early stage, the researchers developed many frequency-based driver genes identification methods, but they could not identify driver genes with low mutation rates well. Afterwards, researchers developed network-based methods by fusing multi-omics data, but they rarely considered the connection among features. In this paper, after analyzing a large number of methods for integrating multi-omics data, a hierarchical weak consensus model for fusing multiple features is proposed according to the connection among features. By analyzing the connection between PPI network and co-mutation hypergraph network, this paper firstly proposes a new topological feature, called co-mutation clustering coefficient (CMCC). Then, a hierarchical weak consensus model is used to integrate CMCC, mRNA and miRNA differential expression scores, and a new driver genes identification method HWC is proposed. In this paper, the HWC method and current 7 state-of-the-art methods are compared on three types of cancers. The comparison results show that HWC has the best identification performance in statistical evaluation index, functional consistency and the partial area under ROC curve. Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-024-00279-6.

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Understanding the historical context and contemporary trends in advanced practice registered nurse (APRN) education and regulation is pivotal for effective professional advocacy. Until the release of the APRN Consensus Model in 2008, a uniform model for APRN regulation was lacking. Adopting the model's recommendations has implications for APRNs beyond licensure and regulation, including full practice authority, license portability, and patient access to APRN-led care. A comprehensive understanding of APRN education and regulation empowers nurses, APRNs, and stakeholders to drive the profession forward through informed advocacy.

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PURPOSE: Motor neuron disease (MND) causes damage to the upper and lower motor neurons including the motor cranial nerves, the latter resulting in bulbar involvement with atrophy of the tongue muscle. To measure tongue atrophy, an operator independent automatic segmentation of the tongue is crucial. The aim of this study was to apply convolutional neural network (CNN) to MRI data in order to determine the volume of the tongue. METHODS: A single triplanar CNN of U-Net architecture trained on axial, coronal, and sagittal planes was used for the segmentation of the tongue in MRI scans of the head. The 3D volumes were processed slice-wise across the three orientations and the predictions were merged using different voting strategies. This approach was developed using MRI datasets from 20 patients with 'classical' spinal amyotrophic lateral sclerosis (ALS) and 20 healthy controls and, in a pilot study, applied to the tongue volume quantification to 19 controls and 19 ALS patients with the variant progressive bulbar palsy (PBP). RESULTS: Consensus models with softmax averaging and majority voting achieved highest segmentation accuracy and outperformed predictions on single orientations and consensus models with union and unanimous voting. At the group level, reduction in tongue volume was not observed in classical spinal ALS, but was significant in the PBP group, as compared to controls. CONCLUSION: Utilizing single U-Net trained on three orthogonal orientations with consequent merging of respective orientations in an optimized consensus model reduces the number of erroneous detections and improves the segmentation of the tongue. The CNN-based automatic segmentation allows for accurate quantification of the tongue volumes in all subjects. The application to the ALS variant PBP showed significant reduction of the tongue volume in these patients and opens the way for unbiased future longitudinal studies in diseases affecting tongue volume.

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Yalkowsky's General Solubility Equation (GSE), with its three fixed constants, is popular and easy to apply, but is not very accurate for polar, zwitterionic, or flexible molecules. This review examines the findings of a series of studies, where we have sought to come up with a better prediction model, by comparing the performances of the GSE to Abraham's Solvation Equation (ABSOLV), and Random Forest regression (RFR) machine-learning (ML) method. Large, well-curated aqueous intrinsic solubility databases are available. However, drugs may be sparsely distributed in chemical space, concentrated in clusters. Even a large database might overlook some regions. Test compounds from under-represented portions of space may be poorly predicted, as might be the case with the 'loose' set of 32 drugs in the Second Solubility Challenge (2020). There appears to be still a need for better coverage of drug space. Increasingly, current trends in predictions of solubility use calculated input descriptors, which may be an advantage for exploring properties of molecules yet to be synthesized. The risk may be that overall prediction approaches might be based on accumulated uncertainty. The increasing use of ML/AI methods can lead to accurate predictions, but such predictions may not readily suggest the strategies to pursue in selecting yet-to-be-synthesized compounds. Based on our latest findings, we recommend predictions based on both 'grouped' ABSOLV(GRP) and 'Flexible Acceptor' GSE(Φ,B) models with the provided best-fit parameters, where Φ is the Kier molecular flexibility index and B is the Abraham H-bond acceptor strength. For molecules with Φ < 11, the prudent choice is to pick the Consensus Model, the average of ABSOLV(GRP) and GSE(Φ,B). For more flexible molecules, GSE(Φ,B) is recommended.

