Computer-aided Drug Design Research Articles

Natural product rhynchophylline prevents stress-induced hair graying by preserving melanocyte stem cells via the β2 adrenergic pathway suppression

Norepinephrine (NA), a stress hormone, can accelerate hair graying by binding to β2 adrenergic receptors (β2AR) on melanocyte stem cells (McSCs). From this, NA-β2AR axis could be a potential target for preventing the stress effect. However, identifying selective blockers for β2AR has been a key challenge. Therefore, in this study, advanced computer-aided drug design (CADD) techniques were harnessed to screen natural molecules, leading to the discovery of rhynchophylline as a promising compound. Rhynchophylline exhibited strong and stable binding within the active site of β2AR, as verified by molecular docking and dynamic simulation assays. When administered to cells, rhynchophylline effectively inhibited NA-β2AR signaling. This intervention resulted in a significant reduction of hair graying in a stress-induced mouse model, from 28.5% to 8.2%. To gain a deeper understanding of the underlying mechanisms, transcriptome sequencing was employed, which revealed that NA might disrupt melanogenesis by affecting intracellular calcium balance and promoting cell apoptosis. Importantly, rhynchophylline acted as a potent inhibitor of these downstream pathways. In conclusion, the study demonstrated that rhynchophylline has the potential to mitigate the negative impact of NA on melanogenesis by targeting β2AR, thus offering a promising solution for preventing stress-induced hair graying.Graphical

154P Computer-aided drug design based on CLDN4 ligand and its biological evaluation in ovarian cancer

154P Computer-aided drug design based on CLDN4 ligand and its biological evaluation in ovarian cancer

Discovery of GABA Aminotransferase Inhibitors via Molecular Docking, Molecular Dynamic Simulation, and Biological Evaluation

γ-Aminobutyric acid aminotransferase (GABA-AT) is a pyridoxal 5′-phosphate (PLP)-dependent enzyme that degrades γ-aminobutyric (GABA) in the brain. GABA is an important inhibitory neurotransmitter that plays important neurological roles in the brain. Therefore, GABA-AT is an important drug target that regulates GABA levels. Novel and potent drug development to inhibit GABA-AT is still a very challenging task. In this study, we aimed to devise novel and potent inhibitors against GABA-AT using computer-aided drug design (CADD) tools. Since the crystal structure of human GABA-AT was not yet available, we utilized a homologous structure derived from our previously published paper. To identify highly potent compounds relative to vigabatrin, an FDA-approved drug against human GABA-AT, we developed a pharmacophore analysis protocol for 530,000 Korea Chemical Bank (KCB) compounds and selected the top 50 compounds for further screening. Preliminary biological analysis was carried out for these 50 compounds and 16 compounds were further assessed. Subsequently, molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations were carried out. In the results, four predicted compounds, A07, B07, D08, and H08, were found to be highly potent and were further evaluated by a biological activity assay to confirm the results of the GABA-AT activity inhibition assay.

Herbal Drug Development for Alzheimer’s Disease: A Promising Approach through Computer-Aided Drug Design

Herbal Drug Development for Alzheimer’s Disease: A Promising Approach through Computer-Aided Drug Design

Mechanisms of action underlying Shentong Zhuyu decoction based treatment of rheumatoid arthritis using systems biology and computer-aided drug design.

Rheumatoid arthritis is an autoimmune disease characterized by chronic polyarticular pain, for which no cure currently exists. In Chinese medicine, rheumatoid arthritis (RA) is believed to be caused by phlegm and blood stagnation. Shentong Zhuyu decoction can be used to treat RA, as it promotes blood circulation, resolves blood stasis, and relieves pain. In our study, we used network pharmacology and computer-aided drug design to evaluate the components, active compounds, and targets of Shentong Zhuyu decoction (STZY). Our results suggest that STZY contains active compounds such as quercetin, luteolin, and formononetin that regulate immune network targets. RA associated genes are enriched in pathways including those associated with nuclear factor kappa B, phosphatidylinositol-3-kinase/AKT, and hypoxia inducible factor 1 signaling. The main active compounds in STZY (quercetin and luteolin) were derived from Achyranthis Bidentatae Radix, Carthami Flos, licorice, Cyperi Rhizoma, and Myrrha and targeted the pro-inflammatory cytokines interleukin 2, interleukin 1 alpha, interleukin 1 beta, and interleukin 6. In addition, the compounds quercetin, luteolin, and formononetin in these herbs can target the anti-inflammatory cytokines interleukin 4 and interleukin 10. Our results suggest that STZY can balance the immune network, promote an anti-inflammatory environment, and reduce the clinical symptoms of RA. Based on the close relationship between inflammatory response and osteoclast formation, we hypothesized that STZY may inhibit inflammation and alleviate bone destruction in RA. Our findings indicate that STZY can treat RA through multiple components, targets, and pathways. This study may provide a reference for the clinical application of STZY in RA treatment.

