Ligand-based Drug Design Approaches Research Articles

Semaglutide as a Possible Calmodulin Binder: Ligand-Based Computational Analyses and Relevance to Its Associated Reward and Appetitive Behaviour Actions

Semaglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, has gained considerable attention as a therapeutic agent for type 2 diabetes mellitus and obesity. Despite its clinical success, the precise mechanisms underlying its pharmacological effects remain incompletely understood. In this study, we employed ligand-based drug design strategies to investigate potential off-target interactions of semaglutide. Through a comprehensive in silico screening of semaglutide’s structural properties against a diverse panel of proteins, we have identified calmodulin (CaM) as a putative novel target of semaglutide. Molecular docking simulations revealed a strong interaction between semaglutide and CaM, characterized by favourable binding energies and a stable binding pose. Further molecular dynamics simulations confirmed the stability of the semaglutide–CaM complex, emphasizing the potential for a physiologically relevant interaction. In conclusion, our ligand-based drug design approach has uncovered calmodulin as a potential novel target of semaglutide. This discovery sheds light on the complex pharmacological profile of semaglutide and offers a promising direction for further research into the development of innovative therapeutic strategies for metabolic disorders. The CaM, and especially so the CaMKII, system is central in the experience of both drug- and natural-related reward. It is here hypothesized that, due to semaglutide binding, the reward pathway-based calmodulin system may be activated, and/or differently regulated. This may result in the positive semaglutide action on appetitive behaviour. Further studies are required to confirm these findings.

On the Road towards Small-Molecule Programmed Cell Death 1 Ligand 1 Positron Emission Tomography Tracers: A Ligand-Based Drug Design Approach.

PD-1/PD-L1 immune checkpoint blockade for cancer therapy showed promising results in clinical studies. Further endeavors are required to enhance patient stratification, as, at present, only a small portion of patients with PD-L1-positive tumors (as determined by PD-L1 targeted immunohistochemistry; IHC) benefit from anti-PD-1/PD-L1 immunotherapy. This can be explained by the heterogeneity of tumor lesions and the intrinsic limitation of multiple biopsies. Consequently, non-invasive in vivo quantification of PD-L1 on tumors and metastases throughout the entire body using positron emission tomography (PET) imaging holds the potential to augment patient stratification. Within the scope of this work, six new small molecules were synthesized by following a ligand-based drug design approach supported by computational docking utilizing lead structures based on the (2-methyl-[1,1'-biphenyl]-3-yl)methanol scaffold and evaluated in vitro for potential future use as PD-L1 PET tracers. The results demonstrated binding affinities in the nanomolar to micromolar range for lead structures and newly prepared molecules, respectively. Carbon-11 labeling was successfully and selectively established and optimized with very good radiochemical conversions of up to 57%. The obtained insights into the significance of polar intermolecular interactions, along with the successful radiosyntheses, could contribute substantially to the future development of small-molecule PD-L1 PET tracers.

In-Silico Design and Evaluation of the Anti-Wolbachia Potential of Boron-Pleuromutilins

Filariasis (Lymphatic filariasis and Onchocerciasis) is a common neglected tropical disease caused by parasitic nematodes called filarial worms, which often host the Wolbachia bacteria. A good treatment approach seeks Wolbachia as a drug target. Here, a computer-aided design of some boron-pleuromutilin analogs was conducted using the ligand-based drug design approach while performing molecular docking investigation and pharmacokinetics analyses to evaluate their drug-likeness properties. The newly designed compounds (49a, 49b, and 49c) showed improved inhibitory activities (pEC50) over those of the template and the clinically relevant pleuromutilins (retapamulin and lefamulin) in the order; 49b (pEC50 = 9.0409) > 49c (8.8175) > 49a (8.5930) > template (49) (8.4222) > retapamulin (6.7403) > lefamulin (6.1369). Standard docking performed with OTU deubiquitinase (6W9O) revealed the order of binding energies; 49c (-88.07 kcal/mol) > 49b (-84.26 kcal/mol) > doxycycline (-83.70 kcal/mol) > template (-82.57 kcal/mol) > 49a (-78.43 kcal/mol) > lefamulin (-76.83 kcal/mol) > retapamulin (-76.78 kcal/mol), with the new compounds all showing good pharmacological interactions with the receptor’s amino acids. The new analogs were also predicted to be orally bioavailable with better pharmacokinetic profiles than the template, retapamulin, lefamulin, and doxycycline having no more than one violation of Lipinski’s ROF. Therefore, the newly designed compounds could be considered potential anti-filarial drug candidates.

