3D-QSAR and molecular docking study of LRRK2 kinase inhibitors by CoMFA and CoMSIA methods

3D-QSAR Modeling on 2-Pyrimidine Carbohydrazides as Utrophin Modulators for the Treatment of Duchenne Muscular Dystrophy by Combining CoMFA, CoMSIA, and Molecular Docking Studies

The 3D-QSAR models were developed using CoMFA and CoMSIA techniques to investigate essential molecular fields, optimization strategies, and structure-activity relationships for utrophin-modulating compounds. The data set (71 molecules) was divided into two training and test sets using the hierarchical clustering approach. The training set was aligned based on the most active compound. The built and optimized models based on the PLS approach provided acceptable results. The results were q 2 = 0.528 and r 2 = 0.776 for CoMFA and q 2 = 0.600 and r 2 = 0.811 for CoMSIA models. According to the statistical results, it was found that both the CoMFA models with and without regional focusing and also the CoMSIA model have good estimation ability. Molecular docking was also performed with high-activity compounds (as ligands) and target receptors (protein), and its results, together with the results of 3D-QSAR, give new insights for the design of compounds with higher biological activity. Finally, based on the overall results, the design of new compounds with higher utrophin modulation activity was carried out.

Design of novel DABO derivatives as HIV-1 RT inhibitors using molecular docking, molecular dynamics simulations and ADMET properties

HIV-1 reverse transcriptase is an important target for developing effective anti-HIV-1 inhibitors. Different types of small molecules have been designed based on this target, showing different levels of inhibitory activity against various types of HIV-1 strains. The relationship between structure and activity of DABO derivatives was investigated by means of 3D-QSAR molecular model, molecular docking, molecular dynamics and ADMET properties. The statistical results of molecular models show that the CoMFA and CoMSIA models have good internal stability (CoMFA: q2 = 0.623, r2 = 0.946; CoMSIA: q2 = 0.668, r2 = 0.983) and external prediction ability (CoMFA: rpred2 = 0.961; CoMSIA: rpred2 = 0.961). In addition, molecular docking has explored the mechanism of action between small molecules and receptor proteins, and the results show that hydrogen bonding between amino acid Lys101 and small molecules can improve the affinity of ligands to receptor binding. A total of 12 novel molecules were designed and their activities were predicted based on the 3D-QSAR model and molecular docking results. The results showed that the designed molecules had higher predictive activity. Subsequently, 100 ns MD simulation and binding free energy verified the stability of molecular docking results. Finally, the pharmacokinetic properties of the novel designed molecule were verified by using ADMET to predict its properties. These results can provide reference for the design and development of novel and effective HIV-1 RT inhibitors, and provide new ideas for the design of subsequent drugs.Communicated by Ramaswamy H. Sarma.

Multi-method computational evaluation of the inhibitors against leucine-rich repeat kinase 2 G2019S mutant for Parkinson's disease

Leucine-rich repeat kinase 2 G2019S mutant (LRRK2 G2019S) is a potential target for Parkinson's disease therapy. In this work, the computational evaluation of the LRRK2 G2019S inhibitors was conducted via a combined approach which contains a preliminary screening of a large database of compounds via similarity and pharmacophore, a secondary selection via structure-based affinity prediction and molecular docking, and a rescoring treatment for the final selection. MD simulations and MM/GBSA calculations were performed to check the agreement between different prediction methods for these inhibitors. 331 experimental ligands were collected, and 170 were used to build the structure-activity relationship. Eight representative ligand structural models were employed in similarity searching and pharmacophore screening over 14 million compounds. The process for selecting proper molecular descriptors provides a successful sample which can be used as a general strategy in QSAR modelling. The rescoring used in this work presents an alternative useful treatment for ranking and selection.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease

Parkinson’s disease is one of the most common neurodegenerative illnesses in older persons and the leucine-rich repeat kinase 2 (LRRK2) is an auspicious target for its pharmacological treatment. In this work, quantitative structure–activity relationship (QSAR) models for identification of putative inhibitors of LRRK2 protein are developed by using an in-house chemical library and several machine learning techniques. The methodology applied in this paper has two steps: first, alternative subsets of molecular descriptors useful for characterizing LRRK2 inhibitors are chosen by a multi-objective feature selection method; secondly, QSAR models are learned by using these subsets and three different strategies for supervised learning. The qualities of all these QSAR models are compared by classical metrics and the best models are discussed in statistical and physicochemical terms.

Insight into the structural requirements of pyrimidine-based phosphodiesterase 10A (PDE10A) inhibitors by multiple validated 3D QSAR approaches

Schizophrenia is a complex disorder of thinking and behaviour (0.3-0.7% of the population is affected). The over-expression of phosphodiesterase 10A (PDE10A) enzyme may be a potential target for schizophrenia and Huntington's disease. Because 3D QSAR analysis is one of the most frequently used modelling techniques, in the present study, five different 3D QSAR tools, namely CoMFA, CoMSIA, kNN-MFA, Open3DQSAR and topomer CoMFA methods, were used on a dataset of pyrimidine-based PDE10A inhibitors. All developed models were validated internally and externally. The non-commercial Open3DQSAR produced the best statistical results amongst 3D QSAR tools. The structural interpretations obtained from different methods were thoroughly analysed and were justified on the basis of information obtained from the crystal structure. Information from one method was mostly validated by the results of other methods and vice versa. In the current work, the use of multiple tools in the same analysis revealed more complete information about the structural requirements of these compounds. On the basis of the observations of the 3D QSAR studies, 12 new compounds were designed for better PDE10A inhibitory activity. The current investigation may help in further designing new PDE10A inhibitors with promising activity.