Drug Design Methods Research Articles

Special issue devoted to the IAPC-10 Meeting: Joint World Conferences on Physico-Chemical Methods in Drug Discovery and Development and on ADMET and DMPK

The present issue of ADMET and DMPK is dedicated to the IAPC-10 Meeting, which was organized as a joint event consisting of 10th World Conference on Physico-Chemical Methods in Drug Discovery and Develop¬ment and 6th World Conference on ADMET and DMPK. The meeting took place in the University of Belgrade Rectorate building, Belgrade, Republic of Serbia, September 4-6, 2023. IAPC meetings are organized as annual events in alternating European and East Asia locations. The topics covered the most advanced directions and new achievements in physico-chemical methods, which underlie almost all instrumental techniques used in the research processes in drug discovery and pharmaceutical development. Experimental determination of ADMET properties through the in vitro and in vivo assays was discussed as well as modern theoretical methods for computer-aided drug design. Ten sessions were organized, among which two special sessions: 1) Special Session on Solubility of Multi-Component Solids (Salts and Cocrystals), organized and co-chaired by Kiyohiko Sugano and Alex Avdeef, and 2) Special Session on the ADME properties and toxicity prediction by HPLC, organized and chaired by Klara Valko. Editorial

A head-to-head comparison of MM/PBSA and MM/GBSA in predicting binding affinities for the CB1 cannabinoid ligands.

The substantial increase in the number of active and inactive-state CB1 receptor experimental structures has provided opportunities for CB1 drug discovery using various structure-based drug design methods, including the popular end-point methods for predicting binding free energies-Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA). In this study, we have therefore evaluated the performance of MM/PBSA and MM/GBSA in calculating binding free energies for CB1 receptor. Additionally, with both MM/PBSA and MM/GBSA being known for their highly individualized performance, we have evaluated the effects of various simulation parameters including the use of energy minimized structures, choice of solute dielectric constant, inclusion of entropy, and the effects of the five GB models. Generally, MM/GBSA provided higher correlations than MM/PBSA (rMM/GBSA = 0.433 - 0.652 vs. rMM/PBSA = 0.100 - 0.486) regardless of the simulation parameters, while also offering faster calculations. Improved correlations were observed with the use of molecular dynamics ensembles compared with energyminimized structures and larger solute dielectric constants. Incorporation of entropic terms led to unfavorable results for both MM/PBSA and MM/GBSA for a majority of the dataset, while the evaluation of the various GB models exerted a varying effect on both the datasets. The findings obtained in this study demonstrate the utility of MM/PBSA and MM/GBSA in predicting binding free energies for the CB1 receptor, hence providing a useful benchmark for their applicability in the endocannabinoid system as well as other G protein-coupled receptors. The study utilized the docked dataset (Induced Fit Docking with Glide XP scoring function) from Loo et al., consisting of 46 ligands-23 agonists and 23 antagonists. The equilibrated structures from Loo et al. were subjected to 30ns production simulations using GROMACS 2018 at 300K and 1atm with the velocity rescaling thermostat and the Parinello-Rahman barostat. AMBER ff99SB*-ILDN was used for the proteins, General Amber Force Field (GAFF) was used for the ligands, and Slipids parameters were used for lipids. MM/PBSA and MM/GBSA binding free energies were then calculated using gmx_MMPBSA. The solute dielectric constant was varied between 1, 2, and 4 to study the effect of different solute dielectric constants on the performance of MM/PB(GB)SA. The effect of entropy on MM/PB(GB)SA binding free energies was evaluated using the interaction entropy module implemented in gmx_MMPBSA. Five GB models, GBHCT, GBOBC1, GBOBC2, GBNeck, and GBNeck2, were evaluated to study the effect of the choice of GB models in the performance of MM/GBSA. Pearson correlation coefficients were used to measure the correlation between experimental and predicted binding free energies.