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BACKGROUND: The human gut microbiome (HGM), consisting of trillions of microorganisms, is crucial to human health. Adverse drug use is one of the most important causes of HGM disorder. Thus, it is necessary to identify drugs or compounds with anti-commensal effects on HGM in the early drug discovery stage. This study proposes a novel anti-commensal effects classification using a machine learning method and optimal molecular features. To improve the prediction performance, we explored combinations of six fingerprints and three descriptors to filter the best characterization as molecular features. RESULTS: The final consensus model based on optimal features yielded the F1-score of 0.725 ± 0.014, ACC of 82.9 ± 0.7%, and AUC of 0.791 ± 0.009 for five-fold cross-validation. In addition, this novel model outperformed the prior studies by using the same algorithm. Furthermore, the important chemical descriptors and misclassified anti-commensal compounds are analyzed to better understand and interpret the model. Finally, seven structural alerts responsible for the chemical anti-commensal effect are identified, implying valuable information for drug design. CONCLUSION: Our study would be a promising tool for screening anti-commensal compounds in the early stage of drug discovery and assessing the potential risks of these drugs in vivo.

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The blood-brain barrier (BBB) is an important defence mechanism that restricts disease-causing pathogens and toxins to enter the brain from the bloodstream. In recent years, many in silico methods were proposed for predicting BBB permeability, however, the reliability of these models is questionable due to the smaller and class-imbalance dataset which subsequently leads to a very high false positive rate. In this study, machine learning and deep learning-based predictive models were built using XGboost, Random Forest, Extra-tree classifiers and deep neural network. A dataset of 8153 compounds comprising both the BBB permeable and BBB non-permeable was curated and subjected to calculations of molecular descriptors and fingerprints for generating the features for machine learning and deep learning models. Three balancing techniques were then applied to the dataset to address the class-imbalance issue. A comprehensive comparison among the models showed that the deep neural network model generated on the balanced MACCS fingerprint dataset outperformed with an accuracy of 97.8% and a ROC-AUC score of 0.98 among all the models. Additionally, a dynamic consensus model was prepared with the machine learning models and validated with a benchmark dataset for predicting BBB permeability with higher confidence scores.

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Chemical mutagenicity is a serious issue that needs to be addressed in early drug discovery. Over a long period of time, medicinal chemists have manually summarized a series of empirical rules for the optimization of chemical mutagenicity. However, given the rising amount of data, it is getting more difficult for medicinal chemists to identify more comprehensive chemical rules behind the biochemical data. Herein, we integrated a large Ames mutagenicity data set with 8576 compounds to derive mutagenicity transformation rules for reversing Ames mutagenicity via matched molecular pairs analysis. A well-trained consensus model with a reasonable applicability domain was constructed, which showed favorable performance in the external validation set with an accuracy of 0.815. The model was used to assess the generalizability and validity of these mutagenicity transformation rules. The results demonstrated that these rules were of great value and could provide inspiration for the structural modifications of compounds with potential mutagenic effects. We also found that the local chemical environment of the attachment points of rules was critical for successful transformation. To facilitate the use of these mutagenicity transformation rules, we integrated them into ADMETopt2 ( http://lmmd.ecust.edu.cn/admetsar2/admetopt2/ ), a free web server for optimization of chemical ADMET properties. The above-mentioned approach would be extended to the optimization of other toxicity endpoints.

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In group decision making (GDM), to facilitate an acceptable consensus among the experts from different fields, time and resources are paid for persuading experts to modify their opinions. Thus, consensus costs are important for the GDM process. Notwithstanding, the unit costs in the common linear cost functions are always fixed, yet experts will generally express more resistance if they have to make more compromises. In this study, we use the quadratic cost functions, the marginal costs of which increase with the opinion changes. Aggregation operators are also considered to expand the applications of the consensus methods. Moreover, this paper further analyzes the minimum cost consensus models under the weighted average (WA) operator and the ordered weighted average (OWA) operators, respectively. Corresponding approaches are developed based on strictly convex quadratic programming and some desirable properties are also provided. Finally, some examples and comparative analyses are furnished to illustrate the validity of the proposed models.

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Intrinsic aqueous solubility is a foundational property for understanding the chemical, technological, pharmaceutical, and environmental behavior of drug substances. Despite years of solubility research, molecular structure-based prediction of the intrinsic aqueous solubility of drug substances is still under active investigation. This paper describes the authors' systematic data-driven modelling in which two fit-for-purpose training data sets for intrinsic aqueous solubility were collected and curated, and three quantitative structure-property relationships were derived to make predictions for the most recent solubility challenge. All three models perform well individually, while being mechanistically transparent and easy to understand. Molecular descriptors involved in the models are related to the following key steps in the solubility process: dissociation of the molecule from the crystal, formation of a cavity in the solvent, and insertion of the molecule into the solvent. A consensus modeling approach with these models remarkably improved prediction capability and reduced the number of strong outliers by more than two times. The performance and outliers of the second solubility challenge predictions were analyzed retrospectively. All developed models have been published in the QsarDB.org repository according to FAIR principles and can be used without restrictions for exploring, downloading, and making predictions.