Integrative Ligand-Based Pharmacophore Modeling, Virtual Screening, and Molecular Docking Simulation Approaches Identified Potential Lead Compounds against Pancreatic Cancer by Targeting FAK1

Pancreatic cancer is a very deadly disease with a 5-year survival rate, making it one of the leading causes of cancer-related deaths globally. Focal adhesion kinase 1 (FAK1) is a ubiquitously expressed protein in pancreatic cancer. FAK, a tyrosine kinase that is overexpressed in cancer cells, is crucial for the development of tumors into malignant phenotypes. FAK functions in response to extracellular signals by triggering transmembrane receptor signaling, which enhances focal adhesion turnover, cell adhesion, cell migration, and gene expression. The ligand-based drug design approach was used to identify potential compounds against the target protein, which included molecular docking: ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized born surface area (MM-GBSA). Following the retrieval of twenty hits, four compounds were selected for further evaluation based on a molecular docking approach. Three newly discovered compounds, including PubChem CID24601203, CID1893370, and CID16355541, with binding scores of -10.4, -10.1, and -9.7 kcal/mol, respectively, may serve as lead compounds for the treatment of pancreatic cancer associated with FAK1. The ADME (absorption, distribution, metabolism, and excretion) and toxicity analyses demonstrated that the compounds were effective and nontoxic. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the cancer.

Structure-activity and binding orientations analysis of potent, newly synthesized, acetylcholinesterase inhibitors

Fourteen novel N-benzylpiperidine and N,N-diarylpiperazine conjugates were synthesized and pharmacologically evaluated as donepezil analogs, in a ligand-based drug design approach. All compounds possessed high to moderate in vitro inhibitory activity with IC50 in range 2.3–20 µM and selectivity towards acetylcholinesterase, while being inactive towards butyrylcholinesterase. Structure-activity relationship analysis revealed the influence of N,N-diarylpiperazine moiety and the linker connecting two pharmacophores on the inhibitory activity. Kinetic studies on the two most active compounds 7g and 8g (IC50 = 2.3 and 4 µM, respectively) revealed mixed type mode of inhibition, binding to both, free enzyme and enzyme-substrate complex. For further understanding of mechanism of action and binding orientations, molecular docking was performed for all compounds, while the ligand transport simulation and molecular dynamics simulations were performed for 7g and 8g. Computational results corroborated well with in vitro activities and kinetic studies.

Identification of potential death-associated protein kinase-1 (DAPK1) inhibitors by an integrated ligand-based and structure-based computational drug design approach

Death-associated protein kinase 1 (DAPK1) is a calcium/calmodulin (Ca2+/CaM)-dependent serine/threonine kinase that is abundantly expressed in the memory- and cognition-related brain areas. DAPK1 is associated with several pathological hallmarks of Alzheimer’s disease (AD); it is an attractive target for designing a novel DAPK1 inhibitor as an effective therapeutic treatment for AD. In the present study, we have used an integrated ligand-based and structure-based drug design method to identify DAPK1 inhibitors. The pharmacophoric features of compound 38 G (PDB ID 4TXC) were mapped, and the models were evaluated using enrichment factor (EF) and goodness of hit (GH) score. The selected models were used to screen Zinc 15 compounds library. The identified hits were passed through drug-likeliness and PAINS filtering. The docking study was performed in three steps to yield molecules with good binding energy and ligand-target interactions. Finally, three hits were obtained, that is, ZINC000020648330, ZINC000006755051 and ZINC000020650468, which were subjected to rigorous molecular dynamics simulation. All three hits exhibited optimal stability under simulated conditions and low predicted toxicity. Communicated by Ramaswamy H. Sarma

Computer-Aided Drug Design: An Update.

