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The use of metal compounds such as cisplatin and its derivatives as therapeutic and diagnostic agents against various diseases is well established. Although metallodrugs have been very successful clinically, low therapeutic selectivity and the potential toxicity of the metal compounds mandate the need for a continuous search for more efficient and specific treatments. Hence, the use of computer‐aided drug design (CADD), molecular modelling and theoretical studies in designing, selecting and applying potential drug candidates have become ever more necessary. Among the many computational and theoretical techniques, molecular docking and density functional theory play significant roles in predicting drug activity. Recent methodologies, within the past 5 years, that rely on these techniques to advance the adoption of potential metallodrugs are highlighted and thoroughly discussed. This is because advancements in computing power have led to the wide use of CADD approaches for drug discovery, development and analysis to shorten the time and reduce the costs associated with drug discovery. [ABSTRACT FROM AUTHOR]

The article discusses the creation of the World Wide Approved Drug (WWAD) database, which aims to provide comprehensive information on medicines approved for therapeutic use worldwide. The database collects data from National Medicine Registers (NMRs) and official documentation from medicines regulatory authorities (MRAs) of different countries. The article emphasizes the need for a unified resource that aggregates information on worldwide approved medicines and highlights the challenges in accessing and processing this data. The World Wide Approved Drugs (WWAD) database is a comprehensive resource that provides information on low molecular weight pharmaceutical substances from 71 countries and regions. It includes data on drug names, chemical structures, therapeutic indications, molecular targets, and biological activities. The database can be used for various purposes, including searching for information on approved drugs, drug repositioning, data enrichment, and assessing the novelty of investigated compounds. The WWAD database is freely available for academic use through the Way2Drug platform. This document contains a list of references and citations from various scientific articles and databases related to topics such as computational chemistry, drug discovery, drug repurposing, and regulatory approval of drugs. The references cover a range of subjects including the use of artificial intelligence and machine learning in drug discovery, the development of databases for drug repurposing, and the analysis of drug approvals by regulatory agencies. The document provides a comprehensive list of sources that library patrons can use to further their research on these topics. [Extracted from the article]

Cruzipain (CZP), the major cysteine protease present in T. cruzi, the ethiological agent of Chagas disease, has attracted particular attention as a therapeutic target for the development of targeted covalent inhibitors (TCI). The vast chemical space associated with the enormous molecular diversity feasible to explore by means of modern synthetic approaches allows the design of CZP inhibitors capable of exhibiting not only an efficient enzyme inhibition but also an adequate translation to anti-T. cruzi activity. In this work, a computer-aided design strategy was developed to combinatorially construct and screen large libraries of 1,4-disubstituted 1,2,3-triazole analogues, further identifying a selected set of candidates for advancement towards synthetic and biological activity evaluation stages. In this way, a virtual molecular library comprising more than 75 thousand diverse and synthetically feasible analogues was studied by means of molecular docking and molecular dynamic simulations in the search of potential TCI of CZP, guiding the synthetic efforts towards a subset of 48 candidates. These were synthesized by applying a Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) centered synthetic scheme, resulting in moderate to good yields and leading to the identification of 12 hits selectively inhibiting CZP activity with IC50 in the low micromolar range. Furthermore, four triazole derivatives showed good anti-T. cruzi inhibition when studied at 50 μ M; and Ald-6 excelled for its high antitrypanocidal activity and low cytotoxicity, exhibiting complete in vitro biological activity translation from CZP to T. cruzi. Overall, not only Ald-6 merits further advancement to preclinical in vivo studies, but these findings also shed light on a valuable chemical space where molecular diversity might be explored in the search for efficient triazole-based antichagasic agents. [ABSTRACT FROM AUTHOR]

This research implements a computer-aided formulation development algorithm based on a novel SeDeM-ODT expert system in establishing the design space for paediatric pyridoxine hydrochloride orodispersible tablets (ODTs) using Prosolv® ODTG2, Prosolv® EasyTab SP, and Ludiflash® systems. For each formulation ingredient, expert system-defined preformulation parameter values were experimentally determined according to standardized methods and then normalized to the theoretical radius range [0,10]. Expert diagrams were constructed and the quantitative performance of each ingredient was evaluated using parametric profile index (IPP), flowability (ff'), and compressibility (ffc) functions. The net direct compression capability was quantitatively expressed as the product of expert system reliability and IPP. Direct compression was conducted in an eccentric tablet press and properties were evaluated using weight, dimension, disintegration test, contact angle, tensile strength, x-ray diffraction, and Fourier-transform infrared spectroscopy. The ODTs dissolution profiles were fitted and compared using zero-order, first-order, Hixson-Crowell, and Hopfenberg models. Results of the expert diagram of pyridoxine hydrochloride indicated suboptimal normalized radii values in 8 out of 12 parameters, implying a compromised mechanical zone (ff'=3.61, ffc=2.11). By setting a target ffc for the optimized formulation mix at 5.0, the predicted proportions of the fillers to remedy the direct compression deficits of the drug were computed as 89.00%, 83.23%, and 76.62% for Prosolv® ODTG2 (ffc=5.36), Prosolv® EasyTab SP (ffc=5.58), and Ludiflash® (ffc=5.88), respectively. The produced ODTs were of acceptable target quality, hence the SeDeM-ODT system was considered a reliable formulation tool for establishing the design space of this particular drug-filler systems. [ABSTRACT FROM AUTHOR]

We report a new scoring method for rating the performance of ligands on same protein, using their extensive dynamic flexibility properties, binding with protein, and impact on receptor protein. Based on molecular dynamics (MD), this method is more accurate than single-point energy calculations. This method identified an ideal FDA-approved drug as β-tubulin microtubule inhibitor with improved attributes compared to commercial microtubule disassembly inhibitor, Paclitaxel (PTX). We started with virtual screening (VS) of FDA-approved drugs inside PTX's binding pocket (A) of human β-tubulin protein. Screened ligands (>80% score) were evaluated for non-permeation through blood-brain barrier (BBB) as targets were body cancers, gastrointestinal absorption, Lipinski, non-efflux from central nervous system (CNS) by p-glycoprotein (Pgp) and ADMET analysis. This identified FDA-approved Naloxegol drug with superior attributes compared to PTX. Pocket (A) specific docking of chain length variable derivatives of Naloxegol gave docked poses that underwent MD run to give a range of properties and their descriptors (RMSD, RMSF, RoG, H-bonds, hydrophobic interaction, and SASA). QSPR validated that MD properties dependent upon [-CH2-CH2-O-]n=0-7 chain length of Naloxegol. MD data underwent normalization, PCA analysis, and scoring against PTX. One Naloxegol derivative scored higher than PTX as a potential microtubule disassembly inhibitor. [ABSTRACT FROM AUTHOR]

