QSAR model for prediction of the therapeutic potency of N-benzylpiperidine derivatives as AChE inhibitors

Design, synthesis, and pharmacological evaluation of indole-piperidine amides as Blood-brain barrier permeable dual cholinesterase and β-secretase inhibitors

Heterocyclic compounds play a crucial role in the discovery of therapeutics. Alzheimer's disease (AD) is an unfathomable sporadic neurodegenerative disorder that involves multiple pathological pathways. The failure of current single-target small molecules to address AD's underlying causes has prompted interest in discovering multi-target directed ligands (MTDLs) to slow down the disease's progression. Herein we report the synthesis and biological evaluation of indole-piperidine amides as MTDLs for AD. The 5,6-dimethoxy-indole N-(2-(1-benzylpiperidine) carboxamide (23a) inhibits hAChE and hBACE-1 with IC50 values of 0.32 and 0.39 μM, respectively. The MTDL 23a is a mixed-type inhibitor of both hAChE and hBACE-1 with Ki values of 0.26 μM and 0.46 μM, respectively. The MD simulation studies revealed that both AChE and BACE-1 experience minor conformational changes on binding with 23a. In the PAMPA-BBB assay, analog 23a demonstrated CNS permeability, indicating the possibility for future investigation in preclinical models of AD.

A Quantitative Structure-Activity Relationship for Human Plasma Protein Binding: Prediction, Validation and Applicability Domain

Purpose

The purpose of this study was to develop a robust and externally predictive in silico QSAR-neural network model for predicting plasma protein binding of drugs. This model aims to enhance drug discovery processes by reducing the need for chemical synthesis and extensive laboratory testing.

Methods

A dataset of 277 drugs was used to develop the QSAR-neural network model. The model was constructed using a Filter method to select 55 molecular descriptors. The validation set's external accuracy was assessed through the predictive squared correlation coefficient Q2 and the root mean squared error (RMSE).

Results

The developed QSAR-neural network model demonstrated robustness and good applicability domain. The external accuracy of the validation set was high, with a predictive squared correlation coefficient Q2 of 0.966 and a root mean squared error (RMSE) of 0.063. Comparatively, this model outperformed previously published models in the literature.

Conclusion

The study successfully developed an advanced QSAR-neural network model capable of predicting plasma protein binding in human plasma for a diverse set of 277 drugs. This model's accuracy and robustness make it a valuable tool in drug discovery, potentially reducing the need for resource-intensive chemical synthesis and laboratory testing.

Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease

Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and β-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.

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.

Computational Studies on Acetylcholinesterase Inhibitors: From Biochemistry to Chemistry

Acetylcholinesterase inhibitors are the most promising therapeutics for Alzheimer's disease treatment as these prevent the loss of acetylcholine and slows the progression of the disease. The drugs approved for the management of Alzheimer's disease by the FDA are acetylcholinesterase inhibitors but are associated with side effects. Consistent and stringent efforts by the researchers with the help of computational methods opened new ways of developing novel molecules with good acetylcholinesterase inhibitory activity. In this manuscript, we reviewed the studies that identified the essential structural features of acetylcholinesterase inhibitors at the molecular level as well as the techniques like molecular docking, molecular dynamics, quantitative structure-activity relationship, virtual screening, and pharmacophore modelling that were used in designing these inhibitors.

2D QSAR studies on a series of (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one as CETP inhibitors

Cardiovascular disease (CVD) is one of the major causes of human death. Preliminary evidence indicates that the inhibition treatment of Cholesteryl Ester Transfer Protein (CETP) causes the most pronounced increase in HDL cholesterol reported so far. Merck has disclosed certain (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one derivatives, which show potent CETP inhibitory activity. Therefore, it would be desirable to develop computational models to facilitate the screening of these inhibitors. In the present work, quantitative structure-activity relationship (QSAR) models have been developed to predict the therapeutic potency of 108 derivatives of (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one: Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Feedforward Neural Network using Particle Swarm Optimization (FNN-PSO). Six descriptors were selected using genetic algorithms, whereas, internal and external validation of the models was performed according to all available validation strategies. It was shown that CETP inhibitory activity is mainly governed by electronegativity, the structure of the molecule, and the electronic properties. The best results were obtained with the SVR model. The results obtained may assist in the design of new CETP inhibitors.

