Applications of density functional theory in COVID-19 drug modeling

Estrogen receptor alpha (ER-α) antagonistic activity of phytoconstituents from Potentilla atrosanguinea and Potentilla fulgens in breast cancer

The Potentilla genus has long been used traditionally as food and a folklore medicine. In the present study, aerial parts of two Potentilla species, Potentilla fulgens and Potentilla atrosanguinea, of western Himalayan origin, were studied for their anti-breast cancer activity. Ethyl acetate (PAA-EA, PFA-EA), methanolic (PAA-ME, PFA-ME) and hydro-methanolic extract (PAA-HM, PFA-HM) of the plants were tested for their antiproliferative activities against MCF-7 and T-47D breast cancer cell lines. The extracts showed good antiproliferative activity against ER-α dominant breast cancer cell line T-47D, having IC50 values 6.19 ± 0.01 to 33.23 ± 0.04 μg/ml. Eight compounds were isolated, characterized, and quantified from ethyl acetate and methanolic extracts by column chromatography, 1D, 2D-NMR, HRMS and TLC densitometric analysis. Two compounds (4 and 6) have shown better antiproliferative activity than standard bazedoxifene and were further evaluated for their ER-α binding affinity via-fluorescence polarization-based competitive binding assay. The antiestrogenic properties of both compounds were assessed using western blotting. Compounds 4 and 6 were found to have significant affinity for the ER-α and managed to decrease its expression by 38 and 54% respectively. Compounds 4 and 6 also had good stability and reactivity as measured by minimal fluctuations in molecular dynamic simulation analysis, a good dock score in molecular docking, and a respectable HOMO-LUMO energy gap in DFT calculations. Compounds 4 and 6 have shown reliable results and can be used in the development of natural product-based anti-breast cancer agents.

Computational discovery of AKT serine/threonine kinase 1 inhibitors through shape screening for rheumatoid arthritis intervention

Rheumatoid Arthritis (RA) is a chronic, symmetrical inflammatory autoimmune disorder characterized by painful, swollen synovitis and joint erosions, which can cause damage to bone and cartilage and be associated with progressive disability. Despite expanded treatment options, some patients still experience inadequate response or intolerable adverse effects. Consequently, the treatment options for RA remain quite limited. The enzyme AKT1 is crucial in designing drugs for various human diseases, supporting cellular functions like proliferation, survival, metabolism, and angiogenesis in both normal and malignant cells. Therefore, AKT serine/threonine kinase 1 is considered crucial for targeting therapeutic strategies aimed at mitigating RA mechanisms. In this context, directing efforts toward AKT1 represents an innovative approach to developing new anti-arthritis medications. The primary objective of this research is to prioritize AKT1 inhibitors using computational techniques such as molecular modeling and dynamics simulation (MDS) and shape-based virtual screening (SBVS). A combined SBVS approach was employed to predict potent inhibitors against AKT1 by screening a pool of compounds sourced from the ChemDiv and IMPPAT databases. From the SBVS results, only the top three compounds, ChemDiv_7266, ChemDiv_2796, and ChemDiv_9468, were subjected to stability analysis based on their high binding affinity and favorable ADME/Tox properties. The SBVS findings have revealed that critical residues, including Glu17, Gly37, Glu85, and Arg273, significantly contribute to the successful binding of the highest-ranked lead compounds at the active site of AKT1. This insight helps to understand the specific binding mechanism of these leads in inhibiting RA, facilitating the rational design of more effective therapeutic agents.

Elucidating the structural basis for the enhanced antifungal activity of amide derivative against Candida albicans: a comprehensive computational investigation

The continuous search for more effective options against well-known pathogens such as Candida albicans remains the rationale for the search for novel lead compounds from various sources. This study aims to investigate the chemical structure, chemical properties, of 5-(2-((5-(((1S,3R) -3-(5-acetamido-1,3,4-thiadiazolidin-2-yl) cyclopentyl) methyl)-1,3,4-thiadiazolidin-2-yl)amino)-2-oxoethyl)-2-methyl-2,3-dihydro-1H-pyrazol-3-ide designated ATCTP using DFT method ωB97XD/-311 +  + g(2d, 2p) and the biological potential of compound ATCTP against Candida albicans using molecular docking and ADMET studies. Geometry optimization was carried out in DMSO, ethanol. gas and water revealing minute discrepancies in bond length and wider differences in bond angles. Frontier molecular orbital investigations reveal HOMO-LUMO energy gap magnitude in decreasing order of ATCTP_Gas > ATCTP_Water > ATCTP_ethanol > ATCTP_DMSO inferring that water influences chemical stability of the compound the most compared to ethanol and DMSO. Density of state investigations have revealed electron density contributions at corresponding energy peaks. In silico pharmacokinetic predicts ATCTP not to be cytotoxic, hepatotoxic, immunotoxic or mutagenic but probable mutagen. Molecular docking investigation of ATCTP against aspartic proteinase of Candida albicans (ID: 2QZX) in comparison with standard drug Fluconazole. Compound ATCTP had higher binding affinity (- 8.1 kcal/mol) compared to that of the standard drug fluconazole (- 5.6 kcal/mol) which records 4 conventional hydrogen interactions compared to 2 formed in the interaction of ATCTP + 2QZX. ATCTP also reports binding affinity of - 7.2 kcal/mol which reportedly surpassed that of 2QZX interaction with fluconazole (- 5.7 kcal/mol). ATCTP binds with lanosterol14-α-demethylase (5v5z) with binding affinity of - 9.7 kcal/mol binding to active site amino acid residues of the protein compared to fluconazole + 5v5z (- 8.0 kcal/mol). ATCTP is therefore recommended to be a lead compound for the possible design of a new and more effective anti-candida therapeutic compound.

