Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods

A machine learning-integrated stepwise method to discover novel anti-obesity phytochemicals that antagonize the glucocorticoid receptor

As a type of stress hormone, glucocorticoids (GCs) affect numerous physiological pathways by binding to the glucocorticoid receptor (GR) and regulating the transcription of various genes. However, when GCs are dysregulated, the resulting hypercortisolism may contribute to various metabolic disorders, including obesity. Thus, attempts have been made to discover potent GR antagonists that can reverse excess-GC-related metabolic diseases. Phytochemicals are a collection of valuable bioactive compounds that are known for their wide variety of chemotypes. Recently, various computational methods have been developed to obtain active phytochemicals that can modulate desired target proteins. In this study, we developed a workflow comprising two consecutive quantitative structure-activity relationship-based machine learning models to discover novel GR-antagonizing phytochemicals. These two models collectively identified 65 phytochemicals that bind to and antagonize GR. Of these, nine commercially available phytochemicals were validated for GR-antagonist and anti-obesity activities. In particular, we confirmed that demethylzeylasteral, a phytochemical of the Tripterygium wilfordii Radix, exhibits potent anti-obesity activity in vitro through GR antagonism.

Predicting mosquito repellents for clothing application from molecular fingerprint-based artificial neural network SAR models

Spraying repellents on clothing limits toxicity and allergy problems that can occur when the repellents are directly applied to skin. This also allows the use of higher doses to ensure longer lasting effects. As the number of repellents available on the market is limited, it is necessary to propose new ones, especially by using in silico methods that reduce costs and time. In this context SAR models were built from a dataset of 2027 chemicals for which repellent activity on clothing was measured against Aedes aegypti. The interest of using either the ECFP or MACCS fingerprints as input neurons of a three-layer perceptron was evaluated. Transformation of MACCS bit strings into disjunctive tables led to interesting results. Models obtained with both types of fingerprints were compared to a model including physicochemical and topological descriptors.

In silico prediction of mosquito repellents for clothing application

Use of protective clothing is a simple and efficient way to reduce the contacts with mosquitoes and consequently the probability of transmission of diseases spread by them. This mechanical barrier can be enhanced by the application of repellents. Unfortunately the number of available repellents is limited. As a result, there is a crucial need to find new active and safer molecules repelling mosquitoes. In this context, a structure-activity relationship (SAR) model was proposed for the design of repellents active on clothing. It was computed from a dataset of 2027 chemicals for which repellent activity on clothing was measured against Aedes aegypti. Molecules were described by means of 20 molecular descriptors encoding physicochemical properties, topological information and structural features. A three-layer perceptron was used as statistical tool. An accuracy of 87% was obtained for both the training and test sets. Most of the wrong predictions can be explained. Avenues for increasing the performances of the model have been proposed.

Ranked binding energies of residues and data fusion to identify the active and selective pyrimidine-based Janus kinases 3 (JAK3) inhibitors

The idea of using ranked binding energies of residues and data fusion are presented here for the first time as a valuable tool to classify active and selective inhibitors. Selective inhibitors of JAK3 can inhibit inflammatory cytokine while preventing targeting other subtypes of JAK1 and JAK2. Herein, we report a novel way to identify both active JAK3 and selective JAK1/JAK3 and JAK2/JAK3 inhibitors using the effective activity and selectivity classifications. The most important residues (top 10) responsible for the inhibition mechanism are sorted from high to low energies, which are considered as variables in the classification process. In addition, the ranked energies of ligands' heteroatoms (top 5), ranked energies of hydrogen bonds (top 5) and important molecular descriptors (top 10) were used to construct different data fusion possibilities. It is shown that the proposed data fusion strategy can increase the accuracy of the activity classification to 100% and the selectivity classification to 96.4%. The proposed strategies represented in this paper can help medicinal or pharmaceutical chemist in evaluation of both active and selective inhibitors before synthesizing new pharmaceuticals.

First molecular modelling report on tri-substituted pyrazolines as phosphodiesterase 5 (PDE5) inhibitors through classical and machine learning based multi-QSAR analysis

Phosphodiesterase 5 (PDE5) falls under a broad category of metallohydrolase enzymes responsible for the catalysis of the phosphodiesterase bond, and thus it can terminate the action of cyclic guanosine monophosphate (cGMP). Overexpression of this enzyme leads to development of a number of pathological conditions. Thus, targeting the enzyme to develop inhibitors could be useful for the treatment of erectile dysfunction as well as pulmonary hypertension. In the current study, several molecular modelling techniques were utilized including Bayesian classification, single tree and forest tree recursive partitioning, and genetic function approximation to identify crucial structural fingerprints important for optimization of tri-substituted pyrazoline derivatives as PDE5 inhibitors. Later, various machine learning models were also developed that could be utilized to predict and screen PDE5 inhibitors in the future.