QSAR and molecular docking studies of lethal factor protease inhibitors against Bacillus anthracis

Bacillus anthracis is considered as a biological warfare agent because it is the causative agent of the serious infectious anthrax disease. Delay in treatment leads to lethal factor-mediated toxaemia which is very critical due to lack of therapeutic options. Consequently, attempts have been made to discover potent lethal factor (LF) protease inhibitors such as small-molecule synthetic 2-thio-1,3-thiazolidine-4-one (rhodanine) compounds. But computed descriptor-based quantitative structure-activity relationship (QSAR) and drug design studies on such aspect are poorly represented. Therefore, an attempt was made for developing QSAR models using structural descriptors for 1,3-thiazolidine-4-one compounds. The models were developed on a series of 49 LF protease inhibitors using the combination of constitutional, functional group, atom-centred fragment and molecular property descriptors. The best QSAR model included four variables, namely, C-040, nR05, GVWAI-80 and ALOGP that correlated well with the anti-LF protease activity with a good correlation coefficient (r = 0.870) of good statistical significance (F_{4, 29} = 14.09 (α = 0.001) F_{4, 29} = 6.19). This model was also validated and explained 58.1% of variances of the Bacillus anthracis inhibitory activities of the studied compounds with r²_pred = 0.710 which denotes external predictability. Finally, molecular docking was carried out to predict the mode of binding of some highly active congeneric compounds. It was shown that VAL 1403 is an important residue for phenyl ring. TYR 1456 and HIS 1418 are responsible for interaction with the rhodanine nucleus. Therefore, these residues are considered responsible for the inhibition of LF protease anthrax and can predict significant dimension of essential structural features of these inhibitors to evaluate, screen and help priorities of the synthesis of the candidates against anthrax bioterrorism.

Lipophilicity in drug design: an overview of lipophilicity descriptors in 3D-QSAR studies

The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host-guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modeling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure-activity relationships studies.

Prediction of therapeutic potency of tacrine derivatives as BuChE inhibitors from quantitative structure-activity relationship modelling

Numerous studies show that tacrine derivatives exhibit increased inhibitory activity against butyrylcholinesterase (BuChE) and acetylcholinesterase (AChE). However, the screening assays for currently available BuChE inhibitors are expensive, time consuming and dependent on the inhibitory compound. It is therefore desirable to develop alternative methods to facilitate the screening of these derivatives in the early phase of drug discovery. In order to develop robust predictive models, three regression methods were chosen in this study: multiple linear regression (MLR), support vector regression (SVR) and multilayer perceptron network (MLP). Eight relevant descriptors were selected on a dataset of 151 molecules using a method based on genetic algorithms. Internal and external validation strategies play an important role. Also, to check the robustness of the selected models, all available validation strategies were used, and all criteria used to validate these models revealed the superiority of the SVR model. The statistical parameters obtained with the SVR model were RMSE = 0.197, r² = 0.969 and Q² = 0.964 for the training set, and r² = 0.906 and Q² = 0.891 for the test set. Therefore, the model developed in this study provides an excellent prediction of the inhibitory concentration of tacrine derivatives.

QSAR modeling in ecotoxicological risk assessment: application to the prediction of acute contact toxicity of pesticides on bees (Apis mellifera L.)

Despite their indisputable importance around the world, the pesticides can be dangerous for a range of species of ecological importance such as honeybees (Apis mellifera L.). Thus, a particular attention should be paid to their protection, not only for their ecological importance by contributing to the maintenance of wild plant diversity, but also for their economic value as honey producers and crop-pollinating agents. For all these reasons, the environmental protection requires the resort of risk assessment of pesticides. The goal of this work was therefore to develop a validated QSAR model to predict contact acute toxicity (LD₅₀) of 111 pesticides to bees because the QSAR models devoted to this species are very scarce. The analysis of the statistical parameters of this model and those published in the literature shows that our model is more efficient. The QSAR model was assessed according to the OECD principles for the validation of QSAR models. The calculated values for the internal and external validation statistic parameters (Q ² and [Formula: see text] are greater than 0.85. In addition to this validation, a mathematical equation derived from the ANN model was used to predict the LD₅₀ of 20 other pesticides. A good correlation between predicted and experimental values was found (R ² = 0.97 and RMSE = 0.14). As a result, this equation could be a means of predicting the toxicity of new pesticides.