Quantum mechanics/molecular mechanics (QM/MM) methods in drug design: a comprehensive review of development and applications

The integration of quantum mechanics (QM) and molecular mechanics (MM) methods, known as QM/MM, has emerged as a powerful computational approach in drug design. This hybrid technique combines the accuracy of QM calculations for the reactive region with the computational efficiency of MM methods for the surrounding environment. QM/MM methods have proven invaluable in studying chemical reactions, exploring enzyme mechanisms, and investigating ligand-protein interactions, all of which are crucial for rational drug design. This review provides a comprehensive overview of the QM/MM methodology, its theoretical foundations, and its applications in various aspects of drug design. We discussed the key components, such as QM and MM region partitioning, link atom schemes, and boundary treatments. Additionally, we highlight recent advancements and challenges in QM/MM methods, including polarizable force fields, implicit solvation models, and enhanced sampling techniques. Furthermore, we present illustrative examples showcasing the successful application of QM/MM methods in lead optimization, virtual screening, and the elucidation of biochemical mechanisms relevant to drug targets. Finally, we provide perspectives on future developments and the potential impact of QM/MM methods on the drug discovery pipeline.

Computer-Aided Design of VEGFR-2 Inhibitors as Anticancer Agents: A Review.

Due to its intricate molecular and structural characteristics, vascular endothelial growth factor receptor 2 (VEGFR-2) is essential for the development of new blood vessels in various pathological processes and conditions, especially in cancers. VEGFR-2 inhibitors have demonstrated significant anticancer effects by blocking many signaling pathways linked to tumor growth, metastasis, and angiogenesis. Several small compounds, including the well-tolerated sunitinib and sorafenib, have been approved as VEGFR-2 inhibitors. However, the widespread side effects linked to these VEGFR-2 inhibitors-hypertension, epistaxis, proteinuria, and upper respiratory infection-motivate researchers to search for new VEGFR-2 inhibitors with better pharmacokinetic profiles. The key molecular interactions required for the interaction of the small molecules with the protein target to produce the desired pharmacological effects are identified using computer-aided drug design (CADD) methods such as pharmacophore and QSAR modeling, structure-based virtual screening, molecular docking, molecular dynamics (MD) simulation coupled with MM/PB(GB)SA, and other computational strategies. This review discusses the applications of these methods for VEGFR-2 inhibitor design. Future VEGFR-2 inhibitor designs may be influenced by this review, which focuses on the current trends of using multiple screening layers to design better inhibitors.

The In vitro evaluation of in silico-designed synthetic peptides AKVUAM-1 and AKVUAM-2 on human lung fibroblast cell line MRC5 and Mycobacterium tuberculosis isolates

Tuberculosis is a major global health problem caused by Mycobacterium tuberculosis and the increase in drug resistance is driving the need for new treatments. Today, various approaches are being applied in the development of drugs for the treatment of tuberculosis. Computer-aided drug design (CADD) enables the prediction of pharmacological efficacy for potential drug molecules during the design process. Thus, new therapeutic compounds can be developed that are more potent, less toxic and have fewer side effects than existing drugs. In this study, we investigated the in vitro activities of AKVUAM-1 and AKVUAM-2 synthetic peptides designed in silico by computer-aided drug design method to inhibit the interaction between M. tuberculosis outer membrane protein Cpn T and macrophage surface receptor CR-1 and Surfactant D protein. Notably, these synthetic peptides do not show cytotoxic effect on normal lung tissue and do not kill M. tuberculosis directly. The MIC values for AKVUAM-1 were higher than 512 μg/ml for all bacterial strains except IST-16 strain (128 μg/ml). According to our results, AKVUAM-1 and AKVUAM-2 synthetic peptides have the potential to be successful candidates for investigating their potential to block macrophage entry of bacilli as targeted.

Discovery of Small Molecule Inhibitors Targeting CTNNB1 (β-catenin) for Endometrial cancer: Employing 3D QSAR, Drug-Likeness Assessment, ADMET Predictions, Molecular Docking and Simulation.