Abstract We here introduce Ensemble Optimizer (EnOpt), a machine-learning tool to improve the accuracy and interpretability of ensemble virtual screening (VS). Ensemble VS is an established method for predicting protein/small-molecule (ligand) binding. Unlike traditional VS, which focuses on a single protein conformation, ensemble VS better accounts for protein flexibility by predicting binding to multiple protein conformations. Each compound is thus associated with a spectrum of scores (one score per protein conformation) rather than a single score. To effectively rank and prioritize the molecules for further evaluation (including experimental testing), researchers must select which protein conformations to consider and how best to map each compound’s spectrum of scores to a single value, decisions that are system-specific. EnOpt uses machine learning to address these challenges. We perform benchmark VS to show that for many systems, EnOpt ranking distinguishes active compounds from inactive or decoy molecules more effectively than traditional ensemble VS methods. To encourage broad adoption, we release EnOpt free of charge under the terms of the MIT license.

Abstract In recent years, significant advancements have been made in molecular generation algorithms aimed at facilitating drug development, and molecular diversity holds paramount importance within the realm of molecular generation. Nonetheless, the effective quantification of molecular diversity remains an elusive challenge, as extant metrics exemplified by Richness and Internal Diversity fall short in concurrently encapsulating the two main aspects of such diversity: quantity and dissimilarity. To address this quandary, we propose Hamiltonian diversity, a novel molecular diversity metric predicated upon the shortest Hamiltonian circuit. This metric embodies both aspects of molecular diversity in principle, and we implement its calculation with high efficiency and accuracy. Furthermore, through empirical experiments we demonstrate the high consistency of Hamiltonian diversity with real-world chemical diversity, and substantiate its effects in promoting diversity of molecular generation algorithms. Our implementation of Hamiltonian diversity in Python is available at: https://github.com/HXYfighter/HamDiv . Scientific contribution We propose a more rational molecular diversity metric for the community of cheminformatics and drug development. This metric can be applied to evaluation of existing molecular generation methods and enhancing drug design algorithms.

Within the field of Philippine folkloric medicine, the utilization of indigenous plants like Euphorbia hirta (tawa-tawa), Carica papaya (papaya), and Psidium guajava (guava) as potential dengue remedies has gained attention. Yet, limited research exists on their comprehensive effects, particularly their anti-dengue activity. This study screened 2944 phytochemicals from various Philippine plants for anti-dengue activity. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling provided 1265 compounds demonstrating pharmacokinetic profiles suitable for human use. Molecular docking targeting the dengue virus NS2b-NS3 protease’s catalytic triad (Asp 75, Ser 135, and His 51) identified ten ligands with higher docking scores than reference compounds idelalisib and nintedanib. Molecular dynamics simulations confirmed the stability of eight of these ligand–protease complexes. Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) analysis highlighted six ligands, including veramiline (−80.682 kJ/mol), cyclobranol (−70.943 kJ/mol), chlorogenin (−63.279 kJ/mol), 25beta-Hydroxyverazine (−61.951 kJ/mol), etiolin (−59.923 kJ/mol), and ecliptalbine (−56.932 kJ/mol) with favorable binding energies, high oral bioavailability, and drug-like properties. This integration of traditional medical knowledge with advanced computational drug discovery methods paves new pathways for the development of treatments for dengue.

In recent years, significant advancements have been made in molecular generation algorithms aimed at facilitating drug development, and molecular diversity holds paramount importance within the realm of molecular generation. Nonetheless, the effective quantification of molecular diversity remains an elusive challenge, as extant metrics exemplified by Richness and Internal Diversity fall short in concurrently encapsulating the two main aspects of such diversity: quantity and dissimilarity. To address this quandary, we propose Hamiltonian diversity, a novel molecular diversity metric predicated upon the shortest Hamiltonian circuit. This metric embodies both aspects of molecular diversity in principle, and we implement its calculation with high efficiency and accuracy. Furthermore, through empirical experiments we demonstrate the high consistency of Hamiltonian diversity with real-world chemical diversity, and substantiate its effects in promoting diversity of molecular generation algorithms. Our implementation of Hamiltonian diversity in Python is available at: https://github.com/HXYfighter/HamDiv. Scientific contribution We propose a more rational molecular diversity metric for the community of cheminformatics and drug development. This metric can be applied to evaluation of existing molecular generation methods and enhancing drug design algorithms. [ABSTRACT FROM AUTHOR]

Introduction: The human immunodeficiency virus (HIV) remains a significant global health concern, with a reported high infection rate of 38.4 million cases globally; an estimated 2 million new infections and approximately 700,000 HIV/ AIDS-related deaths were reported in 2021. Despite the advent of anti-retroviral therapy (ART), HIV/AIDS persists as a chronic disease. To combat this, several studies focus on developing inhibitors targeting various stages of the HIV infection cycle, including HIV-1 protease. This study aims to synthesize and characterize novel glyco diphenylphosphino metal complexes with potential HIV inhibitory properties. Method: A series of new gold(I) thiolate derivatives and three bimetallic complexes, incorporating amino phosphines and thiocarbohydrate as auxiliary ligands, were synthesized using procedures described by Jiang, et al. (2009) and Coetzee et al. (2007). Structural elucidation and purity assessment of the synthesized compounds (1-11) were conducted using micro-analysis, NMR, and infrared spectrometry. Results and Discussion: Using molecular modeling techniques, three of the metal complexes were identified as potential HIV protease inhibitors, exhibiting strong binding affinity interactions with binding pocket residues. These inhibitors demonstrated an ability to inhibit the flexibility of the flap regions of the HIV protease, similar to the known HIV protease inhibitor, darunavir. This study sheds light on the promising avenues for the development of novel therapeutic agents against HIV/AIDS. [ABSTRACT FROM AUTHOR]

