Structural and Functional Diversities of the Hexadecahydro-1H-cyclopenta[a]phenanthrene Framework, a Ubiquitous Scaffold in Steroidal Hormones

The Preventive and Therapeutic Potential of the Flavonoids in Liver Cirrhosis: Current and Future Perspectives

Non-alcoholic fatty liver disease (NAFLD) may vary from moderately mild non-alcohol fatty liver (NAFL) towards the malignant variant known as non-alcoholic steatohepatitis (NASH), which is marked by fatty liver inflammation and may progress to liver cirrhosis (LC), liver cancer, fibrosis, or liver failure. Flavonoids can protect the liver from toxins through their anti-inflammatory, antioxidant, anti-cancer, and antifibrogenic pharmacological activities. Furthermore, flavonoids protect against LC by regulation of hepatic stellate cells (HSCs) trans-differentiation, inhibiting growth factors like TGF-β and platelets-derived growth factor (PDGF), vascular epithelial growth factor (VEGF), viral infections like hepatitis-B, C and D viruses (HBV, HCV & HDV), autoimmune-induced, alcohol-induced, metabolic disorder-induced, causing by apoptosis, and regulating MAPK pathways. These flavonoids may be explored in the future as a therapeutic solution for hepatic diseases.

Towards systematic exploration of chemical space: building the fragment library module in molecular property diagnostic suite

A fragment-based drug discovery (FBDD) approach has traditionally been of utmost significance in drug design studies. It allows the exploration of large chemical space to find novel scaffolds and chemotypes which can be improved into selective inhibitors with good affinity. In the current work, several public domain chemical libraries (ChEMBL, DrugCentral, PDB ligands, COCONUT, and SAVI) comprising bioactive and virtual molecules were retrieved to develop a fragment library. A systematic fragmentation method that breaks a given molecule into rings, linkers, and substituents was used to cleave the molecules and the fragments were analyzed. Further, only the ring framework was taken into the consideration to develop a fragment library that consists of a total number of 107,614 unique fragments. This set represents a rich diverse structure framework that covers a wide variety of yet-to-be-explored fragments for a wide range of small molecule-based applications. This fragment library is an integral part of the molecular property diagnostic suite (MPDS) suite that can be used with other modeling and informatics methods for FBDD approaches. The fragment library module of MPDS can be accessed at http://mpds.neist.res.in:8085.

Virtual repurposing of ursodeoxycholate and chenodeoxycholate as lead candidates against SARS-Cov2-Envelope protein: A molecular dynamics investigation

Drug repurposing is an apt choice to combat the currently prevailing global threat of COVID-19, caused by SARS-Cov2in absence of any specific medication/vaccine. The present work employs state of art computational methods like homology modelling, molecular docking and molecular dynamics simulations to evaluate the potential of two widely used surfactant drugs namely chenodeoxycholate(CDC) and ursodeoxycholate (UDC), to bind to the envelope protein of SARS-Cov2(SARS-Cov2-E).The monomeric unit of SARS-Cov2-E was modelled from a close homologue (>90% sequence identity) and a pentameric assembly was modelled using symmetric docking, followed by energy minimization in a DPPC membrane environment. The minimized structure was used to generate best scoring SARS-Cov2-E-CDC/UDC complexes through blind docking. These complexes were subjected to 230 ns molecular dynamics simulations in triplicates in a DPPC membrane environment. Comparative analyses of structural properties and molecular interaction profiles from the MD trajectories revealed that, both CDC and UDC could stably bind to SARS-Cov2-E through H-bonds, water-bridges and hydrophobic contacts with the transmembrane-channelresidues.T30 was observed to be a key residue for CDC/UDC binding. CDC/UDC binding affected the H-bonding pattern between adjacent monomeric chains, slackening the compact transmembrane region of SARS-Cov2-E. Additionally, the polar functional groups of CDC/UDC facilitated entry of a large number of water molecules into the channel. These observations suggest CDC/UDC as potential candidates to hinder the survival of SARS-Cov2 by disrupting the structure of SARS-Cov2-E and facilitating the entry of solvents/polar inhibitors inside the viral cell.Communicated by Ramaswamy H. Sarma.