Computer-aided drug design (CADD) approaches are playing an increasingly important role in understanding the fundamentals of ligand-receptor interactions and helping medicinal chemists design therapeutics. About 5years ago, we presented a chapter devoted to an overview of CADD methods and covered typical CADD protocols including structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches that were frequently used in the antibiotic drug design process. Advances in computational hardware and algorithms and emerging CADD methods are enhancing the accuracy and ability of CADD in drug design and development. In this chapter, an update to our previous chapter is provided with a focus on new CADD approaches from our laboratory and other peers that can be employed to facilitate the development of antibiotic therapeutics.

Integration of Ligand-Based and Structure-Based Methods for the Design of Small-Molecule TLR7 Antagonists.

Toll-like receptor 7 (TLR7) is activated in response to the binding of single-stranded RNA. Its over-activation has been implicated in several autoimmune disorders, and thus, it is an established therapeutic target in such circumstances. TLR7 small-molecule antagonists are not yet available for therapeutic use. We conducted a ligand-based drug design of new TLR7 antagonists through a concerted effort encompassing 2D-QSAR, 3D-QSAR, and pharmacophore modelling of 54 reported TLR7 antagonists. The developed 2D-QSAR model depicted an excellent correlation coefficient (R2training: 0.86 and R2test: 0.78) between the experimental and estimated activities. The ligand-based drug design approach utilizing the 3D-QSAR model (R2training: 0.95 and R2test: 0.84) demonstrated a significant contribution of electrostatic potential and steric fields towards the TLR7 antagonism. This consolidated approach, along with a pharmacophore model with high correlation (Rtraining: 0.94 and Rtest: 0.92), was used to design quinazoline-core-based hTLR7 antagonists. Subsequently, the newly designed molecules were subjected to molecular docking onto the previously proposed binding model and a molecular dynamics study for a better understanding of their binding pattern. The toxicity profiles and drug-likeness characteristics of the designed compounds were evaluated with in silico ADMET predictions. This ligand-based study contributes towards a better understanding of lead optimization and the future development of potent TLR7 antagonists.

A review of approaches in computer-aided drug design in drug discovery

The process of discovering and developing a new medication is often seen as a lengthy and expensive endeavors. As a result, computer-aided drug design methods are now frequently utilized to improve the efficiency of the drug discovery and development process. Various CADD approaches are regarded as potential techniques based on their needs; nevertheless, structure-based drug design and ligand-based drug design approaches are well-known as highly efficient and powerful strategies in drug discovery and development. Both of these approaches may be used in conjunction with molecular docking to conduct virtual screening for the purpose of identifying and optimizing leads. In recent years, computational tools have become increasingly popular in the pharmaceutical industry and academic fields as a means of improving the efficiency and effectiveness of the drug discovery and development pipeline. In this post, we'll go over computational methods, which are a creative way of discovering new leads and assisting in drug discovery and development research.

Identification of Mtb GlmU Uridyltransferase Domain Inhibitors by Ligand-Based and Structure-Based Drug Design Approaches.

Targeting enzymes that play a role in the biosynthesis of the bacterial cell wall has long been a strategy for antibacterial discovery. In particular, the cell wall of Mycobacterium tuberculosis (Mtb) is a complex of three layers, one of which is Peptidoglycan, an essential component providing rigidity and strength. UDP-GlcNAc, a precursor for the synthesis of peptidoglycan, is formed by GlmU, a bi-functional enzyme. Inhibiting GlmU Uridyltransferase activity has been proven to be an effective anti-bacterial, but its similarity with human enzymes has been a deterrent to drug development. To develop Mtb selective hits, the Mtb GlmU substrate binding pocket was compared with structurally similar human enzymes to identify selectivity determining factors. Substrate binding pockets and conformational changes upon substrate binding were analyzed and MD simulations with substrates were performed to quantify crucial interactions to develop critical pharmacophore features. Thereafter, two strategies were applied to propose potent and selective bacterial GlmU Uridyltransferase domain inhibitors: (i) optimization of existing inhibitors, and (ii) identification by virtual screening. The binding modes of hits identified from virtual screening and ligand growing approaches were evaluated further for their ability to retain stable contacts within the pocket during 20 ns MD simulations. Hits that are predicted to be more potent than existing inhibitors and selective against human homologues could be of great interest for rejuvenating drug discovery efforts towards targeting the Mtb cell wall for antibacterial discovery.

Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches.

JAK1 plays a significant role in the intracellular signaling by interacting with cytokine receptors in different types of cells and is linked to the pathogenesis of various cancers and in the pathology of the immune system. In this study, ligand-based pharmacophore modeling combined with virtual screening and molecular docking methods was incorporated to identify the potent and selective lead compounds for JAK1. Initially, the ligand-based pharmacophore models were generated using a set of 52 JAK1 inhibitors named C-2 methyl/hydroxyethyl imidazopyrrolopyridines derivatives. Twenty-seven pharmacophore models with five and six pharmacophore features were generated and validated using potency and selectivity validation methods. During potency validation, the Guner-Henry score was calculated to check the accuracy of the generated models, whereas in selectivity validation, the pharmacophore models that are capable of identifying selective JAK1 inhibitors were evaluated. Based on the validation results, the best pharmacophore models ADHRRR, DDHRRR, DDRRR, DPRRR, DHRRR, ADRRR, DDHRR, and ADPRR were selected and taken for virtual screening against the Maybridge, Asinex, Chemdiv, Enamine, Lifechemicals, and Zinc database to identify the new molecules with novel scaffold that can bind to JAK1. A total of 4,265 hits were identified from screening and checked for acceptable drug-like properties. A total of 2,856 hits were selected after ADME predictions and taken for Glide molecular docking to assess the accurate binding modes of the lead candidates. Ninety molecules were shortlisted based on binding energy and H-bond interactions with the important residues of JAK1. The docking results were authenticated by calculating binding free energy for protein–ligand complexes using the MM-GBSA calculation and induced fit docking methods. Subsequently, the cross-docking approach was carried out to recognize the selective JAK1 lead compounds. Finally, top five lead compounds that were potent and selective against JAK1 were selected and validated using molecular dynamics simulation. Besides, the density functional theory study was also carried out for the selected leads. Through various computational studies, we observed good potency and selectivity of these lead compounds when compared with the drug ruxolitinib. Compounds such as T5923555 and T5923531 were found to be the best and can be further validated using in vitro and in vivo methods.

Part-II- in silico drug design: application and success

Abstract In silico tools have indeed reframed the steps involved in traditional drug discovery and development process and the term in silico has become a familiar term in pharmaceutical sector like the terms in vitro and in vivo. The successful design of HIV protease inhibitors, Saquinavir, Indinavir and other important medicinal agents, initiated interest of researchers in structure based drug design approaches (SBDD). The interactions between biomolecules and a ligand, binding energy, free energy and stability of biomolecule-ligand complex can be envisioned and predicted by applying molecular docking studies. Protein-ligand, protein-protein, DNA-ligand interactions etc. aid in elucidating molecular level mechanisms of drug molecules. In the Ligand based drug design (LBDD) approaches, QSAR studies have tremendously contributed to the development of antimicrobial, anticancer, antimalarial agents. In the recent years, multiQSAR (mt-QSAR) approaches have been successfully employed for designing drugs against multifactorial diseases. Output of a research in several instances is rewarding when both SBDD and LBDD approaches are combined. Application of in silico studies for prediction of pharmacokinetics was once a real challenge but one can see unlimited number publications comprising tools, data bases which can accurately predict almost all the pharmacokinetic parameters. Absorption, distribution, metabolism, transporters, blood brain barrier permeability, hERG toxicity, P-gp affinity and several toxicological end points can be accurately predicted for a candidate molecule before its synthesis. In silico approaches are greatly encouraged a result of growing limitations and new legislations related to the animal use for research. The combined use of in vitro data and in silico tools will definitely decrease the use of animal testing in the future.In this chapter, in silico approaches and their applications are reviewed and discussed giving suitable examples.

