Structure-based Molecular Docking Research Articles

Novel Dipeptide Inhibitors of PfPNP: In-Silico Identification of Promising New Antimalarials.

Malaria, an infectious disease caused by Plasmodium falciparum, is becoming increasingly difficult to treat due to the emergence of drug-resistant strains. Recent studies have proposed purine nucleoside phosphorylase from P. falciparum (PfPNP) as a potential target for malaria treatment. In the present study, we designed a virtual library of 400 dipeptides to discover novel anti-malarial peptide inhibitors. A structure-based molecular docking method was employed to virtually screen the designed library against the wild-type structure of PfPNP (PDB: 5ZNC). The best four (Phe-Arg, Arg-His, Trp-Arg and Tyr-Arg) dipeptides, which were then investigated for their binding potential against PfPNP using Molecular Dynamics simulation studies. Parameters such as RMSD, RMSF, Rg, and SASA were analyzed to understand the structural changes, energetics, and overall behavior of PfPNP-dipeptide complexes. The PfPNP demonstrated significant stability upon binding with each of the identified dipeptides with ΔG of over -168 kcal/mol. Additionally, DFT and ADME predictions indicated that the electronic structure, energetics, and pharmacokinetic properties of Phe-Arg, Arg-His, Trp-Arg and Tyr-Arg were favourable for drug development. Our comprehensive computational investigation has identified these four dipeptides as promising candidates. These designed and selected dipeptides may further be modified using peptidomimetic and medicinal chemistry tools to develop a novel class of promising antimalarials.

Exploring The Antimicrobial Potential of Novel 1,2,4‒Triazole Conjugates with Pyrazole: Synthesis, Biological Activity and In Silico Docking.

In the present study, a new series of 1,2,4‒triazole linked pyrazole hybrids (5a‒5l) were synthesized from dimethyl amino pyrazole (1) in good yield by three-step reaction. The chemical structures of the resulted compounds were thoroughly elucidated using spectral analyses such as IR, 1H-NMR, 13C-NMR, mass spectra and elemental analysis. The target compounds were screened for their antimicrobial activities against the various standard pathogen strains, Gram‒(‒ve) (E. coli, P. aeruginosa, K. pneumoniae, A. baumannii), and Gram‒(+ve) (S. aureus,S. faecalis) microorganisms. According to the results obtained, in particular, compounds 5b, 5f, 5h and 5j was effective at inhibiting the antibacterial growth of all the bacteria's, having MIC values ranging 0.983‒14.862 mg/mL and compared to moxifloxacin (1.391‒22.01 mg mL-1). The most active compounds were chosen to interact with the DNA gyrase and topoisomerase-IV targets via molecular docking. These selected ligands interacted with PDB targets 2XCO, 1S16 and docked into the active site of amino acids Ala-269, Gly-413, Asn-405, Ser-1182, Thr-1185, His-1186, His-1186, Lys-1189, and Trp-1213.The pharmacokinetic properties, stability, and drug-likeness parameters of all target molecules were estimated using SwissADME and PkCSM protocols. The current study used in silico approaches combining e-pharmacophore modeling and structure-based molecular docking of targets to identify antimicrobial agents.

Based on Molecular Docking, Molecular Dynamics Simulation and MM/PB(GB)SA to Study Potential Inhibitorsof PRRSV-Nsp4.

Porcine reproductive and respiratory syndrome (PRRS) is one of the most serious infectious immunosuppressive diseases in the world. The nonstructural protein Nsp4 can be used as an ideal target for anti-PRRSV replication inhibitors. However, little is known about potential inhibitors that target Nsp4 to affect PRRSV replication. The purpose of this study was to screen potential natural inhibitors that affect PRRSV replication by inhibiting Nsp4. Five compounds with strong binding affinity to Nsp4 were selected by structure-based molecular docking method. The complexes of naringin dihydrochalcone (NDC), agathisflavone (AGT), and amentoflavone (AMF) with Nsp4 were stable throughout the molecular dynamics simulation. According to MM/PBSA analysis, the free energies of binding of NDC, AGT, and AMF to Nsp4 were less than-30 Kcal/mol. In conclusion, these three compounds are worthy of further investigation as novel inhibitors of PRRSV. This study provides a theoretical basis for the development of anti-PRRSV natural drugs.