Endometrial carcinoma (EC) is a type of cancer that originates in the lining of the uterus, known as the endometrium. It is associated with various treatment options such as surgery, radiation therapy, chemotherapy, and hormone therapy, each presenting unique challenges and limitations. Beta-catenin, a protein involved in the development and progression of several cancers, including EC, plays a crucial role. Abnormal beta-catenin signaling is often linked to the emergence of specific EC subtypes, affecting tumor growth and invasion. The study's objective is to identify compounds targeting the beta-catenin protein for treating endometrial cancer (EC) using in silico drug design. Our approach includes molecular docking to evaluate binding affinities, ADME profiling for pharmacokinetic properties, toxicity assessments, and molecular dynamics simulations to assess compound stability and interactions. Approximately one thousand anti-cancer phytochemicals were sourced from PubChem and subjected to molecular docking simulations against the beta-catenin protein. The compounds were evaluated based on their binding affinities, with the top five selected for further analysis. These five molecules underwent toxicity and ADME profiling. The Prediction of Activity Spectra for Substances (PASS) tool was used to identify compounds targeting CTNNB1. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were employed to establish quantitative structure-activity relationship (QSAR) models for the five CTNNB1 antagonist molecules. The selected five compounds, namely Pazopanib, Binimetinib, Telatinib, 4-(2,3-Dihydrobenzo[ b][1,4]dioxin-6-yl)-3-((5-nitrothiazol-2-yl)thio)-1H-1,2,4-triazol-5(4H)-one, and Ribavirin, demonstrated efficacy against CTNN1. MD simulations of the docked complexes confirmed the stability of these drugs in binding to the target protein. All five molecules showed promising safety and effectiveness profiles according to their ADME and toxicity evaluations. Through a comprehensive screening process employing in silico drug design methods, this study successfully identified five potential human anticancer drug candidates targeting the beta-catenin protein. These findings offer a foundation for further experimental validation and development towards the treatment of EC.

Structure based exploration of mitochondrial alpha carbonic anhydrase inhibitors as potential leads for anti-obesity drug development.

Obesity has emerged as a major health challenge globally in the last two decades. Dysregulated fatty acid metabolism and de novo lipogenesis are prime causes for obesity development which ultimately trigger other co-morbid pathological conditions thereby risking life longevity. Fatty acid metabolism and de novo lipogenesis involve several biochemical steps both in cytosol and mitochondria. Reportedly, the high catalytically active mitochondrial carbonic anhydrases (CAVA/CAVB) regulate the intercellular depot of bicarbonate ions and catalyze the rapid carboxylation of pyruvate and acetyl-co-A to acetyl-co-A and malonate respectively, which are the precursors of fatty acid synthesis and lipogenesis. Several in vitro and in vivo investigations indicate inhibition of mitochondrial carbonic anhydrase isoforms interfere in the functioning of pyruvate, fatty acid and succinate pathways. Targeting of mitochondrial carbonic anhydrase isoforms (CAVA/CAVB) could thereby modulate gluconeogenetic as well as lipogenetic pathways and pave way for designing of novel leads in the development pipeline of anti-obesity medications. The present review unveils a diverse chemical space including synthetic sulphonamides, sulphamates, sulfamides and many natural bioactive molecules which selectively inhibit the mitochondrial isoform CAVA/CAVB with an emphasis on major state-of-art drug design strategies. More than 60% similarity in the structural framework of the carbonic anhydrase isoforms has converged the drug design methods towards the development of isoform selective chemotypes. While the benzene sulphonamide derivatives selectively inhibit CAVA/CAVB in low nanomolar ranges depending on the substitutions on the phenyl ring, the sulpamates and sulpamides potently inhibit CAVB. The virtual screening and drug repurposing methods have also explored many non-sulphonamide chemical scaffolds which can potently inhibit CAVA. The review could pave way for the development of novel and effective anti-obesity drugs which can modulate the energy metabolism.

Investigating the Antimicrobial Properties of Essential Oil Constituents and Their Mode of Action.

Essential oils (EOs) and plant extracts, rich in beneficial chemical compounds, have diverse applications in medicine, food, cosmetics, and agriculture. This study investigates the antibacterial activity of nine essential oil constituents (EOCs) against Escherichia coli, focusing on the effects of treatment pH and biosynthetic requirements. The impact of EOCs on bacterial inactivation in E. coli strains was examined using both nonselective and selective culture media. Computer-assisted drug design (CADD) methods were employed to identify critical binding sites and predict the main binding modes of ligands to proteins. The EOCs, including citral, α-terpinyl acetate, α-terpineol, and linalool, demonstrated significant bacterial inactivation, particularly under acidic conditions. This study revealed that EOCs have an effect on the presence of sublethal damage to both the cytoplasmic membrane and the outer membrane in Gram-negative bacteria. Adding penicillin G to the repair medium prevents the recovery of sublethal injuries in E. coli treated with α-terpinyl acetate, α-terpineol, linalool, and citral, indicating that peptidoglycan synthesis is essential for recovering from these injuries. However, penicillin G did not hinder the recovery process of most sublethally injured cells treated with the other assessed EOCs. Molecular docking studies revealed the favorable binding interactions of α-terpinyl acetate, α-terpineol, linalool, and citral with the β-lactamase enzyme Toho-1, indicating their potential as effective antibacterial agents. The findings suggest that EOCs could serve as viable alternatives to synthetic preservatives, offering new strategies for combating antibiotic-resistant bacteria.