Ligand-based design approach of potential Bcl-2 inhibitors for cancer chemotherapy

Background and ObjectivesOverexpression of prosurvival Bcl-2 family members make tumor cells resistant to conventional cancer therapeutic agents. It is commonly observed feature in many different types of human tumors. Hence, small-molecules as Bcl-2 inhibitors may have a promising therapeutic potential for the treatment of human cancer. The given study focusses on development of novel and small Bcl-2 inhibitors using ligand-based drug design approach. MethodsLigand based pharmacophore was generated using the PHASE tool of Schrödinger and screened ZINC database through ZINCPharmer webserver to identify compounds with similar features. Compounds having good fitness score were selected for molecular docking and binding interactions were compared with drugs in market as well as trials. QSAR model was generated using advanced AutoQSAR tool and validated for prediction of unknown compounds. QSAR prediction of in silico active identified three potential compounds and were subjected to investigate stability by molecular dynamics simulations and MM-PBSA binding energy calculations. ResultsStudy identified three in silico potential molecules with good stability and binding affinity. Further substructure search and pIC50 value prediction has identified six more molecules. Total nine molecules have demonstrated good drug likeness features. ConclusionFinal oral rat LD50 calculation of nine molecules has identified three hit molecules i.e., ZINC76760927, ZINC76768675 and ZINC52767796 for further in vitro and in vivo testing as safe and potential Bcl-2 inhibitors.

Design, synthesis and in silico insights of new 7,8-disubstituted-1,3-dimethyl-1H-purine-2,6(3H,7H)-dione derivatives with potent anticancer and multi-kinase inhibitory activities

Aiming to obtain an efficient anti-proliferative activity, structure- and ligand-based drug design approaches were expanded and utilized to design and refine a small compound library. Subsequently, thirty-two 7,8-disubstituted-1,3-dimethyl-1H-purine-2,6(3H,7H)-dione derivatives were selected for synthesis based on the characteristic pharmacophoric features required for PI3K and B-Raf oncogenes inhibition. All the synthesized compounds were evaluated for their in vitro anticancer activity. Compounds 17 and 22c displayed an acceptable potent activity according to the DTP-NCI and were further evaluated in the NCI five doses assay. To validate our design, compounds with the highest mean growth inhibition percent were screened against the target PI3Kα and B-RafV600E to confirm their multi-kinase activity. The tested compounds showed promising multi-kinase activity. Compounds 17 and 22c anticancer effectiveness and multi-kinase activity against PI3Kα and B-RafV600E were consolidated by the inhibition of B-RafWT, EGFR and VEGFR-2 with IC50 in the sub-micromolar range. Further investigations on the most potent compounds 17 and 22c were carried out by studying their safety on normal cell line, in silico profiling and predicted ADME characteristics.

Ligand-based Drug Design Approach in Designing New Leads from Existing Pharmacophores; Binding Pocket Estimation and Binding Pocket-directed Screening of Antiviral Drugs for Treatment of Novel Corona COVID-19 and Topoisomerase Inhibitors

Ligand-based Drug Design Approach in Designing New Leads from Existing Pharmacophores; Binding Pocket Estimation and Binding Pocket-directed Screening of Antiviral Drugs for Treatment of Novel Corona COVID-19 and Topoisomerase Inhibitors

In silico design and molecular docking study of CDK2 inhibitors with potent cytotoxic activity against HCT116 colorectal cancer cell line