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Antiangiogenic potential of phytochemicals from Clerodendrum inerme (L.) Gaertn investigated through in silico and quantum computational methods

Suppressing vascular endothelial growth factor (VEGF), its receptor (VEGFR2), and the VEGF/VEGFR2 signaling cascade system to inhibit angiogenesis has emerged as a possible cancer therapeutic target. The present work was designed to discover and evaluate bioactive phytochemicals from the Clerodendrum inerme (L.) Gaertn plant for their anti-angiogenic potential. Molecular docking of twenty-one phytochemicals against the VEGFR-2 (PDB ID: 3VHE) protein was performed, followed by ADMET profiling and molecular docking simulations. These investigations unveiled two hit compounds, cirsimaritin (- 12.29 kcal/mol) and salvigenin (- 12.14 kcal/mol), with the highest binding energy values when compared to the reference drug, Sorafenib (- 15.14 kcal/mol). Furthermore, only nine phytochemicals (cirsimaritin and salvigenin included) obeyed Lipinski's rule of five and passed ADMET filters. Molecular dynamics simulations run over 100 ns revealed that the protein-ligand complexes remained stable with minimal backbone fluctuations. The binding free energy values of cirsimaritin (- 52.35 kcal/mol) and salvigenin (- 55.89 kcal/mol), deciphered by MM-GBSA analyses, further corroborated the docking interactions. The HOMO-LUMO band energy gap (ΔE) was calculated using density-functional theory (DFT) and substantiated using density of state (DOS) spectra. The chemical reactivity analyses revealed that salvigenin exhibited the highest chemical softness value (6.384 eV), the lowest hardness value (0.07831 eV), and the lowest ΔE value (0.1566 eV), which implies salvigenin was less stable and chemically more reactive than cirsimaritin and sorafenib. These findings provide further evidence that cirsimaritin and salvigenin have the ability to prevent angiogenesis and the development of cancer. Nevertheless, more in vitro and in vivo confirmation is necessary.

3D-QSAR-based pharmacophore modelling of quinazoline derivatives for the identification of acetylcholinesterase inhibitors through virtual screening, molecular docking, molecular dynamics and DFT studies

Alzheimer's disease (AD) is a progressive neurological disorder responsible for the cognitive dysfunction and cognitive impairment in the patients. Acetylcholinesterase inhibitors (AChEIs) are used to treat AD however, these only provided symptomatic relief and more efficient drug molecules are desired for the effective treatment of the disease. In this article, ligand-based drug-designing strategy was used to develop and validate a field-based 3D-QSAR pharmacophore model on quinazoline-based AChEIs reported in the literature. The validated pharmacophore model (AAAHR_1) was used as a prefilter to screen an ASINEX database via virtual screening workflow (VSW). The hits generated were subjected to MM-GBSA to identify potential AChEIs and top three scoring molecules (BAS 05264565, LEG 12727144 and SYN 22339886) were evaluated for thermodynamic stability at the target site using molecular dynamic simulations. Additionally, DFT study was performed to predict the reactivity of lead molecules towards acetylcholinesterase (AChE). Thus, by utilising various computational tools, three molecules were identified as potent AChEIs that can be developed as potential drug candidates for the treatment of AD.Communicated by Ramaswamy H. Sarma.

The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials

Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.

Synthesis, Computational, and Anticancer In Vitro Investigations of Aminobenzylnaphthols Derived from 2-Naphtol, Benzaldehydes, and α-Aminoacids via the Betti Reaction

Multicomponent reactions have emerged as an important approach for the synthesis of diverse and complicated chemical compounds. They have various advantages over two-component reactions, including the convenience of one-pot procedures and the ability to modify the structure of agents. Here, we employed in vitro and in silico studies to explore the anticancer potential of novel aminobenzylnaphthols derived from the Betti reaction (MMZ compounds). MTT assay was used to explore the cytotoxic activity of the compounds in pancreatic (BxPC-3 cells) and colorectal (HT-29) cancer cell lines or normal human lung fibroblasts (WI-38 cells). Proapoptotic properties of two derivatives MMZ-45AA and MMZ-140C were explored using AO/EB and annexin V-FITC/PI staining. In silico studies including ADMET profiling, molecular target prediction, docking, and dynamics were employed. The compounds exhibited cytotoxic properties and showed proapoptotic properties in respective IC50 concentrations. As indicated by in silico investigations, anticancer activity of MMZs can be attributed to the inhibition of ADORA1, CDK2, and TRIM24. Furthermore, compounds exhibited favorable ADMET properties. MMZs constitute an interesting scaffold for the potential development of new anticancer agents.

