Computational neural network analysis of the affinity of lobeline and tetrabenazine analogs for the vesicular monoamine transporter-2

Chi-MIC-share: a new feature selection algorithm for quantitative structure–activity relationship models

An algorithm based on an improved maximal information coefficient and a redundant allocation strategy, which can terminate feature selection automatically, is presented.

Virtual Screening Meets Deep Learning

BACKGROUND

Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence.

OBJECTIVE

This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints

Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.

Modeling in vitro inhibition of butyrylcholinesterase using molecular docking, multi-linear regression and artificial neural network approaches

Butyrylcholinesterase (BChE) has been an important protein used for development of anti-cocaine medication. Through computational design, BChE mutants with ∼2000-fold improved catalytic efficiency against cocaine have been discovered in our lab. To study drug-enzyme interaction it is important to build mathematical model to predict molecular inhibitory activity against BChE. This report presents a neural network (NN) QSAR study, compared with multi-linear regression (MLR) and molecular docking, on a set of 93 small molecules that act as inhibitors of BChE by use of the inhibitory activities (pIC₅₀ values) of the molecules as target values. The statistical results for the linear model built from docking generated energy descriptors were: r(2)=0.67, rmsd=0.87, q(2)=0.65 and loormsd=0.90; the statistical results for the ligand-based MLR model were: r(2)=0.89, rmsd=0.51, q(2)=0.85 and loormsd=0.58; the statistical results for the ligand-based NN model were the best: r(2)=0.95, rmsd=0.33, q(2)=0.90 and loormsd=0.48, demonstrating that the NN is powerful in analysis of a set of complicated data. As BChE is also an established drug target to develop new treatment for Alzheimer's disease (AD). The developed QSAR models provide tools for rationalizing identification of potential BChE inhibitors or selection of compounds for synthesis in the discovery of novel effective inhibitors of BChE in the future.

Improving the inhibitory activity of arylidenaminoguanidine compounds at the N-methyl-D-aspartate receptor complex from a recursive computational-experimental structure-activity relationship study

Using a combination of both the partial least squares (PLS) and back-propagation artificial neural network (ANN) pattern recognition methods, several models have been developed to predict the activity of a series of arylidenaminoguanidine analogs as inhibitory modulators of the N-methyl-D-aspartate receptor complex. This was done by correlating structural and physicochemical descriptors obtained from computation software with the experimentally observed [(3)H]MK-801 displacement ability of a small library of synthesized and in vitro screened arylidenaminoguanidines. Results for the generated PLS model were r(2)=0.814, rmsd=0.208, rCV(2)=0.714, loormsd=0.261. The ANN model was created utilizing the eleven descriptors from the PLS model for comparison. The quality of the ANN model (r(2)=0.828, rmsd=0.200, rCV(2)=0.721, loormsd=0.257) is similar to the PLS model, and indicates that the feature between the inputs and the output is majorly linear. These computational models were able to predict inhibition of the NMDA receptor complex by this series of compounds in silico, affording a predictive structure-based 'pre-screening' paradigm for the arylideneaminoguanidine analogs.

Design, synthesis and interaction at the vesicular monoamine transporter-2 of lobeline analogs: potential pharmacotherapies for the treatment of psychostimulant abuse

The vesicular monoamine transporter-2 (VMAT2) is considered as a new target for the development of novel therapeutics to treat psychostimulant abuse. Current information on the structure, function and role of VMAT2 in psychostimulant abuse are presented. Lobeline, the major alkaloidal constituent of Lobelia inflata, interacts with nicotinic receptors and with VMAT2. Numerous studies have shown that lobeline inhibits both the neurochemical and behavioral effects of amphetamine in rodents, and behavioral studies demonstrate that lobeline has potential as a pharmacotherapy for psychostimulant abuse. Systematic structural modification of the lobeline molecule is described with the aim of improving selectivity and affinity for VMAT2 over neuronal nicotinic acetylcholine receptors and other neurotransmitter transporters. This has led to the discovery of more potent and selective ligands for VMAT2. In addition, a computational neural network analysis of the affinity of these lobeline analogs for VMAT2 has been carried out, which provides computational models that have predictive value in the rational design of VMAT2 ligands and is also useful in identifying drug candidates from virtual libraries for subsequent synthesis and evaluation.