Novel Prodrug Strategies for the Treatment of Tuberculosis.

The emergence of drug-resistant strains of Mycobacterium tuberculosis (M.tb), the causative agent of tuberculosis, is on the rise and increasing antimicrobial resistance is a global threat. This phenomenon necessitates new drug design methods such as a prodrug strategy to develop novel antitubercular agents. The prodrug strategy is a viable and useful means to improve the absorption, distribution, metabolism, excretion and toxicity (ADMET) profiles of pharmacologically active agents. Granulomas are a pathological hallmark of M.tb infection and bear a remarkable resemblance to the tumour microenvironment, including regions of hypoxia. The hypoxic environment observed in the two structures offer an exceptional opportunity to deliver antitubercular agents selectively in a similar manner to hypoxia activated prodrugs in cancer therapy. Nitroimidazoles have been studied extensively as bioactivated prodrugs of cancer, and their suitability as substrates for mammalian reductases highlight their huge potential. This review will discuss the mechanism of action and resistance mechanisms of the current prodrugs used for the treatment of tuberculosis. It will also highlight the potential advantages and challenges of using hypoxia activated prodrugs as a viable strategy to target latent M.tb in hypoxic regions of granulomas.

Molecular docking of Portulaca oleracea L. alkaloid compounds for potential antidiabetic activity through Cdk5 protein expression

Cyclin-dependent kinase 5 (Cdk5) is a serine/threonine protein kinase, which forms the p35 or p39 active complex that is expressed predominantly in neurons. Cdk5 plays an important role in physiological functions in non-neural cells such as glucose-stimulated insulin secretion in pancreatic cells. This study aims to explore and determine the effectiveness of the chemical compound Oleracein E (OL-E) in the Portulaca oleracea (PO) as an anti- diabetic when expressed on Cdk5 protein. The test was carried out in silico with a computer- aided drug design method in which the molecular docking process used software such as Pyrx 0.8. The results of this study indicate that OL-E has the potential as an antidiabetic by inhibiting Cdk5 and has 27.65 percent better effectiveness than metformin in inhibiting Cdk5.

A Novel One-Pot Three-Component Approach to Orthoaminocarbonitrile Tetrahydronaphthalenes Using Triethylamine (Et_3N) as a Highly Efficient and Homogeneous Catalyst Under Mild Conditions and Investigating Its Anti-cancer Properties Through Molecular Docking Studies and Calculations

An efficient and environmentally friendly method for the one-pot synthesis of ortho-aminocarbonitrile tetrahydronaphthalenes has been developed in the presence of triethylamine (Et3N) as a homogeneous catalyst. The multicomponent reactions of benzaldehydes, cyclohexanone and malononitrile were carried out under mild conditions to obtain some ortho-aminocarbonitrile tetrahydronaphthalene derivatives. A broad range of structurally diverse benzaldehydes were applied successfully, and corresponding products (4A-L) were obtained in good to excellent yields (87-98%) in very short times (10-25 minutes). The present approach provides several advantages including simple workup, high yields, very mild reaction conditions, short reaction times, little catalyst loading and not requiring specialized equipment. Furthermore, with the help of computational chemistry and drug design methods, the anti-cancer properties of these compounds were studied and investigated. All the synthesized compounds bind to an agonist at the active site of the 3A8P protein, which leads to the inactivation of this protein and produces beneficial effects during cancer treatment. In synthesized compounds, the ligands establish hydrogen bonds with leucine A:728 residues through nitrogen, which has a very special and vital role in biological sciences and pharmaceutical connections. In this study, it was found that these compounds have the potential to become an oral anti-cancer drug.

