Molecular docking and 4D-QSAR studies of metastatic cancer inhibitor thiazoles

Clustering of atoms relative to vector space in the Z-matrix coordinate system and 'graphical fingerprint' analysis of 3D pharmacophore structure

The behavior of a molecule within its environment is governed by chemical fields present in 3D space. However, beyond local descriptors in 3D, the conformations a molecule assumes, and the resulting clusters also play a role in influencing structure-activity models. This study focuses on the clustering of atoms according to the vector space of four atoms aligned in the Z-Matrix Reference system for molecular similarity. Using 3D-QSAR analysis, it was aimed to determine the pharmacophore groups as interaction points in the binding region of the β2-adrenoceptor target of fenoterol stereoisomers. Different types of local reactive descriptors of ligands have been used to elucidate points of interaction with the target. Activity values for ligand-receptor interaction energy were determined using the Levenberg-Marquardt algorithm. Using the Molecular Comparative Electron Topology method, the 3D pharmacophore model (3D-PhaM) was obtained after aligning and superimposing the molecules and was further validated by the molecular docking method. Best guesses were calculated with a non-output validation (LOO-CV) method. Finally, the data were calculated using the 'graphic fingerprint' technique. Based on the eLKlopman (Electrostatic LUMO Klopman) descriptor, the Q2 value of this derivative set was calculated as 0.981 and the R2ext value is calculated as 0.998.

Comparison of various methods for validity evaluation of QSAR models

BACKGROUND

Quantitative structure-activity relationship (QSAR) modeling is one of the most important computational tools employed in drug discovery and development. The external validation of QSAR models is the main point to check the reliability of developed models for the prediction activity of not yet synthesized compounds. It was performed by different criteria in the literature.

METHODS

In this study, 44 reported QSAR models for biologically active compounds reported in scientific papers were collected. Various statistical parameters of external validation of a QSAR model were calculated, and the results were discussed.

RESULTS

The findings revealed that employing the coefficient of determination (r²) alone could not indicate the validity of a QSAR model. The established criteria for external validation have some advantages and disadvantages which should be considered in QSAR studies.

CONCLUSION

This study showed that these methods alone are not only enough to indicate the validity/invalidity of a QSAR model.

The Current Perspectives in Clinical Research: Computer-Assisted Drug Designing, Ethics, and Good Clinical Practice

In the era of emerging microbial and non-communicable diseases and re-emerging microbial infections, the medical fraternity and the public are plagued by under-preparedness. It is evident by the severity of the Coronavirus disease (COVID-19) pandemic that novel microbial diseases are a challenge and are challenging to control. This is mainly attributed to the lack of complete knowledge of the novel microbe’s biology and pathogenesis and the unavailability of therapeutic drugs and vaccines to treat and control the disease. Clinical research is the only answer utilizing which can handle most of these circumstances. In this review, we highlight the importance of computer-assisted drug designing (CADD) and the aspects of molecular docking, molecular superimposition, 3D-pharmacophore technology, ethics, and good clinical practice (GCP) for the development of therapeutic drugs, devices, and vaccines.

Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

Molecular modeling of a series of dehydroquinate dehydratase type II inhibitors of Mycobacterium tuberculosis and design of new binders

Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), is still responsible for a large number of fatal cases, especially in developing countries with alarming rates of incidence and prevalence worldwide. Mycobacterium tuberculosis has a remarkable ability to develop new resistance mechanisms to the conventional antimicrobials treatment. Because of this, there is an urgent need for novel bioactive compounds for its treatment. The dehydroquinate dehydratase II (DHQase II) is considered a key enzyme of shikimate pathway, and it can be used as a promising target for the design of new bioactive compounds with antibacterial action. The aim of this work was the construction of QSAR models to aid the design of new potential DHQase II inhibitors. For that purpose, various molecular modeling approaches, such as activity cliff, QSAR models and computer-aided ligand design were utilized. A predictive in silico 4D-QSAR model was built using a database comprising 86 inhibitors of DHQase II, and the model was used to predict the activity of the designed ligands. The obtained model proved to predict well the DHQase II inhibition for an external validation dataset ([Formula: see text] = 0.72). Also, the Activity Cliff analysis shed light on important structural features applied to the ligand design.

Investigation of 3D pharmacophore of N-benzyl benzamide molecules of melanogenesis inhibitors using a new descriptor Klopman index: uncertainties in model

We used a new descriptor called the Klopman index in our software of the "molecular comparative electron topology" (MCET) method to reduce the uncertainty resulting from the descriptors used in QSAR studies. The 3D pharmacophore model (3D-PhaM), which can demonstrate three-dimensional interaction between the ligand -receptor (L-R), is only possible with local reactive descriptors (LRD). The Klopman index, containing both Coulombic and frontier orbital and interactions of atoms of the ligand, is a good LRD. Molecular conformers having the best matching atoms with the template conformer can be selected as one of the most suitable spatial structures for interaction with the receptor, and the LRD values of the atoms in this conformer serve to determine 3D-PhaM. The 3D-PhaM of the N-benzyl benzamide derivatives, such as the melanogenesis inhibitor, was determined by ligand-based MCET and confirmed by the structure-based FlexX docking method. For compounds of the training set (42) and the external cross validation test set (6), the Q² (0.862) and R² (0.913) of the statistical parameters were calculated, respectively, and were checked by r_m² (0.85) of the stringent validation.