BackgroundColorectal cancer is common to both sexes; third in terms of morbidity and second in terms of mortality, accounting for 10% and 9.2% of cancer cases in men and women globally. Although drugs such as bevacizumab, Camptosar, and cetuximab are being used to manage colorectal cancer, the efficacy of the drugs has been reported to vary from patient to patient. These drugs have also been reported to have varying degrees of side effects; thus, the need for novel drug therapies with better efficacy and lesser side effects. In silico drugs design methods provide a faster and cost-effect method for lead identification and optimization. The aim of this study, therefore, was to design novel imidazol-5-ones via in silico design methods. ResultsA QSAR model was built using the genetic function algorithm method to model the cytotoxicity of the compounds against the HCT116 colorectal cancer cell line. The built model had statistical parameters; R2 = 0.7397, R2adj = 0.6712, Q2cv = 0.5547, and R2ext. = 0.7202 and revealed the cytotoxic activity of the compounds to be dependent on the molecular descriptors nS, GATS5s, VR1_Dze, ETA_dBetaP, and L3i. These molecular descriptors were poorly correlated (VIF < 4.0) and made unique contributions to the built model. The model was used to design a novel set of derivatives via the ligand-based drug design approach. Compounds e, h, j, and l showed significantly better cytotoxicity (IC50 < 5.0 μM) compared to the template. The interaction of the compounds with the CDK2 enzyme (PDB ID: 6GUE) was investigated via molecular docking study. The compounds were potent inhibitors of the enzyme having binding affinity of range −10.8 to −11.0 kcal/mol and primarily formed hydrogen bond interaction with lysine, aspartic acid, leucine, and histidine amino acid residues of the enzyme. ConclusionThe QSAR model built was stable, robust, and had a good predicting ability. Thus, predictions made by the model were reliably employed in further in silico studies. The compounds designed were more active than the template and showed better inhibition of the CDK2 enzyme compared to the standard drugs sorafenib and kenpaullone.

Structural Basis of Activin Receptor-Like Kinase 2 (R206H) Inhibition by Bis-heteroaryl Pyrazole-Based Inhibitors for the Treatment of Fibrodysplasia Ossificans Progressiva Identified by the Integration of Ligand-Based and Structure-Based Drug Design Approaches

Fibrodysplasia ossificans progressiva (FOP) is a rare but severe genetic disorder in which acute inflammation elicits progressive heterotopic ossification in the muscles, tendons, and ligaments. Classic FOP is caused by the R206H mutation in ALK2/ACVR1. While several activin receptor-like kinase 2 (ALK2) inhibitors were found to be efficacious in animal models of FOP, most of the ALK2 (R206H) inhibitors lacked sufficient oral bioavailability for efficacy. Previously, the synthesis of a series of novel bis-heteroaryl pyrazole-based ALK2 (R206H) inhibitors that achieved both substantial potency and an improved ADMET profile was reported. In the present study, the detailed procedure of the in silico approach employed to identify the initial bis-heteroaryl pyrazole-based ALK2 (R206H) inhibitor RK-59638 and the analysis of the ALK2 (R206H) RK-59638 complex structure to guide the synthetic optimization of the chemical series, obtaining RK-71807 showing improved potency and metabolic stability, were described. According to the initial in silico screening, the screening efficiencies and chemical diversity of the hit compounds of both ligand-based and structure-based methods were evaluated. Then, X-ray structures of ALK2 (R206H) and the inhibitors were analyzed to assess the structure–activity relationships of the synthesized compounds. The 3D-RISM analysis indicated the existence of the additional hydrogen bond via water molecules restricting the attachment point in the pyrazole scaffold. The quantum mechanics calculation of the newly determined ALK2 (R206H) RK-71807 complex structure using a fragment molecular orbital method and pair interaction energy decomposition analysis was employed to evaluate the interaction energies between the inhibitor and each of the amino acid residues and decompose them to electrostatic, exchange-repulsion, and charge transfer energies. The pattern of decomposed interaction energies was then compared to that formed by RK-59638 and LDN-193189 to investigate the structural basis of ALK2 (R206H) inhibition.

Protein ligand interaction analysis against new CaMKK2 inhibitors by use of X-ray crystallography and the fragment molecular orbital (FMO) method