In-Silico ADMET Prediction, Structure-Based Drug Design and Molecular Docking Studies of Quinazoline Derivatives as Novel EGFR Inhibitors

This Present research provides valuable insights and implications for the development of quinazoline derivatives as novel EGFR inhibitors. The in-silico ADMET predictions indicate that the quinazoline derivatives possess promising drug-like properties, suggesting their potential for further development as oral drugs. The low risk of toxicity and favorable metabolic and excretion profiles enhance their suitability for therapeutic applications Molecular docking studies revealed strong binding interactions between the quinazoline derivatives and the EGFR kinase domain. These interactions suggest that these compound shave the potential to effectively inhibit EGFR activity, making them promising candidates for anti- cancer drug development to progress from in-silico findings to clinical applications, further research is essential. Future work should encompass in vitro and in vivo experiments to validate the inhibitory potential of these compounds against EGFR. Additionally, pharmacokinetic studies are warranted to confirm their ADMET properties. Structural optimization through medicinal chemistry approaches may further enhance their binding affinities and specificity for EGFR. It represents a pivotal step in the exploration of quinazoline derivatives as novel EGFR inhibitors. The combined efforts in ADMET prediction, structure-based drug design, and molecular docking have provided a strong foundation for the development of innovative anti-cancer therapies. The potential of these compounds to target EGFR, a crucial player in various cancers, holds great promise for improving the treatment options available to patients and advancing the field of oncology.

Rational Design of a Novel 6H-Benzo[c]chromen Series as Selective PI3Kα Inhibitors.

The phosphoinositide 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) signaling pathway is a frequently dysregulated pathway in human cancer, and PI3Kα is one of the most frequently mutated kinases in human cancer. A selective PI3Kα inhibitor may provide the opportunity to spare patients the side effects associated with broader inhibition of the class I PI3K family. Here, we describe our efforts to discover a novel series of selective PI3Kα inhibitors using structure-based drug design and molecular docking to inform the design of 6H-benzo[c]chromen inhibitors. XJTU-L453 (21) was identified with PI3Kα inhibitory potency and unique selectivity over other PI3K isoforms and all other kinases tested. Further evaluation of pharmacokinetic properties and in vivo efficacy led to the identification of the preclinical potential of XJTU-L453 (21).

Computational exploration and molecular dynamic simulation for the discovery of antiviral agents targeting Newcastle disease virus

Newcastle disease virus (NDV) is a highly infectious viral disease that impacts birds globally, especially domestic poultry. NDV is a type of avian paramyxovirus which poses a major threat to the poultry industry due to its ability to inflict significant economic damage. The membrane protein, Hemagglutinin-Neuraminidase (HN) of NDV is an attractive therapeutic candidate. It contributes to pathogenicity through various functions, such as promoting fusion and preventing viral self-agglutination, which allows for viral spread. In this study, we used pharmacophore modeling to identify natural molecules that can inhibit the HN protein of NDV. Physicochemical characteristics and phylogenetic analysis were determined to elucidate structural information and phylogeny of target protein across different species as well as members of the virus family. For structural analysis, the missing residues of HN target protein were filled and the structure was evaluated by PROCHECK and VERIFY 3D. Moreover, shape and feature-based pharmacophore model was employed to screen natural compounds’ library through numerous scoring schemes. Top 48 hits with 0.8860 pharmacophore fit score were subjected towards structure-based molecular docking. Top 9 compounds were observed witihin the range of −8.9 to −7.5 kcal/mol binding score. Five best-fitting compounds in complex with HN receptor were subjected to predict biological activity and further analysis. Top two hits were selected for MD simulations to validate binding modes and structural stability. Finally, upon scrutinization, A1 (ZINC05223166) emerges as potential HN inhibitor to treat NDV, necessitating further validation via clinical trials.

In Silico Investigation against Inhibitors of Alpha-Amylase Using Structure-based Screening, Molecular Docking, and Molecular Simulations Studies.

