Computer-aided Drug Design Methods Research Articles

Computer Aided Drug Design (CADD) Supported Medication: A Guide to Improved Patient Care

Computer Aided Drug Design (CADD) supported medication structure fuses a wide scope of hypothetical and computational methodologies that functions as significant component of present-day drug discovery. CADD strategies have made key commitments to the occasion of medications that are in clinical use or in clinical preliminaries. Such techniques have risen and developed together with trial approaches used in drug formulation. This incorporates different database and different instruments used in the zone of bioinformatics like BLAST, FASTA, DRUG BANK and so forth this strategy is utilized to break down, create and find drugs closely resembling biologically active molecules. The ligand-oriented computer aided drug designing helped study of ligand that are found to associate with the objective target of interest to search their 2D and 3D structures. The significance of those methods stands to theorize the nature and class of the given target. CADD methods can build the odds of perceiving the compounds with useful qualities, prompting development and extend the possibilities of getting a compound over the numerous obstacles of preclinical testing. It tends to be utilized for quick calculation of chemical libraries so as to direct and quicken the beginning period improvement of newly active compounds. It likewise incorporates a wide assortment computational methodology like virtual library, virtual screening, lead optimization and so on. Over the ongoing years, computer aided drug designing (CADD) has been otherwise called in silico screening that has risen as a compelling methodology in view of drug finding and progression through varied advanced attributes. In silico screening additionally makes an approach to combination and screen the chose mixes for new and better therapeutics. CADD and bioinformatics instruments convey advantages, for example, cost sparing, time to market, in-sight information of drug receptor interactions that accelerate drug disclosure and improvement

492 Novel BH3 Mimetic Bcl-2 Inhibitor Promotes Autophagic Cell Death and Reduces in Vivo Glioblastoma Tumor Growth

INTRODUCTION: Anti-apoptotic members of the Bcl-2 family proteins play central roles in the regulation of cell death in glioblastoma, the most malignant type of brain tumor. Despite the advances in GBM treatment, there is still an urgent need for new therapeutic approaches. METHODS: In silico studies resulted in 500,000 molecules being screened with computer-aided drug design methods and the molecule with the highest binding affinity was selected and named BAU-243. Cell viability, IC50 value, tumor formation potential, tumor spheroid formation potential, tumor invasion studies were investigated following BAU-243 application. The molecular changes caused by Bcl-2 inhibition were demonstrated by quantitative PCR, western blot and immunofluorescence methods. In in vivo studies, glioma tumors were transplanted intracranially into Nod Scid Gamma(NSG) mice using the mouse xenograft model. RESULTS: Here, we report a novel 4-thiazolidinone derivative BH3 mimetic, BAU-243 that binds to Bcl-2 with a high affinity. BAU-243 effectively reduced overall glioblastoma cell proliferation including a subpopulation of cancer-initiating cells in contrast to the selective Bcl-2 inhibitor ABT-199. While ABT-199 successfully induces apoptosis in high BCL2-expressing neuroblastoma SHSY-5Y cells, BAU-243 triggered autophagic cell death rather than apoptosis in A172 cells, indicated by the upregulation of BECN1, ATG5, and MAP1LC3B expression. Lc3b-II, a potent autophagy marker, was significantly upregulated following BAU-243 treatment. Moreover, BAU-243 significantly reduced tumor growth in vivo in orthotopic brain tumor models when compared to the vehicle group, and ABT-199 treated animals. To elucidate the molecular mechanisms of action of BAU-243, we performed computational modeling simulations that were consistent with in vitro results. CONCLUSIONS: Results indicated that BAU-243 activates autophagic cell death by disrupting Beclin 1:Bcl-2 complex, and may serve as a potential small molecule for the treatment of glioblastoma.

HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints.

In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target-cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure-activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold.

Insights into the selective mechanism of PDE2/9a inhibitors from silico aspects

The selective design of competitive enzyme inhibitors is an extremely difficult task but necessary work for certain types of systems, such as the phosphodiesterase (PDE) system addressed in this article. In the PDE family, PDE2A and PDE9 respectively target the central nervous system and heart failure, and share many conserved amino acids at their binding sites. Therefore, gaining a deep understanding of the selective mechanisms of PDE2A/9A is crucial for designing highly selective drugs. In this study, various computer-aided drug design (CADD) methods, including molecular docking, molecular dynamics simulations (MD), and binding free energy calculations, are employed to explore the selective mechanisms of PDE2A/9A. Overall, our research results indicate a selective design strategy for PDE2A, which involves incorporating hydrophobic or aromatic moieties into the molecular structure to better accommodate the hydrophobic pocket of PDE2A. Additionally, it is recommended to introduce functional groups capable of forming connections with selective residues, such as Phe830 and Gln812 for PDE2A, or Ala452 and Tyr424 for PDE9A, to enhance the selectivity of inhibitors targeting PDE2A/9A. This achievement is anticipated to pave the way for the development of innovative and selective small molecules targeting PDE2A/9A. Communicated by Ramaswamy H. Sarma