Ligand-Based Drug Design of Genipin Derivatives with Cytotoxic Activity against HeLa Cell Line: A Structural and Theoretical Study.

Cervical cancer is a malignant neoplastic disease, mainly associated to HPV infection, with high mortality rates. Among natural products, iridoids have shown different biological activities, including cytotoxic and antitumor effects, in different cancer cell types. Geniposide and its aglycone Genipin have been assessed against different types of cancer. In this work, both iridoids were evaluated against HeLa and three different cervical cancer cell lines. Furthermore, we performed a SAR analysis incorporating 13 iridoids with a high structural similarity to Geniposide and Genipin, also tested in the HeLa cell line and at the same treatment time. Derived from this analysis, we found that the dipole moment (magnitude and direction) is key for their cytotoxic activity in the HeLa cell line. Then, we proceeded to the ligand-based design of new Genipin derivatives through a QSAR model (R2 = 87.95 and Q2 = 62.33) that incorporates different quantum mechanic molecular descriptor types (ρ, ΔPSA, ∆Polarizability2, and logS). Derived from the ligand-based design, we observed that the presence of an aldehyde or a hydroxymethyl in C4, hydroxyls in C1, C6, and C8, and the lack of the double bond in C7-C8 increased the predicted biological activity of the iridoids. Finally, ten simple iridoids (D9, D107, D35, D36, D55, D56, D58, D60, D61, and D62) are proposed as potential cytotoxic agents against the HeLa cell line based on their predicted IC50 value and electrostatic features.

Molecular Docking using different Tools

Molecular docking is a powerful and effective tool in modern era of computer aided drug design. The purpose of ligand-protein docking is to predict the predominant binding mode(s) of a ligand with a protein of known three-dimensional structure. Successful docking methods search high-dimensional spaces effectively and use a scoring function that correctly ranks candidate dockings. Docking can be used to perform virtual screening of wide variety of compounds, rank the results, and propose structural hypotheses of how the ligands inhibit the target, which is invaluable in lead optimization. This review represents the overview of advanced molecular docking using various software. It analyses the various approaches regarding molecular docking like the use of machine learning algorithms in molecular docking, different physicochemical aspects related to ligand-protein complex. These recent improvements in modern technology affect the whole area of healthcare and welfare science.

Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression

Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.Graphical abstract

Drug design and in-silico study of 2-alkoxylatedquinoline-3-carbaldehyde compounds: Inhibitors of Mycobacterium tuberculosis

Tuberculosis (TB) caused by Mycobacterium tuberculosis (MTB) is a deadly communicable disease that frequently affects the lungs. Current treatment protocols are bedeviled by extensive drug-resistant (XDR) and the evolution of multidrug-resistant (MDR-TB) strains. Virtual in-silico drug discovery tools were used to investigate thirty-two hypothetical 2-alkoxylatedquinoline-3-carbaldehyde compounds for screening against ten different diseases proteins based on drug-likeness, oral bioavailability, pharmacokinetics, global chemical reactivity and their theoretical binding affinities. Their chemical structures were optimized at the density functional theory (DFT) using Becke's three-parameter exchange functional with Lee–Yang–Parr correlation function (B3LYP) and the triple zeta basis set 6–311 in a vacuum using Gaussian 09 W software. Docking study using Pyrx and Discovery studio. Fourteen compounds; 4 - 6, 12 – 14, 19 – 22 and 27–30 complied with the established drug-likeness rules, however, five compounds 12, 13, 27, 28 and 29 exhibited no significant toxicity. Structural activity relationship revealed that shorter (n < 3) or longer (n > 5) alkyloxyl substituents at position-2 of the quinoline moiety reduces drug-likeness and increases toxicity. Individually, the binding energies obtained were (-8.9 kcal/ mole) against malaria for compound 12 and (-8.2 kcal/mole) against the diabetes for compound 29, both highest for the ten diseases investigated. Mycobacterium Tuberculosis proteins investigated. Molecular dynamics also confirms that 12 and 27 binds very well in the active pocket of Mycobacterium tuberculosis and calculated total free binding energy from MMPBSA is -97.53 ± 2.47 and -58.62 ± 2.94 kJ/mol respectively. The five lead compounds all had binding energies higher than the reference tuberculosis drugs; Isoniazid and Ethambutanol.

An Exploration of the Inhibitory Mechanism of Rationally Screened Benzofuran-1,3,4-Oxadiazoles and-1,2,4-Triazoles as Inhibitors of NS5B RdRp Hepatitis C Virus through Pharmacoinformatic Approaches.