Type-II diabetes mellitus is a chronic disorder that results from fluctuations in the glucose level leading to hyperglycemia with severe adverse effects increasing worldwide. Alpha-Amylase is the key enzyme involved in the mechanism of glucose formation therefore Alpha-Amylase inhibitors have become a therapeutic target in the development of new leads as they have the potential to suppress glucose levels. Existing drugs targeting Alpha-Amylase highlight major drawbacks in terms of poor absorption rate that causes several gastrointestinal issues. So, this research is aimed to develop novel inhibitors interacting with Alpha-Amylase's active site using structural-based screening, binding pattern analysis, and molecular dynamic simulation. Hence, to search for a potential lead, we analyzed a total of 133 valiolamine derivatives and 535 desoxynojirimycin derivatives that exhibited drug-like properties screened through Lipinski filters. Virtual screening followed by binding interaction analysis we identified ten compounds that exhibited better binding energy scores compared to the standard drugs voglibose and miglitol, used in our study. The docking analysis, ADMET and metabolic site prediction estimated the best top two compounds with good drug profiles. Further, top compounds VG9 and VG15 were promoted to simulation study using the Biovia Discovery study to access the stability at a time interval of 100 ns. MD simulation results revealed that our compound VG9 possesses better conformational stability in the complex to the active site residues of Alpha-Amylase target protein than standard drug voglibose. Thus, our investigation revealed that compound VG9 also exhibits the best pharmacokinetic as well as binding affinity results and could act as a potential lead compound targeting Alpha-Amylase for Type II diabetes.

Structural exploration of the PfBLM Helicase-ATP Binding Domain and implications in the quest for antimalarial therapies.

The battle against malaria has witnessed remarkable progress in recent years, characterized by increased funding, development of life-saving tools, and a significant reduction in disease prevalence. Yet, the formidable challenge of drug resistance persists, threatening to undo these gains. To tackle this issue, it is imperative to identify new effective drug candidates against the malaria parasite that exhibit minimal toxicity. This study focuses on discovering such candidates by targeting PfRecQ1, also known as PfBLM, a vital protein within the malaria parasite Plasmodium falciparum . PfRecQ1 plays a crucial role in the parasite's life cycle and DNA repair processes, making it an attractive drug development target. The study employs advanced computational techniques, including molecular modeling, structure-based virtual screening (SBVS), ADMET profiling, molecular docking, and dynamic simulations. The study sources ligand molecules from the extensive MCULE database and utilizes strict filters to ensure that the compounds meet essential criteria. Through these techniques, the research identifies MCULE-3763806507-0-9 as a promising antimalarial drug candidate, surpassing the binding affinity of potential antimalarial drugs. However, it is essential to underscore that drug-like properties are primarily based on in silico experiments, and wet lab experiments are necessary to validate these candidates' therapeutic potential. This study represents a critical step in addressing the challenge of drug resistance in the fight against malaria.

Exploring Natural Medicinal Plants for Novel Bcl-2 Inhibitors: In Silico Drug Design and Computational Interaction Mechanism for Anti-Cancer Activity

The disturbance of typical cellular programmed cell death, known as apoptosis, constitutes a distinctive feature across all categories of malignancies. This maladjustment of apoptosis has the potential to result in diverse pathological states such as cancer, autoimmune illnesses, and neurodegenerative disorders. The regulation of apoptosis hinges upon proteins affiliated with the BcL-2 family, which possess the capacity to either foster or impede this progression. The exaggerated expression of anti-apoptotic proteins (Bcl-2, Bcl-xL, and Mcl-1) has been linked to the sustenance, proliferation, and advancement of tumors. Lately, there has been a surge of interest in investigating small compounds and peptides that possess the ability to bind to the BH3 binding pocket of these proteins, as they exhibit promising potential as agents against cancer. Initially, the primary emphasis in the development of anti-cancer agents targeting this protein family was centered on suppressing Bcl-2. However, the precise mechanisms of drugs specific to Bcl-2 and their impacts have not been fully clarified through computational approaches. By conducting a molecular docking analysis against the Bcl-2 protein (PDB ID: 4LVT), out of the 8450 phytomolecules, 6742 compounds were effectively docked with Bcl-2, displaying docking scores ranging from -7.22 kcal/mol to +5.54 kcal/mol. Further investigation employing structure-based molecular docking (SB-MD) and ADMET (absorption, distribution, metabolism, excretion, and toxicity) profile analysis led to the identification of several plant-derived compounds, such as “Norepinephrine, Australine, Calystegine B, 7,7 A-Diepialexine, and Alpha-Methylnoradrenaline” which exhibited strong binding to the active site residues of Bcl-2. In summary, these phytocompounds show promise as potential molecules against the Bcl-2 protein (4LVT) and warrant further validation through “in vitro and in vivo” experiments.