Application status and challenges of molecular docking in development of traditional Chinese medicine

Traditional Chinese medicine is precious treasure of ancient Chinese science and a key to unlocking the treasure trove of Chinese civilization. To elucidate the efficacy and mechanism of traditional Chinese medicines, scientists have been engaged in the research on the molecular basis and regulatory targets. Molecular docking is a computer-aided drug design method capable of visualizing the interaction between components and target proteins. With the progress in the modernization of traditional Chinese medicine and the advancement of algorithms and computing power, molecular docking has become an essential approach in the development of new traditional Chinese medicines. This article summarizes the recent research progress in molecular docking in the development of traditional Chinese medicine, aiming to provide valuable references for further screening of active components and offering insights for improving the development of new traditional Chinese medicines.

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A systematic review.

In the search for better anticancer drugs, computer-aided drug design (CADD) techniques play an indispensable role in facilitating the lengthy and costly drug discovery process especially when natural products are involved. Anthraquinone is one of the most widely-recognized natural products with anticancer properties. This review aimed to systematically assess and synthesize evidence on the utilization of CADD techniques centered on the anthraquinone scaffold for cancer treatment. The conduct and reporting of this review were done in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 guideline. The protocol was registered in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904) and also published recently. The search strategy was designed based on the combination of concept 1 "CADD or virtual screening", concept 2 "anthraquinone" and concept 3 "cancer". The search was executed in PubMed, Scopus, Web of Science and MedRxiv on 30 June 2023. Databases searching retrieved a total of 317 records. After deduplication and applying the eligibility criteria, the final review ended up with 32 articles in which 3 articles were found by citation searching. The CADD methods used in the studies were either structure-based alone (69%) or combined with ligand-based methods via parallel (9%) or sequential (22%) approaches. Molecular docking was performed in all studies, with Glide and AutoDock being the most popular commercial and public software used respectively. Protein data bank was used in most studies to retrieve the crystal structure of the targets of interest while the main ligand databases were PubChem and Zinc. The utilization of in-silico techniques has enabled a deeper dive into the structural, biological and pharmacological properties of anthraquinone derivatives, revealing their remarkable anticancer properties in an all-rounded fashion. By harnessing the power of computational tools and leveraging the natural diversity of anthraquinone compounds, researchers can expedite the development of better drugs to address the unmet medical needs in cancer treatment by improving the treatment outcome for cancer patients.

Rational design of novel phenol ether derivatives as non-covalent proteasome inhibitors through 3D-QSAR, molecular docking and ADMET prediction

Aim: The purpose of this paper is to use different structures and ligand-based drug design methods properly to provide theoretical guidance for the design of novel non-covalent proteasome inhibitors, and conduct theoretical analysis of the binding interaction mode between receptors and ligands. At the same time, the pharmacokinetic (PK) prediction, drug-likeness, and synthesis prediction were made for the screened novel drugs. Therefore, potentially attractive non-covalent proteasome inhibitors with low toxicity could be found as anticancer drugs. Methods: In this work, computer-aided drug design methods, including quantitative structure-activity relationship (QSAR), molecular docking, absorption, distribution, metabolism, excretion, and toxicology (ADMET) prediction, and drug-likeness prediction methods were performed. Results: In this study, the structure-activity relationship (SAR) of a series of non-covalent proteasome inhibitors were studied and the optimal comparative molecular field analysis (CoMFA; Q2 = 0.574, r2 = 0.999, r2pred = 0.755) and comparative molecular similarity indices analysis (CoMSIA)-SEHA (Q2 = 0.584, r2 = 0.989, r2pred = 0.921) models were obtained. According to the results of the QSAR model, some vital clues were found that would effectively enhance the biological activity of the compound. Based on these clues, 24 novel non-covalent proteasome inhibitors (D01–D24) were finally designed and screened. While the binding models between proteasome [protein data bank (PDB) code: 3MG6] and three representative compounds (15, 20, and D24) were also analyzed by using the molecular docking method. The results suggested that hydrogen bond and hydrophobic interaction played a key role in binding interaction between the receptor and ligand. In addition, the results of ADMET prediction indicated that the new designed compounds had reasonable PK parameters and drug-like properties. Conclusions: These statistical results can provide theoretical guidance for structural optimization, design, and synthesis of more effective non-covalent proteasome inhibitors in the future.