Structure-based drug repurposing: traditional and advanced AI/ML-aided methods

The current global health emergency in the form of the Coronavirus 2019 (COVID-19) pandemic has highlighted the need for fast, accurate, and efficient drug discovery pipelines. Traditional drug discovery projects relying on in vitro high-throughput screening (HTS) involve large investments and sophisticated experimental set-ups, affordable only to big biopharmaceutical companies. In this scenario, application of efficient state-of-the-art computational methods and modern artificial intelligence (AI)-based algorithms for rapid screening of repurposable chemical space [approved drugs and natural products (NPs) with proven pharmacokinetic profiles] to identify the initial leads is a powerful option to save resources and time. Structure-based drug repurposing is a popular in silico repurposing approach. In this review, we discuss traditional and modern AI-based computational methods and tools applied at various stages for structure-based drug discovery (SBDD) pipelines. Additionally, we highlight the role of generative models in generating molecules with scaffolds from repurposable chemical space. Teaser: This review highlights the importance of repurposable chemical space, and the contributions of conventional in silico approaches and modern machine-learning algorithms for rapid structure-based drug repurposing.

Hybrid Dynamic Pharmacophore Models as Effective Tools to Identify Novel Chemotypes for Anti-TB Inhibitor Design: A Case Study With Mtb-DapB

Antimicrobial resistance (AMR) is one of the most serious global public health threats as it compromises the successful treatment of deadly infectious diseases like tuberculosis. New therapeutics are constantly needed but it takes a long time and is expensive to explore new biochemical space. One way to address this issue is to repurpose the validated targets and identify novel chemotypes that can simultaneously bind to multiple binding pockets of these targets as a new lead generation strategy. This study reports such a strategy, dynamic hybrid pharmacophore model (DHPM), which represents the combined interaction features of different binding pockets contrary to the conventional approaches, where pharmacophore models are generated from single binding sites. We have considered Mtb-DapB, a validated mycobacterial drug target, as our model system to explore the effectiveness of DHPMs to screen novel unexplored compounds. Mtb-DapB has a cofactor binding site (CBS) and an adjacent substrate binding site (SBS). Four different model systems of Mtb-DapB were designed where, either NADPH/NADH occupies CBS in presence/absence of an inhibitor 2, 6-PDC in the adjacent SBS. Two more model systems were designed, where 2, 6-PDC was linked to NADPH and NADH to form hybrid molecules. The six model systems were subjected to 200 ns molecular dynamics simulations and trajectories were analyzed to identify stable ligand-receptor interaction features. Based on these interactions, conventional pharmacophore models (CPM) were generated from the individual binding sites while DHPMs were created from hybrid-molecules occupying both binding sites. A huge library of 1,563,764 publicly available molecules were screened by CPMs and DHPMs. The screened hits obtained from both types of models were compared based on their Hashed binary molecular fingerprints and 4-point pharmacophore fingerprints using Tanimoto, Cosine, Dice and Tversky similarity matrices. Molecules screened by DHPM exhibited significant structural diversity, better binding strength and drug like properties as compared to the compounds screened by CPMs indicating the efficiency of DHPM to explore new chemical space for anti-TB drug discovery. The idea of DHPM can be applied for a wide range of mycobacterial or other pathogen targets to venture into unexplored chemical space.

Modeling the permeability of drug-like molecules through the cell wall of Mycobacterium tuberculosis: an analogue based approach

The emergence of drug resistant strains of Mycobacterium Tuberculosis (Mtb) accentuates the urgent need for the development of novel antitubercular drugs. The major causes of drug resistance are genetic mutations, the influx-efflux transporter system, and the complex cell wall system of Mtb, which can function as permeability barriers. The driving force for permeability of small molecules through a biological system depends on various physicochemical factors. To understand the permeability of small molecules and subsequent cell inhibition, we have developed predictive QSAR models based on reported enzyme-based (IC₅₀) and cell-based (MIC) Mtb inhibitory data. The compounds that are highly active in enzyme-based assays and have significant variation in cell-based assays are assumed to possess the required permeability through the Mtb cell wall. The obtained models suggest the importance of molecular connectivity, lipophilicity (log P, size, shape), electrotopology (relative atomic mass, polarizability, electronegativity, ionization potential, atomic charges, van der Waals volume, hybridization, hydrogen bond acceptors/donors, number of fused rings) and functional groups (hydroxyl groups, primary and secondary amines) of a molecule in determining both its inhibitory potency and Mtb cell permeability. The models were validated with known Mtb inhibitors (9804) collected from the ChEMBL database, Mtb drugs (27) and clinical candidates (5). Further, these validated models were used to screen and prioritize a large database of compounds, including Zinc (152 128), Asinex (435 215), DrugBank (6531) and antimicrobial compounds (1324). The results obtained from 2D-QSAR analysis could improve our understanding towards Mtb cell permeability, which may aid in the rational design of novel potent molecules for tuberculosis (TB).