Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model

Deep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results through feature engineering (FE), the model regression results based on the CNN model for feature extraction have been substantially improved. The results showed that all six models (FE-SVM, FE-RF, FE-MLP, CNN-SVM, CNN-RF, and CNN-MLP) had good prediction accuracy, but the results based on the CNN model were better. The hyperparameters of six models were optimized by grid search and the 10-fold cross validation. Compared with the existing models in the literature, the model performance has been further improved. The model could be used as an intelligent tool to guide the design or screening of low-toxicity ILs.

The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis

Human ether-a-go-go-related gene (hERG) potassium channel blockage by small molecules may cause severe cardiac side effects. Thus, it is crucial to screen compounds for activity on the hERG channels early in the drug discovery process. In this study, we collected 5299 hERG inhibitors with diverse chemical structures from a number of sources. Based on this dataset, we evaluated different machine learning (ML) and deep learning (DL) algorithms using various integer and binary type fingerprints. A training set of 3991 compounds was used to develop quantitative structure-activity relationship (QSAR) models. The performance of the developed models was evaluated using a test set of 998 compounds. Models were further validated using external set 1 (263 compounds) and external set 2 (47 compounds). Overall, models with integer type fingerprints showed better performance than models with no fingerprints, converted binary type fingerprints or original binary type fingerprints. Comparison of ML and DL algorithms revealed that integer type fingerprints are suitable for ML, whereas binary type fingerprints are suitable for DL. The outcomes of this study indicate that the rational selection of fingerprints is important for hERG blocker prediction.

Computational investigations of hERG channel blockers: New insights and current predictive models

Identification of potential human Ether-a-go-go Related-Gene (hERG) potassium channel blockers is an essential part of the drug development and drug safety process in pharmaceutical industries or academic drug discovery centers, as they may lead to drug-induced QT prolongation, arrhythmia and Torsade de Pointes. Recent reports also suggest starting to address such issues at the hit selection stage. In order to prioritize molecules during the early drug discovery phase and to reduce the risk of drug attrition due to cardiotoxicity during pre-clinical and clinical stages, computational approaches have been developed to predict the potential hERG blockage of new drug candidates. In this review, we will describe the current in silico methods developed and applied to predict and to understand the mechanism of actions of hERG blockers, including ligand-based and structure-based approaches. We then discuss ongoing research on other ion channels and hERG polymorphism susceptible to be involved in LQTS and how systemic approaches can help in the drug safety decision.

The discovery of aurora kinase inhibitor by multi-docking-based virtual screening

We report the discovery of aurora kinase inhibitor using the fragment-based virtual screening by multi-docking strategy. Among a number of fragments collected from eMololecules, we found four fragment molecules showing potent activity (>50% at 100 μM) against aurora kinase. Based on the explored fragment scaffold, we selected two compounds in our synthesized library and validated the biological activity against Aurora kinase.

Synthesis, anticancer activity and pharmacokinetic analysis of 1-[(substituted 2-alkoxyquinoxalin-3-yl)aminocarbonyl]-4-(hetero)arylpiperazine derivatives

Based on the anticancer activity of novel quinoxalinyl-piperazine compounds, 1-[(5 or 6-substituted alkoxyquinoxalinyl)aminocarbonyl]-4-(hetero)arylpiperazine derivatives published in Bioorg. Med. Chem.2010, 18, 7966, we further explored the synthesis of 7 or 8-substituted quinoxalinyl piperazine derivatives. From in vitro studies of the newly synthesized compounds using human cancer cell lines, we identified some of the 8-substituted compounds, for example 6p, 6q and 6r, which inhibited the proliferation of various human cancer cells at nanomolar concentrations. Compound 6r, in particular, showed the lowest IC(50) values, ranging from 6.1 to 17nM, in inhibition of the growth of cancer cells, which is better than compound 6k (compound 25 in the reference cited above). In order to select and develop a leading compound among the quinoxaline compounds with substitutions on positions 5, 6, 7 or 8, the compounds comparable to compound 6k in in vitro cancer cell growth inhibition were chosen and their pharmacokinetic properties were evaluated in rats. In these studies, compound 6k showed the highest oral bioavailability of 83.4%, and compounds 6j and 6q followed, with 77.8% and 57.6%, respectively. From the results of in vitro growth inhibitory activities and the pharmacokinetic study, compound 6k is suggested for further development as an orally deliverable anticancer drug.