QSAR modelling of integrin antagonists using enhanced Bayesian regularised genetic neural networks

Bayesian regularised genetic neural network (BRGNN) has been used for modelling the inhibition activity of 141 biphenylalanine derivatives as integrin antagonists. Three local pattern search (PS) methods, simulated annealing and threshold acceptance were combined with BRGNN in the form of a hybrid genetic algorithm (HGA). The results obtained revealed that PS is a suitable method for improving the ability of BRGNN to break out from the local minima. The proposed HGA technique is able to retrieve important variables from complex systems and nonlinear search spaces for optimisation. Two models with 8-3-1 artificial neural network (ANN) architectures were developed for describing α₄β₇ and α₄β₁ modulatory activities of integrin antagonists. Monte Carlo cross-validation was performed to validate the models and Q₂ values of 0.75 and 0.74 were obtained for α₄β₇ and α₄β₁ inhibitory activities, respectively. The scrambling technique was used for sensitivity analysis of descriptors appearing in ANN models. Frequencies of repetition and sensitivity analysis of molecular descriptors revealed that 3D-Morse descriptors are influential factors for describing α₄β₇ inhibitory activity, while in the case of α₄β₁ inhibitory activity, the Randic shape index, the lowest eigenvalue of the Burden matrix and the number of rotatable bonds are important parameters.

Sex differences in tolerance to the locomotor depressant effects of lobeline in periadolescent rats

Lobeline is being tested in clinical trials as a pharmacotherapy for methamphetamine abuse and attention deficit hyperactivity disorder. Preclinical research demonstrates that lobeline produces locomotor hypoactivity apart from its therapeutic effects; however, the hypothesis that there are sex differences in hypoactivity or in the development of tolerance to its locomotor depressant effects has not been investigated. Periadolescent rats were injected with saline to determine baseline locomotor activity. Animals received saline or lobeline (1.0-10mg/kg) daily for 7 consecutive days (post natal days 29-35), and were challenged with saline 24h later to assess baseline activity. Lobeline produced hypoactivity in total horizontal activity and center distance travelled. Tolerance developed to the lobeline-induced hypoactivity and sex differences in lobeline tolerance were observed on both measures. Females acquired tolerance to lobeline 5.6 mg/kg at a slower rate than males. Saline challenge revealed a linear dose-dependent trend of hyperactivity on both measures, which indicates that rats exhibited altered locomotor behavior 24h after the final lobeline treatment. These findings demonstrate sex differences in the hypoactive response to lobeline prior to puberty and suggest that females may experience more locomotor depressant effects than males. Chronic lobeline may induce hyperactivity following cessation of treatment.

QSAR study on maximal inhibition (Imax) of quaternary ammonium antagonists for S-(-)-nicotine-evoked dopamine release from dopaminergic nerve terminals in rat striatum

Maximal inhibition (I(max)) of the agonist effect is an important pharmacological property of inhibitors that interact with multiple receptor subtypes that are activated by the same agonist and which elicit the same functional response. This report represents the first QSAR study on a set of 66 mono- and bis-quaternary ammonium salts that act as antagonists at neuronal nicotinic acetylcholine receptors mediating nicotine-evoked dopamine release, conducted using multi-linear regression (MLR) and neural network (NN) analysis with the maximal inhibition (I(max)) values of the antagonists as target values. The statistical results for the generated MLR model were: r(2)=0.89, rmsd=9.01, q(2)=0.83 and loormsd=11.1; the statistical results for the generated NN model were: r(2)=0.89, rmsd=8.98, q(2)=0.83 and loormsd=11.2. The maximal inhibition values of the compounds exhibited a good correlation with the predictions made by the QSAR models developed, which provide a basis for rationalizing selection of compounds for synthesis in the discovery of effective and selective second generation inhibitors of nAChRs mediating nicotine-evoked dopamine release.

Computational neural network analysis of the affinity of N-n-alkylnicotinium salts for the alpha4beta2* nicotinic acetylcholine receptor

Based on an 85 molecule database, linear regression with different size datasets and an artificial neural network approach have been used to build mathematical relationships to fit experimentally obtained affinity values (K(i)) of a series of mono- and bis-quaternary ammonium salts from [(3)H]nicotine binding assays using rat striatal membrane preparations. The fitted results were then used to analyze the pattern among the experimental K(i) values of a set of N-n-alkylnicotinium analogs with increasing n-alkyl chain length from 1 to 20 carbons. The affinity of these N-n-alkylnicotinium compounds was shown to parabolically vary with increasing numbers of carbon atoms in the n-alkyl chain, with a local minimum for the C(4) (n-butyl) analogue. A decrease in K(i) value between C(12) and C(13) was also observed. The statistical results for the best neural network fit of the 85 experimental K(i) values are r(2) = 0.84, rmsd = 0.39; r(cv)(2) = 0.68, and loormsd = 0.56. The generated neural network model with the 85 molecule training set may also be of value for future predictions of K(i) values for new virtual compounds, which can then be identified, subsequently synthesized, and tested experimentally.

Synthesis of (-)-lobeline via enzymatic desymmetrization of lobelanidine

The bioactive alkaloid (-)-lobeline was synthesized via the stereoselective acylation (desymmetrization) of meso-lobelanidine by vinyl acetate in the presence of Candida antarctica lipase B.