In Silico Screening of Some Active Phytochemicals to Identify Promising Inhibitors Against SARS-CoV-2 Targets.

There are very few small-molecule drug candidates developed against SARS-CoV-2 that have been revealed since the epidemic began in November 2019. The typical medicinal chemistry discovery approach requires more than a decade of the year of painstaking research and development and a significant financial guarantee, which is not feasible in the challenge of the current epidemic. This current study proposes to find and identify the most effective and promising phytomolecules against SARS-CoV-2 in six essential proteins (3CL protease, Main protease, Papain- Like protease, N-protein RNA binding domain, RNA-dependent RNA polymerase, and Spike receptor binding domain target through in silico screening of 63 phytomolecules from six different Ayurveda medicinal plants. The phytomolecules and SARS-CoV-2 proteins were taken from public domain databases such as PubChem and RCSB Protein Data Bank. For in silico screening, the molecular interactions, binding energy, and ADMET properties were investigated. The structure-based molecular docking reveals some molecules' greater affinity towards the target than the co-crystal ligand. Our results show that tannic acid, cyanidin-3-rutinoside, zeaxanthin, and carbolactone are phytomolecules capable of inhibiting SARS-CoV-2 target proteins in the least energy conformations. Tannic acid had the least binding energy of -8.8 kcal/mol, which is better than the binding energy of its corresponding co-crystal ligand (-7.5 kcal/mol) against 3 CL protease. Also, it has shown the least binding energy of -9.9 kcal/mol with a more significant number of conventional hydrogen bond interactions against the RdRp target. Cyanidin-3-rutinoside showed binding energy values of -8.8 and -7.6 kcal/mol against Main protease and Papain-like protease, respectively. Zeaxanthin was the top candidate in the N protein RBD with a binding score of - 8.4 kcal/mol, which is slightly better when compared to a co-crystal ligand (-8.2 kcal/mol). In the spike, carbolactone was the suitable candidate with the binding energy of -7.2 kcal/mol and formed a conventional hydrogen bond and two hydrophobic interactions. The best binding affinity-scored phytomolecules were selected for the MD simulations studies. The present in silico screening study suggested that active phytomolecules from medicinal plants could inhibit SARS-CoV-2 targets. The elite docked compounds with drug-like properties have a harmless ADMET profile, which may help to develop promising COVID-19 inhibitors.

In silico exploration of phenolics as modulators of penicillin binding protein (PBP) 2× of Streptococcus pneumoniae

Infections caused by multidrug-resistant Streptococcus pneumoniae remain the leading cause of pneumonia-related deaths in children < 5 years globally, and mutations in penicillin-binding protein (PBP) 2 × have been identified as the major cause of resistance in the organism to beta-lactams. Thus, the development of new modulators with enhanced binding of PBP2x is highly encouraged. In this study, phenolics, due to their reported antibacterial activities, were screened against the active site of PBP2x using structure-based pharmacophore and molecular docking techniques, and the ability of the top-hit phenolics to inhibit the active and allosteric sites of PBP2x was refined through 120 ns molecular dynamic simulation. Except for gallocatechin gallate and lysidicichin, respectively, at the active and allosteric sites of PBP2x, the top-hit phenolics had higher negative binding free energy (ΔGbind) than amoxicillin [active site (− 19.23 kcal/mol), allosteric site (− 33.75 kcal/mol)]. Although silicristin had the best broad-spectrum effects at the active (− 38.41 kcal/mol) and allosteric (− 50.54 kcal/mol) sites of PBP2x, the high thermodynamic entropy (4.90 Å) of the resulting complex might suggest the need for its possible structural refinement for enhanced potency. Interestingly, silicristin had a predicted synthetic feasibility score of < 5 and quantum calculations using the DFT B3LYP/6-31G+ (dp) revealed that silicristin is less stable and more reactive than amoxicillin. These findings point to the possible benefits of the top-hit phenolics, and most especially silicristin, in the direct and synergistic treatment of infections caused by S. pneumoniae. Accordingly, silicristin is currently the subject of further confirmatory in vitro research.