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Reproductive toxicity is one of the prominent endpoints in the risk assessment of environmental and industrial chemicals. Due to the complexity of the reproductive system, traditional reproductive toxicity testing in animals, especially guideline multigeneration reproductive toxicity studies, take a long time and are expensive. Therefore, machine learning, as a promising alternative approach, should be considered when evaluating the reproductive toxicity of chemicals. We curated rat multigeneration reproductive toxicity testing data of 275 chemicals from ToxRefDB (Toxicity Reference Database) and developed predictive models using seven machine learning algorithms (decision tree, decision forest, random forest, k-nearest neighbors, support vector machine, linear discriminant analysis, and logistic regression). A consensus model was built based on the seven individual models. An external validation set was curated from the COSMOS database and the literature. The performances of individual and consensus models were evaluated using 500 iterations of 5-fold cross-validations and the external validation data set. The balanced accuracy of the models ranged from 58% to 65% in the 5-fold cross-validations and 45%-61% in the external validations. Prediction confidence analysis was conducted to provide additional information for more appropriate applications of the developed models. The impact of our findings is in increasing confidence in machine learning models. We demonstrate the importance of using consensus models for harnessing the benefits of multiple machine learning models (i.e., using redundant systems to check validity of outcomes). While we continue to build upon the models to better characterize weak toxicants, there is current utility in saving resources by being able to screen out strong reproductive toxicants before investing in vivo testing. The modeling approach (machine learning models) is offered for assessing the rat multigeneration reproductive toxicity of chemicals. Our results suggest that machine learning may be a promising alternative approach to evaluate the potential reproductive toxicity of chemicals.

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In recent decades, a rapid range expansion of the golden jackal (Canis aureus) towards Northern and Western Europe has been observed. The golden jackal is a medium-sized canid, with a broad and flexible diet. Almost 200 different parasite species have been reported worldwide from C. aureus, including many parasites that are shared with dogs and cats and parasite species of public health concern. As parasites may follow the range shifts of their host, the range expansion of the golden jackal could be accompanied by changes in the parasite fauna in the new ecosystems. In the new distribution area, the golden jackal could affect ecosystem equilibrium, e.g., through changed competition situations or predation pressure. In a niche modeling approach, we project the future climatic habitat suitability of the golden jackal in Europe in the context of whether climatic changes promote range expansion. We use an ensemble forecast based on six presence-absence algorithms to estimate the climatic suitability of C. aureus for different time periods up to the year 2100 considering different IPCC scenarios on future development. As predictor variables, we used six bioclimatic variables provided by worldclim. Our results clearly indicate that areas with climatic conditions analogous to those of the current core distribution area of the golden jackal in Europe will strongly expand towards the north and the west in future decades. Thus, the observed range expansion may be favored by climate change. The occurrence of stable populations can be expected in Central Europe. With regard to biodiversity and public health concerns, the population and range dynamics of the golden jackal should be surveyed. Correlative niche models provide a useful and frequently applied tool for this purpose. The results can help to make monitoring more efficient by identifying areas with suitable habitat and thus a higher probability of occurrence.

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Natural and engineered nanoparticles (NPs) entering the environment are influenced by many physicochemical processes and show various behavior in different systems (e.g., natural waters showing different characteristics). Determining the physicochemical characteristics and predicting the behavior of nanoparticles ending up in the natural aquatic environment are key aspects of their risk assessment. Here, we show that the quantitative structure-property relationship modeling method used in nanoinformatics (nano-QSPR) can be successfully applied to predict environmental fate-relevant properties (electrophoretic mobility) of TiO2, ZnO, and CeO2 nanoparticles. However, in contrast to the previous works, we postulate to use, in parallel: (i) the nanoparticles' structure descriptors (S-descriptors) and (ii) the environment descriptors (E-descriptors) as the input variables. Thus, the method should be abbreviated more precisely as nano-QSEPR ("E" stands for the "environment"). As a proof-of-the-concept, we have developed a group of models (including MLR, GA-PLS, PCR, and Meta-Consensus models) with high predictive capabilities (QEXT2 = 0.931 for the GA-PLS model), where the S-descriptors are represented by the core-shell model descriptor and the E-descriptors - by different ambient water features (including ions concentration and the ionic strength). The newly proposed nano-QSEPR modeling scheme can be efficiently used to design safe and sustainable nanomaterials.