Benzofuran, 1,3,4-oxadiazole, and 1,2,4-triazole are privileged heterocyclic moieties that display the most promising and wide spectrum of biological activities against a wide variety of diseases. In the current study, benzofuran-1,3,4-oxadiazole BF1-BF7 and benzofuran-1,2,4-triazole compounds BF8-BF15 were tested against HCV NS5B RNA-dependent RNA polymerase (RdRp) utilizing structure-based screening via a computer-aided drug design (CADD) approach. A molecular docking approach was applied to evaluate the binding potential of benzofuran-appended 1,3,4-oxadiazole and 1,2,4-triazole BF1-BF15 molecules. Benzofuran-1,3,4-oxadiazole scaffolds BF1-BF7 showed lesser binding affinities (-12.63 to -14.04 Kcal/mol) than benzofuran-1,2,4-triazole scaffolds BF8-BF15 (-14.11 to -16.09 Kcal/mol) against the HCV NS5B enzyme. Molecular docking studies revealed the excellent binding affinity scores exhibited by benzofuran-1,2,4-triazole structural motifs BF-9 (-16.09 Kcal/mol), BF-12 (-15.75 Kcal/mol), and BF-13 (-15.82 Kcal/mol), respectively, which were comparatively better than benzofuran-based HCV NS5B inhibitors' standard reference drug Nesbuvir (-15.42 Kcal/mol). A molecular dynamics simulation assay was also conducted to obtain valuable insights about the enzyme-compounds interaction profile and structural stability, which indicated the strong intermolecular energies of the BF-9+NS5B complex and the BF-12+NS5B complex as per the MM-PBSA method, while the BF-12+NS5B complex was the most stable system as per the MM-GBSA calculation. The drug-likeness and ADMET studies of all the benzofuran-1,2,4-triazole derivatives BF8-BF15 revealed that these compounds possessed good medicinal chemistry profiles in agreement with all the evaluated parameters for being drugs. The molecular docking affinity scores, MM-PBSA/MM-GBSA and MD-simulation stability analysis, drug-likeness profiling, and ADMET study assessment indicated that N-4-fluorophenyl-S-linked benzofuran-1,2,4-triazole BF-12 could be a future promising anti-HCV NS5B RdRp inhibitor therapeutic drug candidate that has a structural agreement with the Nesbuvir standard reference drug.

Antibreast cancer activities of phytochemicals from Anonna muricata using computer-aided drug design (CADD) approach

Abstract Antibreast cancer activities of 131 phytochemicals from Annona muricata (Soursop) were investigated against human placental aromatase (PDB ID: 3S7S), a prominent target receptor in breast cancer therapy using computer aided-drug design approach. An antibreast cancer drug (tamoxifen) was used for comparison. The result of this work flourishes caffeoquinic acid (−8.4 kcal/mol), roseoside (−8.3 kcal/mol), chlorogenic acid (−8.2 kcal/mol), feruloylglycoside (−8.1 kcal/mol), citroside A (−8.0 kcal/mol), and coreximine (−7.8 kcal/mol), as probable inhibitors of human placental aromatase. This is due to their excellent binding affinities (ΔG), coupled with outstanding druglike, absorption, distribution, metabolism, excretion, and toxicity profiles, bioavailability and oral-bioavailability properties, and the interactions of important residues with the active pocket of human placental aromatase. All the results obtained were similar to that of the standards tamoxifen (−8.0 kcal/mol) but could be better when optimized. Thus, lead optimization, molecular dynamics, and in vivo investigations are thereby recommended on the identified potent compounds in the quest of developing new therapeutic agents against breast cancer.

Design, Synthesis, Evaluation and Molecular Dynamics Simulation of Dengue Virus NS5-RdRp Inhibitors.