In silico Identification of HDAC Inhibitors for Multiple Myeloma: A Structure-based Virtual Screening, Drug Likeness, ADMET Profiling, Molecular Docking, and Molecular Dynamics Simulation Study

Background: Multiple myeloma (MM) is a hematological malignancy of plasma cells that produce a monoclonal immunoglobulin protein. Despite significant advances in the treatment of MM, currently available therapies are associated with toxicity and resistance. As a result, there is an increasing demand for novel, effective therapeutics. Inhibition of histone deacetylases (HDACs) is emerging as a potential method for treating cancer. HDAC6 is one of 18 different HDAC isoforms that regulate tubulin lysine 40 and function in the microtubule network. HDAC6 participates in tumorigenesis and metastasis through protein ubiquitination, tubulin, and Hsp90. Several studies have found that inhibiting HDAC6 causes AKT and ERK dephosphorylation, which leads to decreased cell proliferation and promotes cancer cell death via the PI3K/AKT and MAPK/ERK signaling pathways. Objective: The objective of this study is to target HDAC6 and identify potent inhibitors for the treatment of multiple myeloma by employing computer-aided drug design. Materials and Methods: A total of 199,611,439 molecules from five different chemical databases, such as CHEMBL25, ChemSpace, Mcule, MolPort, and ZINC, have been screened against HDAC6 by structure- based virtual screening, followed by filtering for various drug-likeness, ADME, toxicity, consensus molecular docking, and 100 ns MD simulation. Results: Our research work resulted in three molecules that have shown strong binding affinity (CHEMBL2425964 -9.99 kcal/mol, CHEMBL2425966 -9.89 kcal/mol, and CSC067477144 -9.86 kcal/mol) at the active site HDAC6, along with effective ADME properties, low toxicity, and high stability. Inhibiting HDAC6 with these identified molecules will induce AKT and ERK dephosphorylation linked to reduced cell proliferation and promote cancer cell death. Conclusion: CHEMBL2425964, CHEMBL2425966, and CSC067477144 could be effective against multiple myeloma.

Identification of Potential Inhibitors of the SARS-CoV-2 NSP13 Helicase via Structure-Based Ligand Design, Molecular Docking and Nonequilibrium Alchemical Simulations.

We have assembled a computational pipeline based on virtual screening, docking techniques, and nonequilibrium molecular dynamics simulations, with the goal of identifying possible inhibitors of the SARS-CoV-2 NSP13 helicase, catalyzing by ATP hydrolysis the unwinding of double or single-stranded RNA in the viral replication process inside the host cell. The druggable sites for broad-spectrum inhibitors are represented by the RNA binding sites at the 5' entrance and 3' exit of the central channel, a structural motif that is highly conserved across coronaviruses. Potential binders were first generated using structure-based ligand techniques. Their potency was estimated by using four popular docking scoring functions. Common docking hits for NSP13 were finally tested using advanced nonequilibrium alchemical techniques for binding free energy calculations on a high-performing parallel cluster. Four potential NSP13 inhibitors with potency from submicrimolar to nanomolar were finally identified.