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BACKGROUND: The Consensus Model specifies distinct education preparations for acute care and primary care nurse practitioners (NP), but incomplete implementation and employer hiring practices risk misalignment of certification and practice. PURPOSE: Report the prevalence of acute care certification among NPs working in acute care, investigate the factors associated with alignment, and explore the impact of alignment on nurse outcomes. METHODS: Using the 2018 National Sample Survey of Registered Nurses, we selected NPs practicing acute care and compared employment, education, and nurse outcomes by certification alignment. FINDINGS: A minority of NPs (44.5%) held acute care certification. Controlling for NP characteristics, those practicing in states that restrict a NP's practice to their area of certification had 47% higher odds of acute care certification. DISCUSSION: Understanding patterns of alignment in the NP workforce and the factors that produce them is critical to an appropriate regulatory framework for advanced practice nursing.

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In the process of reaching consensus, it is necessary to coordinate different views to form a general group opinion. However, there are many uncertain factors in this process, which has brought different degrees of influence in group decision-making. Besides, these uncertain elements bring the risk of loss to the whole process of consensus building. Currently available models not account for these two aspects. To deal with these issues, three different modeling methods for constructing the two-stage mean-risk stochastic minimum cost consensus models (MCCMs) with asymmetric adjustment cost are investigated. Due to the complexity of the resulting models, the L-shaped algorithm is applied to achieve an optimal solution. In addition, a numerical example of a peer-to-peer online lending platform demonstrated the utility of the proposed modeling approach. To verify the result obtained by the L-shaped algorithm, it is compared with the CPLEX solver. Moreover, the comparison results show the accuracy and efficiency of the given method. Sensitivity analyses are undertaken to assess the impact of risk on results. And in the presence of asymmetric cost, the comparisons between the new proposed risk-averse MCCMs and the two-stage stochastic MCCMs and robust consensus models are also given.

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As COVID-19 has spread rapidly, detection of the COVID-19 infection from radiology and radiography images is probably one of the quickest ways to diagnose the patients. Many researchers found the necessity to utilize chest X-ray and chest computed tomography imaging to diagnose COVID-19 infection. In this paper, our objective is to minimize the false negatives and false positives in the detection process. Reduction in the number of false negatives minimizes community spread of the COVID-19 pandemic. Reducing false positives help people avoid mental trauma and wasteful expenses. This paper proposes a novel weighted consensus model to minimize the number of false negatives and false positives without compromising accuracy. In the proposed novel weighted consensus model, the accuracy of individual classification models is normalized. While predicting, different models predict different classes, and the sum of the normalized accuracy for a particular class is then considered based on a predefined threshold value. We used traditional Machine Learning classification algorithms like Linear Regression, Support Vector Machine, k-Nearest Neighbours, Decision Tree, and Random Forest for the weighted consensus experimental evaluation. We predicted the classes, which provided better insights into the condition. The proposed model can perform as well as the existing state-of-the-art technique in terms of accuracy (99.64%) and reduce false negatives and false positives.

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Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity. In the present study, we focused on in silico prediction of chemical-induced hematotoxicity with machine learning (ML) and deep learning (DL) methods. We collected a large data set contained 632 hematotoxic chemicals and 1525 approved drugs without hematotoxicity. Computational models were built using several different machine learning and deep learning algorithms integrated on the Online Chemical Modeling Environment (OCHEM). Based on the three best individual models, a consensus model was developed. It yielded the prediction accuracy of 0.83 and balanced accuracy of 0.77 on external validation. The consensus model and the best individual model developed with random forest regression and classification algorithm (RFR) and QNPR descriptors were made available at https://ochem.eu/article/135149 , respectively. The relevance of 8 commonly used molecular properties and chemical-induced hematotoxicity was also investigated. Several molecular properties have an obvious differentiating effect on chemical-induced hematotoxicity. Besides, 12 structural alerts responsible for chemical hematotoxicity were identified using frequency analysis of substructures from Klekota-Roth fingerprint. These results should provide meaningful knowledge and useful tools for hematotoxicity evaluation in drug discovery and environmental risk assessment.

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Drug-induced ototoxicity has become a serious global problem, because of leading to deafness in hundreds of thousands of people every year. It always results from exposure to drugs or environmental chemicals that cause the impairment and degeneration of the inner ear. Herein, we focused on the in silico modeling of drug-induced ototoxicity of chemicals. We collected 1,102 ototoxic medications and 1,705 non-ototoxic drugs. Based on the data set, a series of computational models were developed with different traditional machine learning and deep learning algorithms implemented on an online chemical database and modeling environment. Six ML models performed best on 5-fold cross-validation and test set. A consensus model was developed with the best individual models. These models were further validated with an external validation. The consensus model showed best predictive ability, with high accuracy of 0.95 on test set and 0.90 on validation set. The consensus model and the data sets used for model development are available at https://ochem.eu/model/46566321. Besides, 16 structural alerts responsible for drug-induced ototoxicity were identified. We hope the results could provide meaningful knowledge and useful tools for ototoxicity evaluation in drug discovery and environmental risk assessment.

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