New Strategies for Responding to SARS-CoV-2: The Present and Future of Dual-Target Drugs.

The 2019 coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in millions of deaths, posing a serious threat to public health and safety. Rapid mutations of SARS-CoV-2 and complex interactions among multiple targets during infection pose a risk of expiry for small molecule inhibitors. This suggests that the traditional concept of "one bug, one drug" could be ineffective in dealing with the coronavirus. The dual-target drug strategy is expected to be the key to ending coronavirus infections. However, the lack of design method and improper combination of dual-targets poses obstacle to the discovery of new dual-target drugs. In this Perspective, we summarized the profiles concerning drug design methods, structure-activity relationships, and pharmacological parameters of dual-target drugs for the treatment of COVID-19. Importantly, we underscored how target combination and rational drug design illuminate the development of dual-target drugs for SARS-CoV-2.

A brief review on quantum computing based drug design

AbstractDesign and development of new drug molecules are essential for the survival of human society. New drugs are designed for therapeutic purposes to combat new diseases. Besides treating new diseases, new drug development is also needed to treat pre‐existing diseases more effectively and reduce the existing drugs' side effects. The design of drugs involves several steps, from the discovery of the drug molecule to its commercialization in the market. One of the most critical steps in drug design is to find the molecular interactions between the target (infected) molecule and the drug molecule. Several complex chemical equations need to be solved to determine the molecular interactions. In the late 20th Century, the advancement of computational technologies has made the solution of chemical equations relatively easier and faster. Moreover, the design of drug molecules involves multi‐criteria optimization. Classical computational methodologies have been used for drug design since the end of the 20th Century. However, nowadays, more advanced computational methodologies are inevitable in designing drugs for new diseases and drugs with fewer side effects. In this context, the quantum computing paradigm has proved beneficial in drug design due to its advanced computational capabilities. This paper presents a state‐of‐the‐art comprehensive review of the quantum computing‐based methodologies involved in drug design. A comparative study is made about the different quantum‐aided drug design methods, stating each methodology's merits and demerits. The review work presented in this manuscript will help new researchers assess the present state‐of‐the‐art concept of quantum‐based drug design.This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Computational Intelligence Application Areas > Health Care

Novel 1,3,4-oxadiazole derivatives of naproxen targeting EGFR: Synthesis, molecular docking studies, and cytotoxic evaluation.

The close association between inflammation and cancer inspired the synthesis of a series of 1,3,4-oxadiazole derivatives (compounds H4-A-F) of 6-methoxynaphtalene. The chemical structures of the new compounds were validated utilizing Fourier-transform infrared, proton nuclear magnetic resonance, and carbon-13 nuclear magnetic resonance spectroscopic techniques and CHN analysis. Computer-aided drug design methods were used to predict the compounds biological target, ADMET properties, toxicity, and to evaluate the molecular similarities between the design compounds and erlotinib, a standard epidermal growth factor receptor (EGFR) inhibitor. The antiproliferative effects of the new compounds were evaluated by the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide assay, cell cycle analysis, apoptosis detection by microscopy, quantitative reverse transcription-polymerase chain reaction, and immunoblotting, and EGFR enzyme inhibition assay. In silico analysis of the new oxadiazole derivatives indicated that these compounds target EGFR, and that compounds H4-A, H4-B, H4-C, and H4-E show similar molecular properties to erlotinib. Additionally, the results indicated that none of the synthesized compounds are carcinogenic, and that compounds H4-A, H4-C, and H4-F are nontoxic. Compound H4-A showed the best-fit score against EGFR pharmacophore model, however, the in vitro studies indicated that compound H4-C was the most cytotoxic. Compound H4-C caused cytotoxicity in HCT-116 colorectal cancer cells by inducing both apoptosis and necrosis. Furthermore, compounds H4-D, H4-C, and H4-B had potent inhibitory effect on EGFR tyrosine kinase that was comparable to erlotinib. The findings of this inquiry offer a basis for further investigation into the differences between the synthesized compounds and erlotinib. However, additional testing will be needed to assess all of these differences and to identify the most promising compound for further research.