Full Modification Control over Retrosynthetic Routes for Guided Optimization of Lead Structures.

Synthesizability is essential for compounds designed in silico. Regardless, synthetic accessibility is often considered only as an afterthought in the design and optimization process. In addition, the trend with modern computer-aided drug design methods is going toward full automation and away from the possibility of incorporating user knowledge. With this work, we present the second major release of our software tool, Synthesia, for synthesis-aware lead structure modification, where the user's expertise is now fully utilized. A provided retrosynthetic route is used as a pathway to guide structural modifications that introduce desired structural changes in the target compound. Moreover, the approach allows the user to define the exact position or component in the retrosynthetic route, which should be modified, further integrating the user's expert knowledge. This paper describes the functionality of Synthesia, its basic concepts, and several application scenarios ranging from simple examples to a comparison of the effects of the different exchange functions to an analysis of a set of bioisosteric linker structures, highlighting potential synthetically feasible replacements.

Quinolines: A Promising Heterocyclic Scaffold for Cancer Therapeutics.

The quinoline scaffold is a widely recognized heterocycle with applications across various disease categories, ranging from malaria and viral infections to bacterial infections, high cholesterol, and even tumors. Consequently, quinoline plays a crucial role in the development of new drugs, and the field greatly benefits from advancements in computer-aided drug design. This review aims to provide insights into the evolution of quinoline and its derivatives, offering a comprehensive exploration of both marketed and developing drugs. Furthermore, the function and mechanism of quinoline compounds are introduced. Many studies rely on cell experiments to demonstrate drug cytotoxicity. In the concluding section of this review, the interaction between quinoline compounds and targets is simulated using computer-aided drug design methods. A thorough analysis is conducted on the potential influencing factors affecting the binding state between quinoline compounds and targets. Notably, the Pi-Alkyl interaction emerges as a significant contributor, while hydrogen bonding is identified as a pivotal bond in these interactions. This review serves as a valuable overview of the potential contributions of quinoline compounds to cancer treatment. It seamlessly combines the essential functions of marketed quinoline drugs with the promise held by emerging quinoline-based compounds. Additionally, the simulation of interactions between quinoline compounds and proteins through computer-aided design enhances our understanding of these compounds' efficacy.

Ni(II) complexes with in-situ generated N, O-bidentate aroylhydrazone Schiff base ligands: One-pot synthesis, characterization, crystal structure, and in-silico studies

A series of novel aroylhydrazone-based Ni(II) complexes were synthesized by using analogous Schiff base ligands prepared in-situ by reacting 4-[(4-methylbenzyl)oxy]benzoylhydrazine with different aldehydes in the presence of Ni(II) acetate tetrahydrate. The correspondent [NiL2] complexes, 4-7, were characterized by physicochemical techniques, viz., molar conductance, magnetic susceptibility measurement, IR, NMR, and mass spectroscopic techniques. The single-crystal X-ray structural analyses of complexes 4, 6 and 7 showed a distorted square-planar geometry for Ni atoms with deprotonated ligands in trans-configuration bound via azomethine-N and carbonylate-O atoms. Next, quantum chemical methods were incorporated to investigate their thermochemical, molecular orbital, and electrostatic potential properties. Computer-aided drug design methods were also implemented to assess their medicinal, pharmacological, and toxicological properties. Molecular docking and interaction analyses disclosed their potential binding affinity against human lung Cytochrome P450 2A13 receptor protein. Pharmacokinetic and toxicological studies suggest their relatively safe drug administrative properties.

An Updated Review on Developing Small Molecule Kinase Inhibitors Using Computer-Aided Drug Design Approaches.

Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure-activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs.

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A protocol for systematic review.

