Chemometric descriptors in modeling the carbonic anhydrase inhibition activity of sulfonamide and sulfamate derivatives

A Combined QSAR and Molecular Docking Approach for Identifying Pyrimidine Derivatives as Penicillin Binding Protein Inhibitors

Background:

Antimicrobial resistance has been rising continuously in the past few years due to the overuse and exploitation of existing antimicrobials. This has motivated the search for a novel scaffold that has the capability of rapid antimicrobial action. The hybridized pyrimidines have attracted us due to their widespread biological activities, such as anti-bacterial and antifungal activities.

Objective:

The present study incorporates a series of pyrimidine-based antimicrobial agents for the 2D quantitative structure-activity relationship analysis (2D QSAR) and docking analysis.

Methods:

The exploration of the chemical structures in combination with the biological activity in CPMLR led to the detection of six descriptors (Constitutional descriptors, Topological descriptors, Modified Burden Eigenvalues and 2D autocorrelations) for modeling the activity. The resulted QSAR model has been validated using combinatorial protocol in multiple linear regression (CP-MLR) and partial least squares (PLS) analysis.

Methods:

The exploration of the chemical structures in combination with the biological activity in CPMLR led to the detection of six descriptors (Constitutional descriptors, Topological descriptors, Modified Burden Eigenvalues and 2D autocorrelations) for modeling the activity. The resulted QSAR model has been validated using a combinatorial protocol in multiple linear regression (CP-MLR) and partial least squares (PLS) analysis.

Results:

The best QSAR model displays the r2 t value of 0.594, Q2 LOO value of 0.779, Q2 L5O value of 0.767. Further docking study was executed using Autodock Vina against Penicillin-binding protein (PBP2a).

Conclusion:

From the results, Compounds 4, 11and 24 were found to possess a good binding affinity towards PBP2a.

Ontological model of multi-agent Smart-system for predicting drug properties based on modified algorithms of artificial immune systems

BACKGROUND

Currently, due to the huge progress in the field of information technologies and computer equipment, it is important to use modern approaches of artificial intelligence in order to process extensive chemical information at creating new drugs with desired properties. The interdisciplinary of research creates additional difficulties in creating new drugs. Currently, there are no universal algorithms and software for predicting the "structure-property" dependence of drug compounds that can take into account the needs of specialists in this field. In this regard, the development of a modern Smart-system based on the promising bio-inspired approach of artificial immune systems for predicting the structure-property dependence of drug compounds is relevant. The aim of this work is to develop a multi-agent Smart-system for predicting the "structure-property" dependence of drug compounds using the ontological approach and modified algorithms of artificial immune systems using the example of drug compounds of the sulfonamide group. The proposed system makes it possible to increase the accuracy of prediction models of the "structure-property" dependence, to reduce the time and financial costs for obtaining candidate drug compounds.

METHODS

During the creation of a Smart-system, there are used multi-agent and ontological approaches, which allow to structure input and output data, optimally to distribute computing resources and to coordinate the work of the system. As a promising approach for processing a large amount of chemical information, extracting informative descriptors and for the creation of an optimal data set, as well as further predicting the properties of medicinal compounds, there are considered modified algorithms of artificial immune systems and various algorithms of artificial intelligence.

RESULTS

There was developed an ontological model of a multi-agent Smart-system. There are presented the results of the «structure-property» dependence simulation based on a modified grey wolf optimization algorithm and artificial immune systems. During the simulation, there was used information from the Mol-Instincts sulfonamide descriptor database.

CONCLUSION

The developed multi-agent Smart-system using ontological models allows visually to present the structure and interrelationships of agents functioning, which greatly facilitates the development of software and reduces time and financial costs during the development of new drugs.

Molecular Descriptors in Modelling the Tumour Necrosis Factor-α Converting Enzyme Inhibition Activity of Novel Tartrate-Based Analogues

The tumour necrosis factor-α converting enzyme inhibition activity of a series comprising of novel tartrate-based analogues has been quantitatively analysed in terms of molecular descriptors. The statistically validated quantitative structure-activity relationship models provided rationales to explain the inhibition activity of these congeners. The descriptors identified through combinatorial protocol in multiple linear regression analysis have highlighted the role of Moran autocorrelation of lag 7, weighted by atomic van der Waals volume, presence of both prime and nonprime amide carbonyl oxygen in the tartrate moiety and occurrence of five membered ring bearing substituents at varying sites. A few potential novel tartrate-based analogues have been suggested for further investigation.

Combining QSAR classification models for predictive modeling of human monoamine oxidase inhibitors

Due to their role in the metabolism of monoamine neurotransmitters, MAO-A and MAO-B present a significant pharmacological interest. For instance the inhibitors of human MAO-B are considered useful tools for the treatment of Parkinson Disease. Therefore, the rational design and synthesis of new MAOs inhibitors is considered of great importance for the development of new and more effective treatments of Parkinson Disease. In this work, Quantitative Structure Activity Relationships (QSAR) has been developed to predict the human MAO inhibitory activity and selectivity. The first step was the selection of a suitable dataset of heterocyclic compounds that include chromones, coumarins, chalcones, thiazolylhydrazones, etc. These compounds were previously synthesized in one of our laboratories, or elsewhere, and their activities measured by the same assays and for the same laboratory staff. Applying linear discriminant analysis to data derived from a variety of molecular representations and feature selection algorithms, reliable QSAR models were built which could be used to predict for test compounds the inhibitory activity and selectivity toward human MAO. This work also showed how several QSAR models can be combined to make better predictions. The final models exhibit significant statistics, interpretability, as well as displaying predictive power on an external validation set made up of chromone derivatives with unknown activity (that are being reported here for first time) synthesized by our group, and coumarins recently reported in the literature.

Modelling of serotonin reuptake inhibitory and histamine H₃antagonistic activity of piperazine and diazepane amides: QSAR rationales for co-optimization of the activity profiles

Selective human serotonin reuptake transporter (hSERT) inhibition is the first line of treatment to deal with the depression. In clinical practice for managing depression, the stimulants are co-prescribed to overcome cognitive impairment and fatigue. Recently, histamine H(3) antagonists with serotonin reuptake inhibition activity have been proposed as alternative approach for the treatment of depression. In this context, a QSAR study of hSERT inhibitory and H(3) antagonistic activity of piperazine and diazepane amide derivatives has been carried out using the combinatorial protocol in multiple linear regression (CP-MLR) with 0D- to 2D-Dragon descriptors. The derived QSAR models have provided a rational approach for the development of new piperazine and diazepane amide derivatives as hSERT inhibitors and H(3) antagonists. In a concomitant partial least-squares (PLS) analysis of the hSERT and histamine H(3) activities, the fraction contributions of identified descriptors revealed their importance in modulating these activities. The PLS analysis of other biological endpoints, namely hNET, hDAT, and histamine H(3) activity in functional assay (H(3)pA(2)) of these analogues with the identified descriptors has further highlighted their scope in modulating these activities.