scholarly journals Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures

2022 ◽  
Author(s):  
Kousuke Nishikiori ◽  
Kentaro Tanaka ◽  
Yoshihiro Uesawa
Keyword(s):  
Prediction Model ◽  
Model Validation ◽  
Prediction Models ◽  
Removal Rate ◽  
Structural Characteristics ◽  
Estimation Method ◽  
Quantitative Structure Activity Relationship ◽  
Pharmacokinetic Parameters ◽  
Qsar Analysis ◽  
Chemical Structures

Abstract In designing drug dosing for hemodialysis patients, the removal rate (RR) of the drug by hemodialysis is important. However, acquiring the RR is difficult, and there is a need for an estimation method that can be used in clinical settings. In this study, the RR predictive model was constructed using the RR of known drugs by quantitative structure–activity relationship (QSAR) analysis. Drugs were divided into a model construction drug set (75%) and a model validation drug set (25%). The RR was collected from 143 medicines. The objective variable (RR) and chemical structural characteristics (descriptors) of the drug (explanatory variable) were used to construct a prediction model using partial least squares (PLS) regression and artificial neural network (ANN) analyses. The determination coefficients in the PLS and ANN methods were 0.586 and 0.721 for the model validation drug set, respectively. QSAR analysis successfully constructed dialysis RR prediction models that were comparable or superior to those using pharmacokinetic parameters. Considering that the RR dataset contains potential errors, we believe that this study has achieved the most reliable RR prediction accuracy currently available. These predictive RR models can be achieved using only the chemical structure of the drug. This model is expected to be applied at the time of hemodialysis. Graphic Abstract

scholarly journals kGCN: A Graph-Based Deep Learning Framework for Chemical Structures

2020 ◽  
Author(s):  
Ryosuke Kojima ◽  
Shoichi Ishida ◽  
Masateru Ohta ◽  
Hiroaki Iwata ◽  
Teruki Honma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Prediction Models ◽  
Bayesian Optimization ◽  
Factors Affecting ◽  
Learning Framework ◽  
Chemical Structures ◽  
Programming Skills ◽  
Model Tuning ◽  
Interpretation Of Results

<div>Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multimodal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo/kGCN.</div>

scholarly journals Predicting blood-to-plasma concentration ratios of drugs from chemical structures and volumes of distribution in humans

2021 ◽  
Author(s):  
Hideaki Mamada ◽  
Kazuhiko Iwamoto ◽  
Yukihiro Nomura ◽  
Yoshihiro Uesawa
Keyword(s):  
Plasma Concentration ◽  
Prediction Model ◽  
Correlation Coefficient ◽  
Prediction Models ◽  
Molecular Descriptors ◽  
Volume Of Distribution ◽  
Human Pharmacokinetics ◽  
Chemical Structures ◽  
Drug Concentrations ◽  
Concentration Ratios

Abstract Despite their importance in determining the dosing regimen of drugs in the clinic, only a few studies have investigated methods for predicting blood-to-plasma concentration ratios (Rb). This study established an Rb prediction model incorporating typical human pharmacokinetics (PK) parameters. Experimental Rb values were compiled for 289 compounds, offering reliable predictions by expanding the applicability domain. Notably, it is the largest list of Rb values reported so far. Subsequently, human PK parameters calculated from plasma drug concentrations, including the volume of distribution (Vd), clearance, mean residence time, and plasma protein binding rate, as well as 2702 kinds of molecular descriptors, were used to construct quantitative structure–PK relationship models for Rb. Among the evaluated PK parameters, logVd correlated best with Rb (correlation coefficient of 0.47). Thus, in addition to molecular descriptors selected by XGBoost, logVd was employed to construct the prediction models. Among the analyzed algorithms, artificial neural networks gave the best results. Following optimization using six molecular descriptors and logVd, the model exhibited a correlation coefficient of 0.64 and a root-mean-square error of 0.205, which were superior to those previously reported for other Rb prediction methods. Since Vd values and chemical structures are known for most medications, the Rb prediction model described herein is expected to be valuable in clinical settings. Graphical abstract

scholarly journals Quantitative structure-activity relationship modelling of influenza M2 ion channels inhibitors