Abstract 6272: Loss of SIRT6 diverts TGF-β signaling to promote liver cancer

Abstract Background: In the US, ~35% of individuals on the waitlist for a liver transplant who had hepatocellular cancer (HCC) also had metabolic dysfunction-associated steatohepatitis (MASH). Yet, key signaling mechanisms controlling cellular transformation in this obesity-driven HCC remain only partially understood. SIRT6 is NAD+-dependent protein deacetylases that limits the activity of the lipogenic transcription factors SREBP1/2. Reduced SIRT6 is associated with human fatty liver and cancer. However, SIRT6 also limits liver fibrosis by inhibiting TGF-β signaling through the deacetylation of SMAD2 and SMAD3. We found that Smad3 driven TGF-β signaling is modulated by βII-Spectrin (encoded by SPTBN1), and that a hot mutation in HCC- D1089Y encompasses the SPTBN1-SMAD3 binding site (Gastro 2018; 154:195-210). In stressed hepatocytes, βII-Spectrin promotes lipogenesis through interactions between caspase-cleaved forms of both SREBP1 and βII-Spectrin (Sci Transl Med 2021;13:624). Liver-specific knockout of βII-Spectrin (LSKO) blocks fatty liver and HCC. Here we explored the extent of βII-spectrin-D1089Y regulation of SIRT6 expression through SMAD3, and the combined roles of SIRT6 and SPTBN1 in human HCC. Methods: We generated a SPTBN1-D1089Y mutant mouse and examined SIRT6 in the SPTBN1-D1089Y mutant mouse liver tissues and MEFs. TGF-β/Smad3 pathway activity was analyzed in SPTBN1-D1089Y mutant MEFs via western blot and RNA-seq. SIRT6 expression was examined in mouse MAFLD/MASH (GSE156059). Structure-based molecular docking simulations using three-dimensional structural coordinates of human SIRT6 (ID: Q8N6T7) and β2SP (ID: Q01082) from the AlphaFold database, followed by interactions between SIRT6 and SPTBN1. Results: SIRT6 levels are decreased in SPTBN1-D1089Y mutant liver cancer cell line and mouse liver tissues. TGF-β/SMAD3 pathway activity was altered in SPTBN1-D1089Y mutant MEF cells with decreased phospho-SMAD3 levels. The structural model predicted specific interactions between human SPTBN1 and SIRT6. Overexpression of Flag-SIRT6 reduced βII-Spectrin acetylation in HepG2 cells. TGF-β induced SMAD3, βII-Spectrin coimmunoprecipitation with SIRT6. SIRT6 knockdown increases nSREBP1, SCD1 (encoded by a nSREBP1 target gene), and βII-Spectrin in HepG2 cells. SIRT6 RNA expression is significantly decreased in liver macrophages from mice with NAFLD/NASH. Conclusions: Our data indicated a SMAD dependent as well as a SMAD3 independent role of SPTBN1 in regulating SIRT6. βII-spectrin-D1089Y together with SIRT6 deficiency may increase the susceptibility to MASH/HCC via regulating the crosstalk between macrophage and hepatocytes or regulating MASH and HCC. Citation Format: Kazufumi Ohshiro, Xiyan Xiang, Xiaochun Yang, Krishanu Bhowmick, Mohammad I. Hassan, Taj Mohammad, Lopa Mishra. Loss of SIRT6 diverts TGF-β signaling to promote liver cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6272.

SGPocket: A New Graph Convolutional Neural Network for Ligand-protein Binding Site Prediction.

Drug research is a long process, taking more than 10 years and requiring considerable financial resources. Therefore, researchers and industrials aim to reduce time and cost. Thus, they use computational simulations like molecular docking to explore huge databases of compounds and extract the most promising ones for further tests. Structure-based molecular docking is a complex process mixing surface exploration and energy computation to find the minimal free energy of binding corresponding to the best interaction location. Our work is developed in the ligand-protein context, where ligands are small compounds like drugs. In most cases, no information is known about where on the protein surface the ligand will bind. Thus, the whole protein surface must be explored, which takes a huge amount of time. We have developed SGPocket (meaning Spherical Graph Pocket), a binding site prediction method. Our method allows us to reduce the explored protein surface using deep learning without any information about a ligand. SGPocket uses the spherical graph convolutional operator working on a spherical relative positioning of amino acids in the protein. Then, a final step of clustering extracts the binding sites. Tested and compared (with well-known binding site prediction methods) on a hand-made dataset, our method performed well and can reduce the docking computing time. Thus, SGPocket allows the reduction of the exploration surface in the molecular docking process by restricting the simulation only to the site(s) predicted to be interesting.

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.

Discovery of dual kinase inhibitors targeting VEGFR2 and FAK: structure-based pharmacophore modeling, virtual screening, and molecular docking studies

VEGFR2 and FAK signaling pathways are interconnected and have synergistic effects on tumor angiogenesis, growth, and metastasis. Thus, instead of the conventional targeting of each of these proteins individually with a specific inhibitor, the present work aimed to discover novel dual inhibitors targeting both VEGFR2 and FAK exploiting their association. To this end, receptor-based pharmacophore modeling technique was opted to generate 3D pharmacophore models for VEGFR2 and FAK type II kinase inhibitors. The generated pharmacophore models were validated by assessing their ability to discriminate between active and decoy compounds in a pre-compiled test set of VEGFR2 and FAK active compounds and decoys. ZINCPharmer web tool was then used to screen the ZINC database purchasable subset using the validated pharmacophore models retrieving 42,616 hits for VEGFR2 and 28,475 hits for FAK. Subsequently, they were filtered using various filters leaving 13,023 and 6,832 survived compounds for VEGFR2 and FAK, respectively, with 124 common compounds. Based on molecular docking simulations, thirteen compounds were found to satisfy all necessary interactions with VEGFR2 and FAK kinase domains. Thus, they are predicted to have a possible dual VEGFR2/FAK inhibitory activity. Finally, SwissADME web tool showed that compound ZINC09875266 is not only promising in terms of binding pattern to our target kinases, but also in terms of pharmacokinetic properties.

