4 research outputs found
Importance of Incorporating Protein Flexibility in Molecule Modeling: A Theoretical Study on Type I1/2 NIK Inhibitors
NF-κB inducing kinase (NIK), which is considered as the central component of the non-canonical NF-κB pathway, has been proved to be an important target for the regulation of the immune system. In the past few years, NIK inhibitors with various scaffolds have been successively reported, among which type I1/2 inhibitors that can not only bind in the ATP-binding pocket at the DFG-in state but also extend into an additional back pocket, make up the largest proportion of the NIK inhibitors, and are worthy of more attention. In this study, an integration protocol that combines molecule docking, MD simulations, ensemble docking, MM/GB(PB)SA binding free energy calculations, and decomposition was employed to understand the binding mechanism of 21 tricyclic type I1/2 NIK inhibitors. It is found that the docking accuracy is largely dependent on the selection of docking protocols as well as the crystal structures. The predictions given by the ensemble docking based on multiple receptor conformations (MRCs) and the MM/GB(PB)SA calculations based on MD simulations showed higher linear correlations with the experimental data than those given by conventional rigid receptor docking (RRD) methods (Glide, GOLD, and Autodock Vina), highlighting the importance of incorporating protein flexibility in predicting protein–ligand interactions. Further analysis based on MM/GBSA demonstrates that the hydrophobic interactions play the most essential role in the ligand binding to NIK, and the polar interactions also make an important contribution to the NIK-ligand recognition. A deeper comparison of several pairs of representative derivatives reveals that the hydrophobic interactions are vitally important in the structural optimization of analogs as well. Besides, the H-bond interactions with some key residues and the large desolvation effect in the back pocket devote to the affinity distinction. It is expected that our study could provide valuable insights into the design of novel and potent type I1/2 NIK inhibitors
ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity
As a dangerous end point, respiratory
toxicity can cause serious
adverse health effects and even death. Meanwhile, it is a common and
traditional issue in occupational and environmental protection. Pharmaceutical
and chemical industries have a strong urge to develop precise and
convenient computational tools to evaluate the respiratory toxicity
of compounds as early as possible. Most of the reported theoretical
models were developed based on the respiratory toxicity data sets
with one single symptom, such as respiratory sensitization, and therefore
these models may not afford reliable predictions for toxic compounds
with other respiratory symptoms, such as pneumonia or rhinitis. Here,
based on a diverse data set of mouse intraperitoneal respiratory toxicity
characterized by multiple symptoms, a number of quantitative and qualitative
predictions models with high reliability were developed by machine
learning approaches. First, a four-tier dimension reduction strategy
was employed to find an optimal set of 20 molecular descriptors for
model building. Then, six machine learning approaches were used to
develop the prediction models, including relevance vector machine
(RVM), support vector machine (SVM), regularized random forest (RRF),
extreme gradient boosting (XGBoost), naïve Bayes (NB), and
linear discriminant analysis (LDA). Among all of the models, the SVM
regression model shows the most accurate quantitative predictions
for the test set (<i>q</i><sup>2</sup><sub>ext</sub> = 0.707),
and the XGBoost classification model achieves the most accurate qualitative
predictions for the test set (MCC of 0.644, AUC of 0.893, and global
accuracy of 82.62%). The application domains were analyzed, and all
of the tested compounds fall within the application domain coverage.
We also examined the structural features of the compounds and important
fragments with large prediction errors. In conclusion, the SVM regression
model and the XGBoost classification model can be employed as accurate
prediction tools for respiratory toxicity
ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches
Xenobiotic chemicals and their metabolites
are mainly excreted
out of our bodies by the urinary tract through the urine. Chemical-induced
urinary tract toxicity is one of the main reasons that cause failure
during drug development, and it is a common adverse event for medications,
natural supplements, and environmental chemicals. Despite its importance,
there are only a few <i>in silico</i> models for assessing
urinary tract toxicity for a large number of compounds with diverse
chemical structures. Here, we developed a series of qualitative and
quantitative structure–activity relationship (QSAR) models
for predicting urinary tract toxicity. In our study, the recursive
feature elimination method incorporated with random forests (RFE-RF)
was used for dimension reduction, and then eight machine learning
approaches were used for QSAR modeling, i.e., relevance vector machine
(RVM), support vector machine (SVM), regularized random forest (RRF),
C5.0 trees, eXtreme gradient boosting (XGBoost), AdaBoost.M1, SVM
boosting (SVMBoost), and RVM boosting (RVMBoost). For building classification
models, the synthetic minority oversampling technique was used to
handle the imbalance data set problem. Among all the machine learning
approaches, SVMBoost based on the RBF kernel achieves both the best
quantitative (<i>q</i><sub>ext</sub><sup>2</sup> = 0.845) and qualitative predictions for the
test set (MCC of 0.787, AUC of 0.893, sensitivity of 89.6%, specificity
of 94.1%, and global accuracy of 90.8%). The application domains were
then analyzed, and all of the tested chemicals fall within the application
domain coverage. We also examined the structure features of the chemicals
with large prediction errors. In brief, both the regression and classification
models developed by the SVMBoost approach have reliable prediction
capability for assessing chemical-induced urinary tract toxicity