2021 ◽  
pp. 36-36
Author(s):  
Ivanka Stankova ◽  
Radoslav Chayrov ◽  
Michaela Schmidtke ◽  
Dancho Danalev ◽  
Liudmila Ognichenko ◽  
...  
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Antiviral Activity ◽  
Quantitative Structure Activity Relationship ◽  
Adamantane Derivatives ◽  
Amino Acid Residues ◽  
Qsar Analysis ◽  
Chemical Structures ◽  
Low Cytotoxicity

A series of adamantane derivatives (rimantadine and amantadine) incorporating amino-acid residues are investigated by simplex representation of molecular structure (SiRMS) approach in order to found correlation between chemical structures of investigated compounds and obtained data for antiviral activity and cytotoxicity. The obtained data from QSAR analysis show that adamantane derivatives containing amino acids with short aliphatic non-polar residues in the lateral chain will have good antiviral activity against tested virus A/H3N2, strain Hong Kong/68 with low cytotoxicity. QSAR experiments and in vitro data show also good correlation and reveal that modified adamantine derivatives including guanidated in the lateral chain amino acid and ?-amino acids as substituents have lower or do not show any activity.

scholarly journals kGCN: A Graph-Based Deep Learning Framework for Chemical Structures

2020 ◽  
Author(s):  
Ryosuke Kojima ◽  
Shoichi Ishida ◽  
Masateru Ohta ◽  
Hiroaki Iwata ◽  
Teruki Honma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Prediction Models ◽  
Bayesian Optimization ◽  
Factors Affecting ◽  
Learning Framework ◽  
Chemical Structures ◽  
Programming Skills ◽  
Model Tuning ◽  
Interpretation Of Results

<div>Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multimodal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo/kGCN.</div>

scholarly journals kGCN: A graph-based deep learning framework for chemical structures

2020 ◽  
Author(s):  
Ryosuke Kojima ◽  
Shoichi Ishida ◽  
Masateru Ohta ◽  
Hiroaki Iwata ◽  
Teruki Honma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Prediction Models ◽  
Bayesian Optimization ◽  
Factors Affecting ◽  
Learning Framework ◽  
Chemical Structures ◽  
Programming Skills ◽  
Model Tuning ◽  
Interpretation Of Results

Abstract Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multi-modal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo/kGCN.

scholarly journals Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap–Deep Learning

Molecules ◽  
2020 ◽  
Vol 25 (6) ◽  
pp. 1317 ◽  
Author(s):  
Yasunari Matsuzaka ◽  
Takuomi Hosaka ◽  
Anna Ogaito ◽  
Kouichi Yoshinari ◽  
Yoshihiro Uesawa
Keyword(s):  
Deep Learning ◽  
Aryl Hydrocarbon Receptor ◽  
High Performance ◽  
Prediction Models ◽  
Chemical Activation ◽  
Quantitative Structure Activity Relationship ◽  
Receptor Activation ◽  
Superior Performance ◽  
Qsar Analysis ◽  
Aryl Hydrocarbon

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals contributes to the development and propagation of steatosis and hepatotoxicity. However, the mechanisms for AhR-induced hepatotoxicity and tumor propagation in the liver remain to be revealed, due to the wide variety of AhR ligands. Recently, quantitative structure–activity relationship (QSAR) analysis using deep neural network (DNN) has shown superior performance for the prediction of chemical compounds. Therefore, this study proposes a novel QSAR analysis using deep learning (DL), called the DeepSnap–DL method, to construct prediction models of chemical activation of AhR. Compared with conventional machine learning (ML) techniques, such as the random forest, XGBoost, LightGBM, and CatBoost, the proposed method achieves high-performance prediction of AhR activation. Thus, the DeepSnap–DL method may be considered a useful tool for achieving high-throughput in silico evaluation of AhR-induced hepatotoxicity.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

In vitro Cytotoxicity and Genotoxicity Analysis of Ten Tannery Chemicals Using SOS/umu Tests and High-content In vitro Micronucleus Tests