Advances in Computational Methods for Drug Design: A Revolution in Pharmaceutical Development

Computational methods of drug design involve the use of various software and algorithms to predict the properties of drug molecules, screen for potential targets, and optimize drug candidates for therapeutic efficacy, it lowering the cost of drug research and development time. The discovery and development of a novel medicine is a lengthy, complex, expensive, and high-risk process that has no commercial counterpart. CADD has previously been used to uncover drugs that have gone through clinical trials and become innovative medicines for many ailments. CADD approaches are widely divided into two categories: structure-based drug design (SBDD) and ligand-based drug design (LBDD). SBDD is used when the three-dimensional structures of target proteins are available, while LBDD design is used in the absence of receptor 3D information and relies on knowledge of molecules that bind to the biological target of interest. Some applications in CADD are Lead Optimization, Virtual screening (VS), ADMET prediction, and toxicity prediction. Medicinal chemists use a variety of computational approaches to modify the chemical structure of a compound to maximize its in vitro activity, drug discovery is driven by the idea that a ligand with higher binding affinity to a target should be more efficacious than that with lower binding affinity to the same target. Target flexibility is one of the key issues that still need to be resolved in drug discovery. The majority of molecular docking tools give the ligand high flexibility, but they fix or give the protein's residues close to or inside the active site only limited flexibility. It is very difficult to provide complete molecular flexibility to the protein as this increases the space and time complexity of the computation. In conclusion, computational methods have revolutionized the field of drug design by enabling faster, more cost-effective, and more efficient drug discovery

Computational Simulation Study of Potential Inhibition of c-Met Kinase Receptor by Phenoxy pyridine Derivatives: Based on QSAR, Molecular Docking, Molecular Dynamics.

The mesenchymal-epithelial transition factor (c-Met) is a tyrosine kinase receptor protein, and excessive cell transformation can lead to cancer. Therefore, there is an urgent need to develop novel receptor tyrosine kinase inhibitors by inhibiting the activity of c-Met protein. In this study, 41 compounds are selected from the reported literature, and the interactions between phenoxy pyridine derivatives and tumor-associated proteins are systematically investigated using a series of computer-assisted drug design (CADD) methods, aiming to predict potential c-Met inhibitors with high activity. The Topomer CoMFA (q2=0.620, R2=0.837) and HQSAR (q2=0.684, R2=0.877) models demonstrate a high level of robustness. Further internal and external validation assessments show high applicability and accuracy. Based on the results of the Topomer CoMFA model, structural fragments with higher contribution values are identified and randomly combined using a fragment splice technique, result in a total of 20 compounds with predicted activities higher than the template molecules. Molecular docking results show that these compounds have good interactions and van der Waals forces with the target proteins. The results of molecular dynamics and ADMET predictions indicate that compounds Y4, Y5, and Y14 have potential as c-Met inhibitors. Among them, compound Y14 exhibits superior stability with a binding free energy of -165.18 KJ/mol. These studies provide a reference for the future design and development of novel compounds with c-Met inhibitory activity.

The Power of Molecular Dynamics Simulations and Their Applications to Discover Cysteine Protease Inhibitors.

A large family of enzymes with the function of hydrolyzing peptide bonds, called peptidases or cysteine proteases (CPs), are divided into three categories according to the peptide chain involved. CPs catalyze the hydrolysis of amide, ester, thiol ester, and thioester peptide bonds. They can be divided into several groups, such as papain-like (CA), viral chymotrypsin-like CPs (CB), papainlike endopeptidases of RNA viruses (CC), legumain-type caspases (CD), and showing active residues of His, Glu/Asp, Gln, Cys (CE). The catalytic mechanism of CPs is the essential cysteine residue present in the active site. These mechanisms are often studied through computational methods that provide new information about the catalytic mechanism and identify inhibitors. The role of computational methods during drug design and development stages is increasing. Methods in Computer-Aided Drug Design (CADD) accelerate the discovery process, increase the chances of selecting more promising molecules for experimental studies, and can identify critical mechanisms involved in the pathophysiology and molecular pathways of action. Molecular dynamics (MD) simulations are essential in any drug discovery program due to their high capacity for simulating a physiological environment capable of unveiling significant inhibition mechanisms of new compounds against target proteins, especially CPs. Here, a brief approach will be shown on MD simulations and how the studies were applied to identify inhibitors or critical information against cysteine protease from several microorganisms, such as Trypanosoma cruzi (cruzain), Trypanosoma brucei (rhodesain), Plasmodium spp. (falcipain), and SARS-CoV-2 (Mpro). We hope the readers will gain new insights and use our study as a guide for potential compound identifications using MD simulations.

