Ligand-based pharmacophore modelling, in silico virtual screening, molecular docking and molecular dynamic simulation study to identify novel Francisella tularensis ParE inhibitors

Leveraging decagonal in-silico strategies for uncovering IL-6 inhibitors with precision

Interleukin-6 upregulation leads to various acute phase reactions such as local inflammation and systemic inflammation in many diseases like cancer, multiple sclerosis, rheumatoid arthritis, anemia, and Alzheimer's disease stimulating JAK/STAT3, Ras/MAPK, PI3K-PKB/Akt pathogenic pathways. Since no small molecules are available in the market against IL-6 till now, we have designed a class of small bioactive 1,3 - indanedione (IDC) molecules for inhibiting IL-6 using a decagonal approach computational studies. The IL-6 mutations were mapped in the IL-6 protein (PDB ID: 1ALU) from thorough pharmacogenomic and proteomics studies. The protein-drug interaction networking analysis for 2637 FFDA-approved drugs with IL-6 protein using Cytoscape software showed that 14 drugs have prominent interactions with IL-6. Molecular docking studies showed that the designed compound IDC-24 (-11.8 kcal/mol) and methotrexate (-5.20) bound most strongly to the 1ALU south asian population mutated protein. MMGBSA results indicated that IDC-24 (-41.78 kcal/mol) and methotrexate (-36.81 kcal/mol) had the highest binding energy when compared to the standard molecules LMT-28 (-35.87 kcal/mol) and MDL-A (-26.18 kcal/mol). These results we substantiated by the molecular dynamic studies in which the compound IDC-24 and the methotrexate had the highest stability. Further, the MMPBSA computations produced energies of -28 kcal/mol and -14.69 kcal/mol for IDC-24 and LMT-28. KDeep absolute binding affinity computations revealed energies of -5.81 kcal/mol and -4.74 kcal/mol for IDC-24 and LMT-28 respectively. Finally, our decagonal approach established the compound IDC-24 from the designed 1,3-indanedione library and methotrexate from protein drug interaction networking as suitable HITs against IL-6.

Synthesis and screening of cyclic diketone indanedione derivatives as future scaffolds for neutrophil elastase inhibition

Human neutrophil elastase (HNE) and proteinase 3 (Pr3) released from neutrophils at inflammatory sites are the major causes of pathogens in chronic obstructive pulmonary disease (COPD) and various lung tissue derangements, among which cystic fibrosis and blockade of airway passages are chronic. These proteolytic mediatory agents combined with induced oxidative reactions sustain pathogenicity. Cyclic diketone indane-1,3-dione derivatives were designed, and toxicity evaluation predictions were performed in silico. Benzimidazole and hydrazide derivatives of indanedione were synthesized and characterized. Synthesized compounds were run using neutrophil elastase inhibition assay protocols. The compounds exhibit considerable inhibition of neutrophil elastase enzymes.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Ligand-based pharmacophore modeling and molecular dynamic simulation approaches to identify putative MMP-9 inhibitors

MMP-9 is a calcium-dependent zinc endopeptidase that plays a crucial role in various diseases and is a ubiquitous target for many classes of drugs. The availability of MMP-9 crystal structure in combination with aryl sulfonamide anthranilate hydroxamate inhibitor facilitates to accentuate the computer-aided screening of MMP-9 inhibitors with the presumed binding mode. In the current study, ligand-based pharmacophore modeling and 3D-QSAR analysis were performed using 67 reported MMP-9 inhibitors possessing pIC50 in the range of 5.221 to 9.000. The established five-point hypothesis model DDHRR_1 was statistically validated using various parameters R 2 (0.9076), Q 2 (0.8170), and F value (83.5) at a partial least square of four. Hypothesis validation and enrichment analysis were performed for the generated hypothesis. Further, Y-scrambling and Xternal validation using mean-absolute error-based criteria were performed to evaluate the reliability of the model. Docking in the XP mode and binding free energy was calculated for 67 selected ligands to explore the key binding interactions and binding affinity against the MMP-9 enzyme. Additionally, high-throughput virtual screening was carried out for 2.3 million chemical molecules to explore the potential virtual hits, and their predicted activity was calculated. Thus, the results obtained aid in developing novel MMP-9 inhibitors with significant activity and binding affinity.

Preliminary modelling as the first stage of targeted organic synthesis

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.