Structure-based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation, and Metabolic Reactivity studies of Quinazoline Derivatives for their Anti-EGFR Activity Against Tumor Angiogenesis

Epidermal growth factor receptor (EGFR/HER-1) and its role in tumor development and progression through the mechanism of tumor angiogenesis is prevalent in non-small lung cancer, head and neck cancer, cholangiocarcinoma & glioblastoma. Previous treatments targeting the oncogenic activity of EGFR's kinase domain have been hindered by acquired mutational resistance and side effects from existing drugs like erlotinib, highlighting the need for new EGFR inhibitors through structure- based drug designing The research aims to develop novel quinazoline derivatives through structure-based virtual screening, molecular docking, and molecular dynamics simulation to potentially interact with EGFR's kinase domain and impede tumor angiogenic phenomenon. Quinazoline derivatives were retrieved and filtered from the PubChem database using structure- based virtual screening and the Lipinski rule of five drug-likeness studies. Molecular docking-based virtual screening methods and molecular dynamics simulation were then carried out to identify top leads A total of 1000 quinazoline derivatives were retrieved, with 671 compounds possessing drug-- like properties after applying Lipinski filters. Further filtration using ADME and toxicity filters yielded 28 compounds with good pharmacokinetic profiles. Docking-based virtual screening identified seven compounds with better binding scores than the control drug, dacomitinib. After cross-checking binding scores, three top compounds QU524, QU571, and QU297 were selected for molecular dynamics simulation study of 100 ns interval using Desmond module of Schrodinger maestro to understand their conformational stability The research results showed that the selected quinazoline leads exhibited better binding affinity and conformational stability than the control drug, erlotinib. These compounds also had good pharmacokinetic and pharmacodynamic profiles and did not violate Lipinski’s rule of five limits. The findings suggest that these leads have the potential to target EGFR's kinase domain and inhibit the EGFR-associated phenomenon of tumor angiogenesis.

Computer-aided drug design of novel nirmatrelvir analogs inhibiting main protease of Coronavirus SARS-CoV-2

A computer-aided drug design of new derivatives of nirmatrelvir, an orally active inhibitor of the main-protease (Mpro) of the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2), was performed to identify its analogues with a higher antiviral potency. The following workflow was used: first, an evolutionary library composed of 1,866 analogues was generated starting from a parent nirmatrelvir scaffold and going through small mutation, fitness scoring, ranking, and selection. Second, the generated library was preprocessed and filtered against a 3-D pharmacophore model of nirmatrelvir built from its X-ray structure in a co-crystalized complex with the Mpro enzyme, allowing us to reduce the chemical space to 32 active analogues. Third, structure-based molecular docking against two different enzyme structures further ranked these active candidates, so that up to eight better-binding analogs were identified. The selected hit-analogues target the Mpro enzymes of SARS-CoV-2 with a higher binding affinity than a parent nirmatrelvir. The main structural modifications that increase the overall inhibitory affinity are identified at the azabicyclo[3.1.0] hexane and 2-oxopyrrolidine fragments. A characteristic structural feature of the inhibitor binding with the Mpro active centre is the similar location of the trifluoroacetylamino fragment, which is observed for most hit-analogues. The suggested workflow of the computer-aided rational design of new antiviral noncovalent inhibitors based on the scaffold of approved drugs is a promising, extremely low-cost, and time-efficient approach for the development of new potential pharmaceutical ingredients for the treatment of Coronavirus Disease 2019.

Computational studies of tyrosinase inhibitors.

Computational studies have significantly advanced the understanding of tyrosinase (TYR) function, mechanism, and inhibition, accelerating the development of more effective and selective inhibitors. This chapter provides an overview of in silico studies on TYR inhibitors, emphasizing key inhibitory chemotypes and the main residues involved in ligand-target interactions. The chapter discusses tools applied in the context of TYR inhibitor development, e.g., structure-based virtual screening, molecular docking, artificial intelligence, and machine learning algorithms.