Incidences of drug-resistant tuberculosis have become common and are rising at an alarming rate. Aminoacyl t-RNA synthetase has been validated as a newer target against Mycobacterium tuberculosis. Leucyl t-RNA synthetase (LeuRS) is ubiquitously found in all organisms and regulates transcription, protein synthesis, mitochondrial RNA cleavage, and proofreading of matured t-RNA. Leucyl t-RNA synthetase promotes growth and development and is the key enzyme needed for biofilm formation in Mycobacterium. Inhibition of this enzyme could restrict the growth and development of the mycobacterial population. A database consisting of 2734 drug-like molecules was screened against leucyl t-RNA synthetase enzymes through virtual screening. Based on the docking scores and MMGBSA energy values, the top three compounds were selected for molecular dynamics simulation. The druggable nature of the top three hits was confirmed by predicting their pharmacokinetic parameters. The top three hits—compounds 1035 (ZINC000001543916), 1054 (ZINC000001554197), and 2077 (ZINC000008214483)—were evaluated for their binding affinity toward leucyl t-RNA synthetase by an isothermal titration calorimetry study. The inhibitory activity of these compounds was tested against antimycobacterial activity, biofilm formation, and LeuRS gene expression potential. Compound 1054 (Macimorelin) was found to be the most potent molecule, with better antimycobacterial activity, enzyme binding affinity, and significant inhibition of biofilm formation, as well as inhibition of the LeuRS gene expression. Compound 1054, the top hit compound, has the potential to be used as a lead to develop successful leucyl t-RNA synthetase inhibitors. [ABSTRACT FROM AUTHOR]

Despite significant progress made over the past two decades in the treatment of chronic myeloid leukemia (CML), there is still an unmet need for effective and safe agents to treat patients with resistance and intolerance to the drugs used in clinic. In this work, we designed 2-arylaminopyrimidine amides of isoxazole-3-carboxylic acid, assessed in silico their inhibitory potential against Bcr-Abl tyrosine kinase, and determined their antitumor activity in K562 (CML), HL-60 (acute promyelocytic leukemia), and HeLa (cervical cancer) cells. Based on the analysis of computational and experimental data, three compounds with the antitumor activity against K562 and HL-60 cells were identified. The lead compound efficiently suppressed the growth of these cells, as evidenced by the low IC50 values of 2.8 ± 0.8 μM (K562) and 3.5 ± 0.2 μM (HL-60). The obtained compounds represent promising basic structures for the design of novel, effective, and safe anticancer drugs able to inhibit the catalytic activity of Bcr-Abl kinase by blocking the ATP-binding site of the enzyme. [ABSTRACT FROM AUTHOR]

Aim: Computer-aided drug design (CADD) is a drug design technique for computing ligand-receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics. Methods: A systematic review of studies published between 2000 and 20 July 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analyzed using software such as Excel, VOSviewer, RStudio, and CiteSpace. Results: A total of 2031 publications were included. These publications primarily originated from 99 countries or regions led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and the greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications. Conclusions: Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics that will influence future development. Furthermore, this paper will be helpful in better understanding the frontiers and hotspots of CADD. [ABSTRACT FROM AUTHOR]

BackgroundProtein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.Areas coveredIn this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.Expert opinionComputational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Osteoanabolic agents, or drugs that promote bone formation, have gained considerable attention for osteoporosis management due to their curative and preventive potentials. Sphingosine-1-phosphate receptor 2 (S1PR2) is an attractive drug target, in which its activation leads to osteogenesis-promoting effect. Nitrogen-containing heterocyclic scaffolds (i.e., quinoxaline and indole) are promising pharmacophores possessing diverse bioactivities and were reported as S1PR2 activators. Quantitative structure-activity relationship (QSAR) modeling is a computational approach well-known as a fundamental tool for facilitating successful drug development. This study demonstrates the discovery of new S1PR2 activators using computational-driven rational design. Herein, an original dataset of nitrogen-containing S1PR2 activators was collected from ChEMBL database. The retrieved dataset was separated into two datasets according to their core scaffolds (i.e., quinoxaline and indole). QSAR modeling was performed using multiple linear regression (MLR) algorithm to successfully obtain two models with good predictive performance. The constructed models also revealed key properties playing essential roles for potent S1PR2 activation, such as Van der Waals volume (R2v+ and E3v), mass (MATS5m and Km), electronegativity (H3e), and number of 5-membered rings (nR05). Subsequently, the constructed models were further employed to guide rational design and predict S1PR2 activating effects of an additional set of 752 structurally modified compounds. Most of the modified compounds were predicted to have higher potency than their parents, and a set of promising potent newly designed compounds was highlighted. Additionally, drug-likeness prediction was performed to reveal that most of the newly designed compounds are druggable compounds with possibility for further development.

Abstract As the world grapples with the relentless challenges posed by diseases like malaria, the advent of sophisticated computational tools has emerged as a beacon of hope in the quest for effective treatments. In this study we delve into the strategies behind computational tools encompassing virtual screening, molecular docking, artificial intelligence (AI), and machine learning (ML). We assess their effectiveness and contribution to the progress of malaria treatment. The convergence of these computational strategies, coupled with the ever-increasing power of computing systems, has ushered in a new era of drug discovery, holding immense promise for the eradication of malaria. Scientific contribution Computational tools remain pivotal in drug design and development. They provide a platform for researchers to explore various treatment options and save both time and money in the drug development pipeline. It is imperative to assess computational techniques and monitor their effectiveness in disease control. In this study we examine renown computational tools that have been employed in the battle against malaria, the benefits and challenges these tools have presented, and the potential they hold in the future eradication of the disease.