There is still unmet medical need in cancer treatment mainly due to drug resistance and adverse drug events. Therefore, the search for better drugs is essential. Computer-aided drug design (CADD) and discovery tools are useful to streamline the lengthy and costly drug development process. Anthraquinones are a group of naturally occurring compounds with unique scaffold that exert various biological properties including anticancer activities. This protocol describes a systematic review that provide insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment. It was prepared in accordance with the "Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 guidelines, and published in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904). Search strategies will be developed based on the combination of relevant keywords and executed in PubMed, Scopus, Web of Science and MedRxiv. Only original studies that employed CADD as primary tool in virtual screening for the purpose of designing or discovering anti-cancer drugs involving anthraquinone scaffold published in English language will be included. Two independent reviewers will be involved to screen and select the papers, extract the data and assess the risk of bias. Apart from exploring the trends and types of CADD methods used, the target proteins of these compounds in cancer treatment will also be revealed in this review. It is believed that the outcome of this study could be utilized to support the ongoing research in similar area with better quality and greater probability of success, consequently optimizing the resources in subsequent in vitro, in vivo, non-clinical and clinical development. It will also serve as an evidence based scientific guide for new research to design novel anthraquinone-derived drug with improved efficacy and safety profile for cancer treatment.

High-Quality Conformer Generation with CONFORGE: Algorithm and Performance Assessment.

Knowledge of the putative bound-state conformation of a molecule is an essential prerequisite for the successful application of many computer-aided drug design methods that aim to assess or predict its capability to bind to a particular target receptor. An established approach to predict bioactive conformers in the absence of receptor structure information is to sample the low-energy conformational space of the investigated molecules and derive representative conformer ensembles that can be expected to comprise members closely resembling possible bound-state ligand conformations. The high relevance of such conformer generation functionality led to the development of a wide panel of dedicated commercial and open-source software tools throughout the last decades. Several published benchmarking studies have shown that open-source tools usually lag behind their commercial competitors in many key aspects. In this work, we introduce the open-source conformer ensemble generator CONFORGE, which aims at delivering state-of-the-art performance for all types of organic molecules in drug-like chemical space. The ability of CONFORGE and several well-known commercial and open-source conformer ensemble generators to reproduce experimental 3D structures as well as their computational efficiency and robustness has been assessed thoroughly for both typical drug-like molecules and macrocyclic structures. For small molecules, CONFORGE clearly outperformed all other tested open-source conformer generators and performed at least equally well as the evaluated commercial generators in terms of both processing speed and accuracy. In the case of macrocyclic structures, CONFORGE achieved the best average accuracy among all benchmarked generators, with RDKit's generator coming close in second place.

Dual targeting in prostate cancer with phytoconstituents as a potent lead: a computational approach for novel drug discovery

Prostate Cancer (PCa) is an abnormal cell growth within the prostate. This condition is the second most widespread malignancy in elderly males and one of the most frequently diagnosed life-threatening conditions. The Androgen receptor signaling pathway played a crucial role in the initiation and spread to increase the risk of PCa. Hence, targeting the AR receptor signaling pathway is a key strategy for a therapeutic plan for PCa. Our study focuses on recognizing potential inhibitors for dual targeting in PCa by using the in-silico approach. In this study, we target the two enzymes that are CYP17A1 (3RUK) and 5α-reductase (3G1R) responsible for PCa, with the help of phytoconstituents. The natural plant contains various phytochemical types produced from secondary metabolites and used as a medical treatment. The in-silico investigation of phytoconstituents and enzymes was done by approaching molecular docking, ADMET analysis, and high-level molecular dynamic simulation used to assess the stability and binding affinities of the protein-ligand complex. Some phytoconstituents, such as Peonidin, Pelargonidin, Malvidin and Berberine show complex has good molecular interaction with protein. The reliability of the docking scores was examined using a molecular dynamic simulation, which revealed that the complex remained stable throughout the simulation, which ranged from 0 to 200 ns. The selected hits may be effective against CYP17A1 (3RUK) and 5α-reductase (3G1R) (PCa) using a computer-aided drug design (CADD) method, which further enables researchers for upcoming in-vivo and in-vitro research, according to our in-silico approach. Communicated by Ramaswamy H. Sarma

Virtual screening and biological activity evaluation of novel efflux pump inhibitors targeting AdeB

The AdeABC efflux pump is an important mechanism causing multidrug resistance in Acinetobacter baumannii, and its main component AdeB can recognize carbapenems, aminoglycosides, and other multi-class antibiotics and efflux them intracellularly, which is an ideal target for the development of anti-multidrug resistant bacteria drugs. Here, we combined multiple computer-aided drug design methods to target AdeB to identify promising novel structural inhibitors. Virtual screening was performed by molecular docking and molecular dynamics simulation (MD) and 12 potential compounds were identified from the databases. Meanwhile, their biological activities were validated by in vitro activity assays, and ChemDiv L676–2179 (γ-IFN), ChemDiv L676–1461, and Chembridge 53717615 were confirmed to suppress efflux effects and restore antibiotic susceptibility of resistant bacteria, which are expected to be developed as adjuvant drugs for the treatment of multi-drug resistant Acinetobacter baumannii clinical infections.