2018 ◽  
Vol 21 (4) ◽  
pp. 262-270 ◽  
Author(s):  
Zehao Huang ◽  
Na Li ◽  
Kaifeng Rao ◽  
Cuiting Liu ◽  
Zijian Wang ◽  
...  
Keyword(s):  
Micronucleus Test ◽  
A549 Cells ◽  
Quantitative Structure Activity Relationship ◽  
In Vitro Cytotoxicity ◽  
Cytotoxic Effects ◽  
Qsar Analysis ◽  
Cell Assays ◽  
Micronucleus Tests ◽  
Damaging Effects

Background: More than 2,000 chemicals have been used in the tannery industry. Although some tannery chemicals have been reported to have harmful effects on both human health and the environment, only a few have been subjected to genotoxicity and cytotoxicity evaluations. Objective: This study focused on cytotoxicity and genotoxicity of ten tannery chemicals widely used in China. Materials and Methods: DNA-damaging effects were measured using the SOS/umu test with Salmonella typhimurium TA1535/pSK1002. Chromosome-damaging and cytotoxic effects were determined with the high-content in vitro Micronucleus test (MN test) using the human-derived cell lines MGC-803 and A549. Conclusion: The cytotoxicity of the ten tannery chemicals differed somewhat between the two cell assays, with A549 cells being more sensitive than MGC-803 cells. None of the chemicals induced DNA damage before metabolism, but one was found to have DNA-damaging effects on metabolism. Four of the chemicals, DY64, SB1, DB71 and RR120, were found to have chromosome-damaging effects. A Quantitative Structure-Activity Relationship (QSAR) analysis indicated that one structural feature favouring chemical genotoxicity, Hacceptor-path3-Hacceptor, may contribute to the chromosome-damaging effects of the four MN-test-positive chemicals.

Synthesis of New N1Arylpiperazine Substituted Xanthine Derivatives and Evaluation of their Antioxidant and Cytotoxic Effects

2019 ◽  
Vol 19 (4) ◽  
pp. 528-537 ◽  
Author(s):  
Lily Andonova ◽  
Iva Valkova ◽  
Dimitrina Zheleva-Dimitrova ◽  
Maya Georgieva ◽  
Georgi Momekov ◽  
...  
Keyword(s):  
Antioxidant Activity ◽  
Structural Parameters ◽  
Leukemia Cell ◽  
Quantitative Structure Activity Relationship ◽  
Cytotoxic Effects ◽  
Qsar Analysis ◽  
Xanthine Derivatives ◽  
Human T Cell ◽  
Human Acute Myeloid Leukemia ◽  
Dye Reduction

Background: Cancer is one of the leading causes of morbidity and mortality worldwide, with approximately 14 million new cases in 2012, with most of the clinically used drugs being ineffective. Methylxanthines have raised more interest in research on modifying their structure because of their diverse biological activity. In addition, the piperazine nucleus is one of the most important heterocycles exhibiting remarkable pharmacological activities. Methods: The structure of the obtained compounds was characterized and elucidated by IR, 1H and 13C NMR and LCMS spectral analysis. The purity of the substances was proven by corresponding melting points and elemental analysis. The antioxidant activity was evaluated by four common methods – DPPH, ABTS, FRAP and lipid peroxidation assay. The cytotoxic effects of the tested series were evaluated using the standard MTT-dye reduction assay on three tumour cell lines. Results: A series of new xanthine derivatives comprising an arylpiperazine moiety at N1 were synthesized. The cytotoxicity against human T-cell leukemia cell SKW-3, human acute myeloid leukemia HL-60 and human Bcell precursor leukemia cell REH was evaluated. The relationship between the structure and citotoxicity of the compounds was investigated by quantitative structure-activity relationship (QSAR) analysis and the important structural parameters were drawn. Conclusion: The highest antioxidant activity was demonstrated by compound 6c. The highest cytotoxic effect was observed for compound 6f. It was found that cytotoxicity against SKW-3 depends on the electron density distribution in the structures. Branching of the molecular skeleton and introduction of heteroatoms like fluorine and sulfur in the structures also significantly improved the antiproliferative activity of the compounds.

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

2001 ◽  
Vol 10 (2) ◽  
pp. 241 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Fire Management ◽  
Prediction Models ◽  
Dew Point ◽  
Seasonal Effects ◽  
Experimental Program ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

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