The zoonosis caused by Nocardia is increasing seriously. But commonly used antibiotic drugs often lead to resistance. N. seriolae dUTPase (NsdUTPase) plays a key role in the proliferation of Nocardia, and was regarded as a potent drug target. However, there was little report about the NsdUTPase inhibitors. In this study, we discovered a series of novel NsdUTPase inhibitors to fight against Nocardia. The first crystal structure of NsdUTPase was released, and a structure-based computational design was performed. Compounds 4b and 12b exhibited promising activities towards NsdUTPase (IC50 = 0.99 μM and 0.7 μM). In addition, they showed satisfied anti-Nocardia activity (MIC value ranges from 0.5 to 2 mg/L) and low cytotoxicity, which were better than approved drugs oxytetracycline and florfenicol. Molecular modelling study indicated that hydrophobic interaction might be the main contribution for ligand binding. Our results suggested that NsdUTPase inhibitors might be a useful way to repress Nocardia.

Alpinia officinarum is a commonly used spice with proven folk uses in various traditional medicines. In the current study, six compounds were isolated from its rhizomes, compounds 1–3 were identified as diarylheptanoids, while 4–6 were identified as flavonoids and phenolic acids. The isolated compounds were subjected to virtual screening against α-glucosidase, butyrylcholinesterase (BChE), and acetylcholinesterase (AChE) enzymes to evaluate their potential antidiabetic and anti-Alzheimer's activities. Molecular docking and dynamics studies revealed that 3 exhibited a strong binding affinity to human a α- glucosidase crystal structure compared to acarbose. Furthermore, 2 and 5 demonstrated high potency against AChE. The virtual screening results were further supported by in vitro assays, which assessed the compounds' effects on α-glucosidase, cholinesterases, and their antioxidant activities. 5-Hydroxy-7-(4-hydroxy-3-methoxyphenyl)-1-phenylheptan-3-one (2) showed potent antioxidant effect in both ABTs and ORAC assays, while p-hydroxy cinnamic acid (6) was the most potent in the ORAC assay. In contrary, kaempferide (4) and galangin (5) showed the most potent effect in metal chelation assay. 5-Hydroxy-1,7-diphenylhepta-4,6-dien-3-one (3) and 6 revealed the most potent effect as α-glucosidase inhibitors where compound 3 showed more potent effect compared to acarbose. Galangin (5) revealed a higher selectivity to BChE, while 2 showed the most potent activity to (AChE). [ABSTRACT FROM AUTHOR]

As the world grapples with the relentless challenges posed by diseases like malaria, the advent of sophisticated computational tools has emerged as a beacon of hope in the quest for effective treatments. In this study we delve into the strategies behind computational tools encompassing virtual screening, molecular docking, artificial intelligence (AI), and machine learning (ML). We assess their effectiveness and contribution to the progress of malaria treatment. The convergence of these computational strategies, coupled with the ever-increasing power of computing systems, has ushered in a new era of drug discovery, holding immense promise for the eradication of malaria. Scientific contribution: Computational tools remain pivotal in drug design and development. They provide a platform for researchers to explore various treatment options and save both time and money in the drug development pipeline. It is imperative to assess computational techniques and monitor their effectiveness in disease control. In this study we examine renown computational tools that have been employed in the battle against malaria, the benefits and challenges these tools have presented, and the potential they hold in the future eradication of the disease. [ABSTRACT FROM AUTHOR]

Several derivatives carrying thiazolidine-4-one pharmacophore were prepared to evaluate their antimicrobial activities. The set of compounds were shown to possess potential activities as determined by molecular docking studies for both candidal (14-alpha demethylase) and bacterial enzymes (Penicillin binding protein of E. coli). In vitro antimicrobial activities were also performed to confirm the molecular docking results. Molecular characterization by spectral techniques (FT-IR, 13C NMR and ¹H NMR) was carried out to confirm the identity of the synthesized compounds. The synthesized compounds were evaluated for antibacterial and anticandidal activity by comparing them with the reference drugs (positive controls) ceftriaxone and fluconazole respectively. Four bacterial species (Klebsiella pneumonia, Escherichia coli, Staphylococcus epidermis, Staphylococcus aureus) and one fungal species (Candida albicans) were inoculated into petri dishes and were expose to the synthesized compounds by well diffusion method. The series of the proposed compounds were successfully synthesized and some of them were proven to have antibacterial activities comparable to the reference drug. Particularly, the compounds 2c and 2a posessed activities against K. pneumomia being higher than ceftriaxone with average inhibition zone diameters of 17 mm and 16 mm at highest concentration respectively. Compound 2b was effective against E. Coli also yielding higher activity than ceftriaxone achieving an average diameter of 17 mm at the highest concentration. [ABSTRACT FROM AUTHOR]

Gastric cancer is considered a class 1 carcinogen that is closely linked to infection with Helicobacter pylori (H. pylori), which affects over 1 million people each year. However, the major challenge to fight against H. pylori and its associated gastric cancer due to drug resistance. This research gap had led our research team to investigate a potential drug candidate targeting the Helicobacter pylori‐carcinogenic TNF‐alpha‐inducing protein. In this study, a total of 45 daidzein derivatives were investigated and the best 10 molecules were comprehensively investigated using in silico approaches for drug development, namely pass prediction, quantum calculations, molecular docking, molecular dynamics simulations, Lipinski rule evaluation, and prediction of pharmacokinetics. The molecular docking study was performed to evaluate the binding affinity between the target protein and the ligands. In addition, the stability of ligand–protein complexes was investigated by molecular dynamics simulations. Various parameters were analysed, including root‐mean‐square deviation (RMSD), root‐mean‐square fluctuation (RMSF), radius of gyration (Rg), hydrogen bond analysis, principal component analysis (PCA) and dynamic cross‐correlation matrix (DCCM). The results has confirmed that the ligand–protein complex CID: 129661094 (07) and 129664277 (08) formed stable interactions with the target protein. It was also found that CID: 129661094 (07) has greater hydrogen bond occupancy and stability, while the ligand–protein complex CID 129664277 (08) has greater conformational flexibility. Principal component analysis revealed that the ligand–protein complex CID: 129661094 (07) is more compact and stable. Hydrogen bond analysis revealed favourable interactions with the reported amino acid residues. Overall, this study suggests that daidzein derivatives in particular show promise as potential inhibitors of H. pylori. [ABSTRACT FROM AUTHOR]