Identification of natural product as selective PI3Kα inhibitor against NSCLC: multi-ligand pharmacophore modeling, molecular docking, ADME, DFT, and MD simulations

Non-small cell lung cancer (NSCLC) is a widespread and often aggressive form of cancer affecting people worldwide. PIK3CA missense mutations play a significant role in the progression of growth factor signaling in cancer, making PI3Kα an important biological target for inhibition against NSCLC. Natural product molecules with PI3Kα inhibitory activity are promising therapeutic agents for the treatment of NSCLC, owing to their selectivity and potentially lower toxicity compared to synthetic compounds. To discover new natural product molecules, we integrated ligand-based virtual screening with structure-based virtual screening. We developed a multi-ligand pharmacophore hypothesis, validated it with 3D Field-based QSAR, and screened a Natural-Product-Based Library (ChemDiv) containing 3601 molecules. After initial screening, 137 hit molecules were generated and further screened using the extra precision (XP) Glide docking protocol. The best ten molecules were selected for free binding energy (ΔG) analysis using MMGBSA and ADME predictions. For further optimization, the top four hits were subjected to induced fit docking (IFD), quantum chemical descriptors analysis by Frontier Molecular Orbital (FMO) studies, and a 100 ns molecular dynamics (MD) simulation. The compounds-S721-1955, CM4579-5085, S721-1963, and S721-1999-exhibited better results than the PI3Kα selective inhibitor alpelisib. In silico prediction analysis of S721-1955 and alpelisib revealed that the former exhibited superior selectivity theoretically, as evidenced by its higher affinity for the target protein. The selective natural product molecule identified in this study holds promise as a potential anti-cancer drug against NSCLC in the near future, but further in vitro and in vivo studies are necessary to confirm its efficacy.

Computational insights into pediatric adenovirus inhibitors: in silico strategies for drug repurposing

Human adenovirus (HADV) infection can pose a serious threat to children, leading to a variety of respiratory illnesses and other complications. Particularly, children with weak immune systems are vulnerable to severe adenovirus infections with high mortality. The main focus of this study is to propose new antiviral agents as lead HADV inhibitors for children. So, several antiviral agents used in children were subjected to finding new HADV inhibitors using important computational methods of molecular docking, molecular dynamics (MD) simulation, Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) binding free energy calculations, density functional theory (DFT), and pharmacokinetic analysis. Molecular docking of standard cidofovir along with other ligands, suggested that sofosbuvir has the highest binding energy (-10.8 kcal/mol), followed by baloxavir marboxil (-10.36 kcal/mol). Further, the analysis of molecular interactions using MD simulation (100 ns) and MM-PBSA indicated that baloxavir marboxil has formed the most stable protein-ligand complex with HADV, followed by sofosbuvir. The binding free energies of baloxavir marboxil and sofosbuvir were found to be -61.724 kJ/mol and -48.123 kJ/mol, respectively. The DFT and drug-likeness properties of these compounds were also investigated. Overall, two antiviral agents, such as baloxavir marboxil, and sofosbuvir are suggested as lead repurposed candidates against HADV.Communicated by Ramaswamy H. Sarma.

Application of DFT Calculations in Designing Polymer-Based Drug Delivery Systems: An Overview

Drug delivery systems transfer medications to target locations throughout the body. These systems are often made up of biodegradable and bioabsorbable polymers acting as delivery components. The introduction of density functional theory (DFT) has tremendously aided the application of computational material science in the design and development of drug delivery materials. The use of DFT and other computational approaches avoids time-consuming empirical processes. Therefore, this review explored how the DFT computation may be utilized to explain some of the features of polymer-based drug delivery systems. First, we went through the key aspects of DFT and provided some context. Then we looked at the essential characteristics of a polymer-based drug delivery system that DFT simulations could predict. We observed that the Gaussian software had been extensively employed by researchers, particularly with the B3LYP functional and 6-31G(d, p) basic sets for polymer-based drug delivery systems. However, to give researchers a choice of basis set for modelling complicated organic systems, such as polymer–drug complexes, we then offered possible resources and presented the future trend.

nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset

In this work we present nablaDFT, the new dataset and benchmark for the Density Functional Theory Hamiltonian and energy prediction. We provide data for over 1 million different molecules and over 5 million conformations and baseline models for both tasks.