CaMKK2 (calcium/calmodulin dependent protein kinase kinase 2) is a serine/threonine protein kinase that regulates phosphorylation of CaM kinases (CaMKs) such as CaMKI, CaMKIV, and AMP-activated protein kinase (AMPK). From a pathological perspective, CaMKK2 plays a role in obesity, diabetes, and prostate cancer. Therefore, CaMKK2 is an attractive target protein for drug design. Here, we tried to find new CaMKK2 inhibitors by using ligand-based and structure-based drug design approaches. From the in silico hit compounds, we identified new inhibitors by using a CaMKK2 kinase assay. We solved X-ray crystallography structures of the CaMKK2–inhibitor complexes and performed Fragment Molecular Orbital (FMO) calculations to analyze the protein–ligand interactions, identify the key residues in inhibitor binding, and quantitatively measure their contribution. We experimentally determined five CaMKK2–inhibitor structures and calculated the binding energies of the inhibitors by the FMO method plus MM-PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) approach. The results showed a high correlation (R = −0.89) between experimentally measured inhibitory activity (pIC50) and the predicted ligand binding energy. We then quantitatively evaluated the contribution of each binding site residue in CaMKK2 by the IFIE (Inter-fragment Interaction Energy)/PIEDA (Pair Interaction Energy Decomposition Analysis) method. The IFIE values indicated that Lys194 and Glu236, which formed hydrogen bonds with the carboxylate groups of the inhibitors, were key residues for ligand binding. PIEDA revealed that the dispersion interaction of inhibitors with hydrophobic residues, such as Ile171, Phe267, and Leu319, contributed highly to ligand binding; we considered that this was due to CH-π interactions with methoxy groups and/or aromatic rings contained in our CaMKK2 inhibitor. These results from the quantitative interaction analysis by the FMO method are useful not only for future CaMMK2 inhibitor development but for application of the FMO method to in silico drug design.

In Silico Identification of Potential Inhibitors of the Main Protease of SERS-CoV-2 Using Combined Ligand-based and Structure-based Drug Design approach

Objectives: The outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) remains a serious global threat At the time of writing, there are no specific therapeutic agents or vaccines to combat this disease This study was designed to identify the SARS-CoV-2 main protease inhibitors using drug molecule information retrieved from DrugBank 5 0 (Wishart et al ) Methods: A set of common pharmacophores were generated from a series of 22 known SARS-CoV inhibitors The best pharmacophore used for virtual screening (VS) of DrugBank using the Phase module followed by structure-based virtual screening (VS) using Glide (Release 2020-1;Schrodinger LLC, New York, NY, USA) with SARS-CoV-2 main protease and 50 ns molecular dynamics (MD) simulation studies Results: Six hits were selected based on the fitness score, extra-precision Glide score, and binding affinity with the main protease (Mpro) The predicted inhibitor constant (Ki) values of the 3 best hits, DB03777, DB06834, and DB07456, were 0 8176, 0 2148, and 0 1006 mu M, respectively An MD simulation of DB07456 and DB13592 with the Mpro demonstrated stable protein-ligand complexes Conclusion: The selected inhibitors displayed a similar type of binding interaction with co-ligands and remdesivir, and the predicted Ki values of 2 inhibitors were found to be superior to remdesivir These selected hits may be used for further in vitro and in vivo studies against the SARS- CoV-2 Mpro

(Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds.

Despite the achievements of antiretroviral therapy, discovery of new anti-HIV medicines remains an essential task because the existing drugs do not provide a complete cure for the infected patients, exhibit severe adverse effects, and lead to the appearance of resistant strains. To predict the interaction of drug-like compounds with multiple targets for HIV treatment, ligand-based drug design approach is widely applied. In this study, we evaluated the possibilities and limitations of (Q)SAR analysis aimed at the discovery of novel antiretroviral agents inhibiting the vital HIV enzymes. Local (Q)SAR models are based on the analysis of structure–activity relationships for molecules from the same chemical class, which significantly restrict their applicability domain. In contrast, global (Q)SAR models exploit data from heterogeneous sets of drug-like compounds, which allows their application to databases containing diverse structures. We compared the information for HIV-1 integrase, protease and reverse transcriptase inhibitors available in the EBI ChEMBL, NIAID HIV/OI/TB Therapeutics, and Clarivate Analytics Integrity databases as the sources for (Q)SAR training sets. Using the PASS and GUSAR software, we developed and validated a variety of (Q)SAR models, which can be further used for virtual screening of new antiretrovirals in the SAVI library. The developed models are implemented in the freely available web resource AntiHIV-Pred.