Based on computer-aided drug design (CADD), the active groups of the known active small molecule compounds that can bind to EGFR target protein were analyzed through the molecular docking method. Then, 12 novel asiatic acid derivatives were synthesized by introducing active groups at ring A and C-28 positions of asiatic acid. The structures of these novel compounds were determined by NMR and MS. Furthermore, the anti-tumor activities of these derivatives on human lung cancer cells (A549) and human breast cancer cells (MCF-7) were evaluated by MTT assay. In conclusion, compounds I4 and II3 have stronger anti-cancer activity than parent compounds, the activities were stronger than gefitinib and comparable to afatinib, which may be potential candidate compounds for tumor therapy. [ABSTRACT FROM AUTHOR]

Various in silico approaches to predict activity and properties of chemical compounds constitute nowadays the basis of computer-aided drug design. While there is a general focus on the predictions of values, mathematically more appropriate is the prognosis of probability distributions, which offers additional possibilities, such as the evaluation of uncertainty, higher moments, and quantiles. In this study, we applied the Hierarchical Correlation Reconstruction approach to assess several ADMET properties of chemical compounds. It uses multiple linear regression to independently assess multiple moments, which are then finally combined into predicted probability distribution. The method enables inexpensive selection of compounds with properties nearly certain to fall into the particular range during virtual screening and automatic rejection of predictions characterized by high rate of uncertainty; however, unlike to the currently used virtual screening methods, it focuses on the prediction of the property distribution, not its actual value. Moreover, the presented protocol enables detection of structural features, which should be carefully considered when optimizing compounds towards particular property, as well as it provides deeper understanding of the examined compound representations. [ABSTRACT FROM AUTHOR]

Introduction: Nanoparticles (NPs) are of great interest in the design of various drugs due to their high surface-to-volume ratio, which result from their unique physicochemical properties. Because of the importance of examining the interactions between newly designed particles with different targets in the case of various diseases, techniques for examining the interactions between these particles with different targets, many of which are proteins, are now very common. Methods: In this study, the interactions between metal oxide nanoparticles (MONPs) covered with a carbon layer (Ag2O3, CdO, CuO, Fe2O3, FeO, MgO, MnO, and ZnO NPs) and standard drugs related to the targets of Cancer and bacterial infections were investigated using the molecular docking technique with AutoDock 4.2.6 software tool. Finally, the PRO TOX-II online tool was used to compare the toxicity (LD50) and molecular weight of these MONPs to standard drugs. Results: According to the data obtained from the semi flexible molecular docking process, MgO and Fe2O3 NPs performed better than standard drugs in several cases. MONPs typically have a lower 50% lethal dose (LD50) and a higher molecular weight than standard drugs. MONPs have shown a minor difference in binding energy for different targets in three diseases, which probably can be attributed to the specific physicochemical and pharmacophoric properties of MONPs. Conclusion: The toxicity of MONPs is one of the major challenges in the development of drugs based on them. According to the results of these molecular docking studies, MgO and Fe2O3 NPs had the highest efficiency among the investigated MONPs. [ABSTRACT FROM AUTHOR]

Introduction: The human immunodeficiency virus (HIV) remains a significant global health concern, with a reported high infection rate of 38.4 million cases globally; an estimated 2 million new infections and approximately 700,000 HIV/AIDS-related deaths were reported in 2021. Despite the advent of anti-retroviral therapy (ART), HIV/AIDS persists as a chronic disease. To combat this, several studies focus on developing inhibitors targeting various stages of the HIV infection cycle, including HIV-1 protease. This study aims to synthesize and characterize novel glyco diphenylphosphino metal complexes with potential HIV inhibitory properties.Method: A series of new gold(I) thiolate derivatives and three bimetallic complexes, incorporating amino phosphines and thiocarbohydrate as auxiliary ligands, were synthesized using procedures described by Jiang, et al. (2009) andCoetzee et al. (2007). Structural elucidation and purity assessment of the synthesized compounds (1–11) were conducted using micro-analysis, NMR, and infrared spectrometry.Results and Discussion: Using molecular modeling techniques, three of the metal complexes were identified as potential HIV protease inhibitors, exhibiting strong binding affinity interactions with binding pocket residues. These inhibitors demonstrated an ability to inhibit the flexibility of the flap regions of the HIV protease, similar to the known HIV protease inhibitor, darunavir. This study sheds light on the promising avenues for the development of novel therapeutic agents against HIV/AIDS.

Drought is a major problem worldwide for agriculture, horticulture, and forestry. In many cases, major physiological and biochemical changes occur due to drought stress. The plant’s response to drought stress includes a set of systems for intracellular regulation of gene expression and inter-tissue and inter-organ signaling, which ultimately leads to increased stress tolerance. Meanwhile, the role of plant growth-promoting bacteria in improving many harmful consequences of drought stress has been discussed. One of the new ways to increase tolerance to drought stress in plants is drug design using methods based on computer analysis, bioinformatics, pharmacokinetics, and molecular docking. The present study aimed to identify volatile and non-volatile compounds involved in drought tolerance using molecular docking methods. In this research, among the volatile and non-volatile compounds effective in increasing growth and inducing drought tolerance, compounds that have a high affinity for interacting with the active site of late embryogenesis abundant (LEA) protein were identified through molecular docking methods, and it was presented as a suitable inhibitor for this protein. Based on the docking results, the inhibition potentials of the studied compounds differed, and the most vital interaction in the case of LEA 3 protein was related to the gibberellic acid compound, whose energy is equivalent to −7.78 kcal/mol. Due to the basic understanding of many mechanisms operating in the interactions of plants and bacteria, it is expected that the practical use of these compounds will grow significantly in the coming years, relying on pharmacokinetic methods and molecular docking.