Discovery of drug lead compounds for Anti-Alzheimer's disease on the basis of synaptic plasticity

Alzheimer's disease (AD) is a progressive neurodegenerative disease commonly seen in the middle-aged and the elder. Its clinical presentations are mainly memory impairment and cognitive impairment. Its cardinal pathological features are the deposition of extracellular Amyloid-β (Aβ), intracellular neurofibrillary tangles and synaptic dysfunction. The etiology of AD is complex and the pathogenesis remains unclear. Having AD would lead to awful living experience of it's patients, which may be a burden to the patient even to the public health care system. However, there are no certain cure for AD. Thus it's significant for both medical value and social meaning to find the way to cure or prevent AD and to research on the pathogenesis of AD. In this work, the molecular docking technology, pharmacokinetic analysis and pharmacological experiments were employed to analyse the natural active compounds and the mechanisms against AD based on the synaptic plasticity. A total of seven target proteins related to the synaptic plasticity and 44 natural active compounds with potential to enhance the synaptic plasticity were obtained through a literature review and network pharmacological analysis. Computer-Aided Drug Design (CADD) method was used to dock the anti-AD key target proteins with the 44 compounds. The compounds with good binding effect were screened. Three anti-AD active compounds based on the synaptic plasticity were obtained, including Curcumin, Withaferin A and Withanolide A. In addition, pharmacological experiments were carried out on Withaferin A and Withanolide A based on its good docking results. The experimental results showed that Withaferin A has good anti-AD potential and great potential to enhance synaptic plasticity. The anti-AD effect can be achieved through a multi-target synergistic mechanism.

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies.

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.

Evaluation of Receptor Relationships of Some Drugs Used in the Treatment of COVID-19 by Modeling Studies

Objective: It is important to investigate the interactions of drugs used in the treatment process of COVID-19 with cellular mechanisms. In this study, the aim was to investigate the interactions of Dexamethasone, Favipiravir, and Hydroxychloroquine drugs used in the treatment of COVID-19 with the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2). Materials and Methods: Within the scope of the study, firstly, 3-dimensional structures of receptors and drug molecules were formed. Then the interactions of each of the receptor and drug molecules at the binding site were examined by molecular docking studies, which is a computer-aided drug design method, and their binding affinities were evaluated. Results: As a result of the analyses, it was determined that the drug named Hydroxychloroquine has the highest and the drug called Dexamethasone has the lowest binding affinity for all three receptors. In addition, it has been determined that Dexamethasone develops inappropriate interactions with ER and HER2 receptor active site amino acids. Conclusions: In this study, preliminary data on how receptor interactions can occur when normal individuals and breast cancer patients use Dexamethasone, Favipiravir, and Hydroxychloroquine are presented.

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development.

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

Rational design of stapled antimicrobial peptides.

The global increase in antimicrobial drug resistance has dramatically reduced the effectiveness of traditional antibiotics. Structurally diverse antibiotics are urgently needed to combat multiple-resistant bacterial infections. As part of innate immunity, antimicrobial peptides have been recognized as the most promising candidates because they comprise diverse sequences and mechanisms of action and have a relatively low induction rate of resistance. However, because of their low chemical stability, susceptibility to proteases, and high hemolytic effect, their usage is subject to many restrictions. Chemical modifications such as D-amino acid substitution, cyclization, and unnatural amino acid modification have been used to improve the stability of antimicrobial peptides for decades. Among them, a side-chain covalent bridge modification, the so-called stapled peptide, has attracted much attention. The stapled side-chain bridge stabilizes the secondary structure, induces protease resistance, and increases cell penetration and biological activity. Recent progress in computer-aided drug design and artificial intelligence methods has also been used in the design of stapled antimicrobial peptides and has led to the successful discovery of many prospective peptides. This article reviews the possible structure-activity relationships of stapled antimicrobial peptides, the physicochemical properties that influence their activity (such as net charge, hydrophobicity, helicity, and dipole moment), and computer-aided methods of stapled peptide design. Antimicrobial peptides under clinical trial: Pexiganan (NCT01594762, 2012-05-07). Omiganan (NCT02576847, 2015-10-13).