Discovery of Novel Spike Inhibitors against SARS-CoV-2 Infection.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for the current coronavirus disease pandemic. With the rapid evolution of variant strains, finding effective spike protein inhibitors is a logical and critical priority. Angiotensin-converting enzyme 2 (ACE2) has been identified as the functional receptor for SARS-CoV-2 viral entry, and thus related therapeutic approaches associated with the spike protein-ACE2 interaction show a high degree of feasibility for inhibiting viral infection. Our computer-aided drug design (CADD) method meticulously analyzed more than 260,000 compound records from the United States National Cancer Institute (NCI) database, to identify potential spike inhibitors. The spike protein receptor-binding domain (RBD) was chosen as the target protein for our virtual screening process. In cell-based validation, SARS-CoV-2 pseudovirus carrying a reporter gene was utilized to screen for effective compounds. Ultimately, compounds C2, C8, and C10 demonstrated significant antiviral activity against SARS-CoV-2, with estimated EC50 values of 8.8 μM, 6.7 μM, and 7.6 μM, respectively. Using the above compounds as templates, ten derivatives were generated and robust bioassay results revealed that C8.2 (EC50 = 5.9 μM) exhibited the strongest antiviral efficacy. Compounds C8.2 also displayed inhibitory activity against the Omicron variant, with an EC50 of 9.3 μM. Thus, the CADD method successfully discovered lead compounds binding to the spike protein RBD that are capable of inhibiting viral infection.

A theoretical investigation on MgB11N12, GeB11N12, GaB11N12, SiB11N12, AlB11N12, and pristine B12N12 clusters for sensing Memantine and its drug delivery for Alzheimer’s disease

As an important problem, Alzheimer disease has resulted in thousands of deaths in the human society. Thus, researches around drug design and drug delivery methods for treating this illness is crucial. The MgB11N12, GeB11N12, GaB11N12, SiB11N12, AlB11N12 and pristine B12N12 clusters were designed to investigate effective modes of transporting memantine (MMT) nanoparticles as anti-Alzheimer agents.The results showed that among all considered heteroatoms, the Germanium, and Gallium atoms have the highest effect on decreasing the Eg of the system albeit by making the B12N12 clusters, a semiconductor-like nanoparticle. Due to these results, GeB11N12 derivate with the Eg of 2.76182 eV, is a suitable semiconductor cluster. Also, after adsorption of MMT, the Ge nanoparticles enhances Eg to 3.32533 eV, resulting in a strong signal. Thus, between all studied clusters, the GeB11N12, is the best choice to be used as a sensor for detection of MMT residues. Additionally, the energy surface data indicated that the SiB11N12 is a suitable nanoparticle for delivering memantine, better than the pristine B12N12 cluster. We hope this work will be useful for future experimental studies about the adsorption and sensing of MMT drug residues.

Utilizing a structure-based virtual screening approach to discover potential LSD1 inhibitors

BackgroundLysine-specific demethylase 1 (LSD1) is highly expressed in a variety of malignant tumors, rendering it a crucial epigenetic target for anti-tumor therapy. Therefore, the inhibition of LSD1 activity has emerged as a promising innovative therapeutic approach for targeted cancer treatment.MethodsIn our study, we employed innovative structure-based drug design methods to meticulously select compounds from the ZINC15 database. Utilizing virtual docking, we evaluated docking scores and binding modes to identify potential inhibitors. To further validate our findings, we harnessed molecular dynamic simulations and conducted meticulous biochemical experiments to deeply analyze the binding interactions between the protein and compounds.ResultsOur results showcased that ZINC10039815 exhibits an exquisite binding mode with LSD1, fitting perfectly into the active pocket and forming robust interactions with multiple critical residues of the protein.ConclusionsWith its significant inhibitory effect on LSD1 activity, ZINC10039815 emerges as a highly promising candidate for the development of novel LSD1 inhibitors.