Scientific relevance. Currently, machine learning (ML) methods are widely used in the research and development of new pharmaceuticals. ML methods are particularly important for assessing the safety of pharmacologically active substances early in the research process because such safety assessments significantly reduce the risk of obtaining negative results in the future.Aim. This study aimed to review the main information and prediction resources that can be used for the assessment of the safety of pharmacologically active substances in silico.Discussion. Novel ML methods can identify the most likely molecular targets for a specific compound to interact with, based on structure–activity relationship analysis. In addition, ML methods can be used to search for potential therapeutic and adverse effects, as well as to study acute and specific toxicity, metabolism, and other pharmacodynamic, pharmacokinetic, and toxicological characteristics of investigational substances. Obtained at early stages of research, this information helps to prioritise areas for experimental testing of biological activity, as well as to identify compounds with a low probability of producing adverse and toxic effects. This review describes free online ML-based information and prediction resources for assessing the safety of pharmacologically active substances using their structural formulas. Special attention is paid to the Russian computational products presented on the Way2Drug platform (https://www.way2drug.com/dr/).Conclusions. Contemporary approaches to the assessment of pharmacologically active substances in silico based on structure–activity relationship analysis using ML methods provide information about various safety characteristics and allow developers to select the most promising candidates for further in-depth preclinical and clinical studies.

Cruzipain (CZP), the major cysteine protease present in T. cruzi, the ethiological agent of Chagas disease, has attracted particular attention as a therapeutic target for the development of targeted covalent inhibitors (TCI). The vast chemical space associated with the enormous molecular diversity feasible to explore by means of modern synthetic approaches allows the design of CZP inhibitors capable of exhibiting not only an efficient enzyme inhibition but also an adequate translation to anti-T. cruzi activity. In this work, a computer-aided design strategy was developed to combinatorially construct and screen large libraries of 1,4-disubstituted 1,2,3-triazole analogues, further identifying a selected set of candidates for advancement towards synthetic and biological activity evaluation stages. In this way, a virtual molecular library comprising more than 75 thousand diverse and synthetically feasible analogues was studied by means of molecular docking and molecular dynamic simulations in the search of potential TCI of CZP, guiding the synthetic efforts towards a subset of 48 candidates. These were synthesized by applying a Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) centered synthetic scheme, resulting in moderate to good yields and leading to the identification of 12 hits selectively inhibiting CZP activity with IC50 in the low micromolar range. Furthermore, four triazole derivatives showed good anti-T. cruzi inhibition when studied at 50 μM; and Ald-6 excelled for its high antitrypanocidal activity and low cytotoxicity, exhibiting complete in vitro biological activity translation from CZP to T. cruzi. Overall, not only Ald-6 merits further advancement to preclinical in vivo studies, but these findings also shed light on a valuable chemical space where molecular diversity might be explored in the search for efficient triazole-based antichagasic agents.

Helicobacter pylori (Hp) infections pose a global health challenge demanding innovative therapeutic strategies by which to eradicate them. Urease, a key Hp virulence factor hydrolyzes urea, facilitating bacterial survival in the acidic gastric environment. In this study, a multi-methodological approach combining pharmacophore- and structure-based virtual screening, molecular dynamics simulations, and MM-GBSA calculations was employed to identify novel inhibitors for Hp urease (HpU). A refined dataset of 8,271,505 small molecules from the ZINC15 database underwent pharmacokinetic and physicochemical filtering, resulting in 16% of compounds for pharmacophore-based virtual screening. Molecular docking simulations were performed in successive stages, utilizing HTVS, SP, and XP algorithms. Subsequent energetic re-scoring with MM-GBSA identified promising candidates interacting with distinct urease variants. Lys219, a residue critical for urea catalysis at the urease binding site, can manifest in two forms, neutral (LYN) or carbamylated (KCX). Notably, the evaluated molecules demonstrated different interaction and energetic patterns in both protein variants. Further evaluation through ADMET predictions highlighted compounds with favorable pharmacological profiles, leading to the identification of 15 candidates. Molecular dynamics simulations revealed comparable structural stability to the control DJM, with candidates 5, 8 and 12 (CA5, CA8, and CA12, respectively) exhibiting the lowest binding free energies. These inhibitors suggest a chelating capacity that is crucial for urease inhibition. The analysis underscores the potential of CA5, CA8, and CA12 as novel HpU inhibitors. Finally, we compare our candidates with the chemical space of urease inhibitors finding physicochemical similarities with potent agents such as thiourea. [ABSTRACT FROM AUTHOR]

Molecular docking plays an increasingly necessary role in interdisciplinary research, particularly in modern drug design. Pharmaceutical companies compose a trillion dollar per year industry and the public is generally unaware of how beneficial pharmaceutics come to be. Despite this increasing relevance in contemporary research, docking and, by extension, computational science, have been under-represented in undergraduate education in the chemical, biochemical, and biophysical sciences. We describe herein how a multidisciplinary approach is used to design novel inhibitors of the butyrylcholinesterase enzyme (BChE), an upregulated protein in patients suffering from Alzheimer's disease. Students will then be able to compare their designed inhibitors to known BChE inhibitors via molecular docking using this easily adapted hands-on computational laboratory exercise or at-home activity that provides users with a module in which to learn the fundamentals of computer-aided drug design. While being well suited for upper-division courses in biology/biochemistry and physics/physical chemistry, the accessibility of this module allows for its incorporation into college curricula as early as second-term organic chemistry. Highly portable freeware makes this an ideal learning tool by which to infuse single- or multidisciplinary drug design reasoning into college level curricula at no cost to the student or instructor. [ABSTRACT FROM AUTHOR]

Lysine-specific demethylase 1 (LSD1/KDM1A) has emerged as a promising therapeutic target for treating various cancers (such as breast cancer, liver cancer, etc.) and other diseases (blood diseases, cardiovascular diseases, etc.), owing to its observed overexpression, thereby presenting significant opportunities in drug development. Since its discovery in 2004, extensive research has been conducted on LSD1 inhibitors, with notable contributions from computational approaches. This review systematically summarizes LSD1 inhibitors investigated through computer-aided drug design (CADD) technologies since 2010, showcasing a diverse range of chemical scaffolds, including phenelzine derivatives, tranylcypromine (abbreviated as TCP or 2-PCPA) derivatives, nitrogen-containing heterocyclic (pyridine, pyrimidine, azole, thieno[3,2-b]pyrrole, indole, quinoline and benzoxazole) derivatives, natural products (including sanguinarine, phenolic compounds and resveratrol derivatives, flavonoids and other natural products) and others (including thiourea compounds, Fenoldopam and Raloxifene, (4-cyanophenyl)glycine derivatives, propargylamine and benzohydrazide derivatives and inhibitors discovered through AI techniques). Computational techniques, such as virtual screening, molecular docking and 3D-QSAR models, have played a pivotal role in elucidating the interactions between these inhibitors and LSD1. Moreover, the integration of cutting-edge technologies such as artificial intelligence holds promise in facilitating the discovery of novel LSD1 inhibitors. The comprehensive insights presented in this review aim to provide valuable information for advancing further research on LSD1 inhibitors. [ABSTRACT FROM AUTHOR]

Osteoanabolic agents, or drugs that promote bone formation, have gained considerable attention for osteoporosis management due to their curative and preventive potentials. Sphingosine-1-phosphate receptor 2 (S1PR2) is an attractive drug target, in which its activation leads to osteogenesis-promoting effect. Nitrogen-containing heterocyclic scaffolds (i.e., quinoxaline and indole) are promising pharmacophores possessing diverse bioactivities and were reported as S1PR2 activators. Quantitative structure-activity relationship (QSAR) modeling is a computational approach well-known as a fundamental tool for facilitating successful drug development. This study demonstrates the discovery of new S1PR2 activators using computational-driven rational design. Herein, an original dataset of nitrogen-containing S1PR2 activators was collected from ChEMBL database. The retrieved dataset was separated into two datasets according to their core scaffolds (i.e., quinoxaline and indole). QSAR modeling was performed using multiple linear regression (MLR) algorithm to successfully obtain two models with good predictive performance. The constructed models also revealed key properties playing essential roles for potent S1PR2 activation, such as Van der Waals volume (R2v+ and E3v), mass (MATS5m and Km), electronegativity (H3e), and number of 5-membered rings (nR05). Subsequently, the constructed models were further employed to guide rational design and predict S1PR2 activating effects of an additional set of 752 structurally modified compounds. Most of the modified compounds were predicted to have higher potency than their parents, and a set of promising potent newly designed compounds was highlighted. Additionally, drug-likeness prediction was performed to reveal that most of the newly designed compounds are druggable compounds with possibility for further development. [ABSTRACT FROM AUTHOR]

The prognosis of mixed-lineage leukemia (MLL) has remained a significant health concern, especially for infants. The minimal treatments available for this aggressive type of leukemia has been an ongoing problem. Chromosomal translocations of the KMT2A gene are known as MLL, which expresses MLL fusion proteins. A protein called menin is an important oncogenic cofactor for these MLL fusion proteins, thus providing a new avenue for treatments against this subset of acute leukemias. In this study, we report results using the structure-based drug design (SBDD) approach to discover potential novel MLL-mediated leukemia inhibitors from natural products against menin. The three-dimensional (3D) protein model was derived from Protein Databank (Protein ID: 4GQ4), and EasyModeller 4.0 and I-TASSER were used to fix missing residues during rebuilding. Out of the ten protein models generated (five from EasyModeller and I-TASSER each), one model was selected. The selected model demonstrated the most reasonable quality and had 75.5% of residues in the most favored regions, 18.3% of residues in additionally allowed regions, 3.3% of residues in generously allowed regions, and 2.9% of residues in disallowed regions. A ligand library containing 25,131 ligands from a Chinese database was virtually screened using AutoDock Vina, in addition to three known menin inhibitors. The top 10 compounds including ZINC000103526876, ZINC000095913861, ZINC000095912705, ZINC000085530497, ZINC000095912718, ZINC000070451048, ZINC000085530488, ZINC000095912706, ZINC000103580868, and ZINC000103584057 had binding energies of −11.0, −10.7, −10.6, −10.2, −10.2, −9.9, −9.9, −9.9, −9.9, and −9.9 kcal/mol, respectively. To confirm the stability of the menin–ligand complexes and the binding mechanisms, molecular dynamics simulations including molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) computations were performed. The amino acid residues that were found to be potentially crucial in ligand binding included Phe243, Met283, Cys246, Tyr281, Ala247, Ser160, Asn287, Asp185, Ser183, Tyr328, Asn249, His186, Leu182, Ile248, and Pro250. MI-2-2 and PubChem CIDs 71777742 and 36294 were shown to possess anti-menin properties; thus, this justifies a need to experimentally determine the activity of the identified compounds. The compounds identified herein were found to have good pharmacological profiles and had negligible toxicity. Additionally, these compounds were predicted as antileukemic, antineoplastic, chemopreventive, and apoptotic agents. The 10 natural compounds can be further explored as potential novel agents for the effective treatment of MLL-mediated leukemia. [ABSTRACT FROM AUTHOR]

QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties were widely used to search lead bioactive molecules in chemical databases. The dataset's preparation to build these models has a strong influence on the quality of the generated models, and sampling requires that the original dataset be divided into training (for model training) and test (for statistical evaluation) sets. This sampling can be done randomly or rationally, but the rational division is superior. In this paper, we present MASSA, a Python tool that can be used to automatically sample datasets by exploring the biological, physicochemical, and structural spaces of molecules using PCA, HCA, and K-modes. The proposed algorithm is very useful when the variables used for QSAR are not available or to construct multiple QSAR models with the same training and test sets, producing models with lower variability and better values for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, this tool also generates useful graphical representations that can provide insights into the data. [ABSTRACT FROM AUTHOR]

Ionizing radiation can induce different types of DNA lesions, leading to genomic instability and ultimately cell death. Radiation therapy or radiotherapy, a major modality in cancer treatment, harnesses the genotoxic potential of radiation to target and destroy cancer cells. Nevertheless, cancer cells have the capacity to develop resistance to radiation treatment (radioresistance), which poses a major obstacle in the effective management of cancer. It has been shown that administration of platinum-based drugs to cancer patients can increase tumor radiosensitivity, but despite this, it is associated with severe adverse effects. Several lines of evidence support that activation of the DNA damage response and repair machinery in the irradiated cancer cells enhances radioresistance and cellular survival through the efficient repair of DNA lesions. Therefore, targeting of key DNA damage repair factors would render cancer cells vulnerable to the irradiation effects, increase cancer cell killing, and reduce the risk of side effects on healthy tissue. Herein, we have employed a computer-aided drug design approach for generating ab initio a chemical compound with drug-like properties potentially targeting two proteins implicated in multiple DNA repair pathways. The findings of this study could be taken into consideration in clinical decision-making in terms of co-administering radiation with DNA damage repair factor-based drugs. [ABSTRACT FROM AUTHOR]

Based on the simulated docking of Epidermal growth factor receptor inhibitors with known active small molecule compounds, computer-aided drug design technology was used to analyze key amino acid fragments and determine the active groups binding with key sites. Then, twelve novel analogues of oleanolic acid (OA) were synthesized by introducing active groups at the C-3 and C-28 positions of OA. The structures of these novel analogues were confirmed by NMR and MS. Furthermore, the antitumor activities of these novel analogues were evaluated by MTT assay. As a result, compounds I3 and II3 showed stronger cytotoxicity on tumor cells than positive controls. In conclusion, our study synthesized twelve novel analogues of OA and determined compounds I3 and II3 had better antitumor effect, which may be potential candidate compounds for tumor therapy. [ABSTRACT FROM AUTHOR]

Due to the adverse environmental impacts of toxic heavy metal–based antifoulants, the screening of environmentally friendly antifoulants has become important for the development of marine antifouling technology. Compared with the traditional lengthy and costly screening method, computer-aided drug design (CADD) offers a promising and efficient solution that can accelerate the screening process of green antifoulants. In this study, we selected barnacle chitin synthase (CHS, an important enzyme for barnacle settlement and development) as the target protein for docking screening. Three CHS genes were identified in the barnacle Amphibalanus amphitrite, and their encoded proteins were found to share a conserved glycosyltransferase domain. Molecular docking of 31,561 marine natural products with AaCHSs revealed that zoanthamine alkaloids had the best binding affinity (−11.8 to −12.6 kcal/mol) to AaCHSs. Considering that the low abundance of zoanthamine alkaloids in marine organisms would limit their application as antifoulants, a marine fungal–derived natural product, mycoepoxydiene (MED), which has a similar chemical structure to zoanthamine alkaloids and the potential for large-scale production by fermentation, was selected and validated for stable binding to AaCHS2L2 using molecular docking and molecular dynamics simulations. Finally, the efficacy of MED in inhibiting cyprid settlement of A. amphitrite was confirmed by a bioassay that demonstrated an EC50 of 1.97 μg/mL, suggesting its potential as an antifoulant candidate. Our research confirmed the reliability of using AaCHSs as antifouling targets and has provided insights for the efficient discovery of green antifoulants by CADD.

Incidences of drug-resistant tuberculosis have become common and are rising at an alarming rate. Aminoacyl t-RNA synthetase has been validated as a newer target against Mycobacterium tuberculosis. Leucyl t-RNA synthetase (LeuRS) is ubiquitously found in all organisms and regulates transcription, protein synthesis, mitochondrial RNA cleavage, and proofreading of matured t-RNA. Leucyl t-RNA synthetase promotes growth and development and is the key enzyme needed for biofilm formation in Mycobacterium. Inhibition of this enzyme could restrict the growth and development of the mycobacterial population. A database consisting of 2734 drug-like molecules was screened against leucyl t-RNA synthetase enzymes through virtual screening. Based on the docking scores and MMGBSA energy values, the top three compounds were selected for molecular dynamics simulation. The druggable nature of the top three hits was confirmed by predicting their pharmacokinetic parameters. The top three hits—compounds 1035 (ZINC000001543916), 1054 (ZINC000001554197), and 2077 (ZINC000008214483)—were evaluated for their binding affinity toward leucyl t-RNA synthetase by an isothermal titration calorimetry study. The inhibitory activity of these compounds was tested against antimycobacterial activity, biofilm formation, and LeuRS gene expression potential. Compound 1054 (Macimorelin) was found to be the most potent molecule, with better antimycobacterial activity, enzyme binding affinity, and significant inhibition of biofilm formation, as well as inhibition of the LeuRS gene expression. Compound 1054, the top hit compound, has the potential to be used as a lead to develop successful leucyl t-RNA synthetase inhibitors.

Antivirals with broad coronavirus activity are important for treating high-risk individuals exposed to the constantly evolving SARS-CoV-2 variants of concern (VOCs) as well as emerging drug-resistant variants. We developed and characterized a novel class of active-site-directed 3-chymotrypsin-like protease (3CLpro) inhibitors (C2–C5a). Our lead direct-acting antiviral (DAA), C5a, is a non-covalent, non-peptide with a dissociation constant of 170 nM against recombinant SARS-CoV-2 3CLpro. The compounds C2–C5a exhibit broad-spectrum activity against Omicron subvariants (BA.5, BQ.1.1, and XBB.1.5) and seasonal human coronavirus-229E infection in human cells. Notably, C5a has median effective concentrations of 30–50 nM against BQ.1.1 and XBB.1.5 in two different human cell lines. X-ray crystallography has confirmed the unique binding modes of C2–C5a to the 3CLpro, which can limit virus cross-resistance to emerging Paxlovid-resistant variants. We tested the effect of C5a with two of our newly discovered host-directed antivirals (HDAs): N-0385, a TMPRSS2 inhibitor, and bafilomycin D (BafD), a human vacuolar H+-ATPase [V-ATPase] inhibitor. We demonstrated a synergistic action of C5a in combination with N-0385 and BafD against Omicron BA.5 infection in human Calu-3 lung cells. Our findings underscore that a SARS-CoV-2 multi-targeted treatment for circulating Omicron subvariants based on DAAs (C5a) and HDAs (N-0385 or BafD) can lead to therapeutic benefits by enhancing treatment efficacy. Furthermore, the high-resolution structures of SARS-CoV-2 3CLpro in complex with C2–C5a will facilitate future rational optimization of our novel broad-spectrum active-site-directed 3C-like protease inhibitors.