Dengue virus (DENV) is a major mosquito-borne human pathogen in tropical countries; however, there are currently no targeted antiviral treatments for DENV infection. Compounds 27 and 29 have been reported to be allosteric inhibitors of DENV RdRp with potent inhibitory effects. In this study, the structures of compounds 27 and 29 were optimized using computer-aided drug design (CADD) approaches. Nine novel compounds were synthesized based on rational considerations, including molecular docking scores, free energy of binding to receptor proteins, predicted Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) parameters, structural diversity, and feasibility of synthesis. Subsequently, the anti-DENV activity was assessed. In the cytopathic effect (CPE) assay conducted on BHK-21 cells using the DENV2 NGC strain, both SW-b and SW-d demonstrated comparable or superior activity against DENV2, with IC50 values of 3.58 ± 0.29 μM and 23.94 ± 1.00 μM, respectively, compared to that of compound 27 (IC50 = 19.67 ± 1.12 μM). Importantly, both SW-b and SW-d exhibited low cytotoxicity, with CC50 values of 24.65 μmol and 133.70 μmol, respectively, resulting in selectivity indices of 6.89 and 5.58, respectively. Furthermore, when compared to the positive control compound 3'-dATP (IC50 = 30.09 ± 8.26 μM), SW-b and SW-d displayed superior inhibitory activity in an enzyme inhibitory assay, with IC50 values of 11.54 ± 1.30 μM and 13.54 ± 0.32 μM, respectively. Molecular dynamics (MD) simulations elucidated the mode of action of SW-b and SW-d, highlighting their ability to enhance π-π packing interactions between benzene rings and residue W795 in the S1 fragment, compared to compounds 27 and 29. Although the transacylsulphonamide fragment reduced the interaction between T794 and NH, it augmented the interaction between R729 and T794. In summary, our study underscores the potential of SW-b and SW-d as allosteric inhibitors targeting the DENV NS5 RdRp domain. However, further in vivo studies are warranted to assess their pharmacology and toxicity profiles.

Rudimentary Review on Molecular Docking: A Beginner’s Guide

The computer modelling of structural complexes generated from two or more interacting molecules are referred to as molecular docking. It is an indispensable tool in computer-aided drug design and structural molecular biology. Using this technology, large libraries of compounds may be digitally screened, and the results can be graded along with structural assumptions about how the ligands impact the target's reduction. Recent advances in the synthesis of anti-infectious medicines prompted by structural insights have enabled the application of computer-assisted drug design in the quest for innovative mechanism- or structure-based drugs. Molecular docking is an important phase in the drug development process because it determines the best positions for molecules to occupy when they are coupled together and predicts how effectively two molecules will bind once they have been docked. The input structure's design is also critical, and the results are assessed using sampling methods and scoring systems. The recently developed docking software Local Move Monte Carlo [LMMC] provides a strong choice for customizable receptor docking strategies. Docking is a technique for determining how ligands and proteins interact. It is structurally sound and compatible with computer-assisted medication design. Successful docking discovers high-dimensional spaces and ranks function utilisation, resulting in a candidate docking rating that is acceptable. It may also be used to screen vast libraries of molecules and offer structural hypotheses for the process.

Drug discovery: In silico dry data can bypass biological wet data?

The recent and extraordinary increase in computer power, along with the availability of efficient algorithms based on artificial intelligence, has prompted a large number of inexperienced scientists to challenge the complex and yet competitive world of drug discovery, by pretending to identify new hits through the sole use of computer aided drug design (CADD). Does the golden era of dry data run the risk of overshadowing the importance of wet data and, in doing so, forget that in silico and biological data need each other in successful preclinical drug discovery programmes?

In silico drug repurposing using molecular docking and dynamics to target the protein interaction between the SARS-CoV-2 S-glycoprotein and the ACE2 receptor

Background: The protein interaction between the viral surface S-glycoprotein and the host angiotensin converting enzyme-2 receptor (ACE2) is key to the virulent nature of SARS-CoV-2. The potential role that effective drug repurposing strategies may have to help stem the impact of future outbreaks has been brought to light in the recent COVID-19 pandemic. This study outlines a comprehensive approach towards in-silico drug discovery which aims to identify hit agents that can be suitably translated into a clinical setting. Methods: We use two different computational platforms to analyze the viral S-glycoprotein in its bound conformational state to the ACE2 receptor. We employed a comprehensive screening approach to shortlist compounds capable of binding to the viral target interface and corroborated these findings using both Schrödinger’s Glide and AutoDock Vina. Molecular dynamic simulation studies further verified the stability of the interaction at the viral-host protein interface. Results: Lymecycline, pentagalloylglucose, polydatin, and hexoprenaline were identified as prime candidates for further studies given the robust and stable nature of their interaction at the viral-host interface and relevance for clinical testing. These agents were shown in a 100-nanosecond simulation trajectory to favorably disrupt key binding interactions at the viral-host interface and may potentially inhibit viral entry into host cells. In all hit molecules it was observed that inhibiting the interaction with the following key viral binding residues: Lys17, Gly496, Tyr 505, and key host residues: His34, Asp38, Lys353, played a critical role toward the inhibition of the viral-host protein interaction. Conclusions: Our study is unique in its comprehensive approach to identify agents that can bind to the S-glycoprotein-ACE2 interface using multiple computational platforms. Among the hit compounds shortlisted in this study, both lymecycline and hexoprenaline may be considered as candidates for preliminarily clinical studies to assess their therapeutic potential in the management of COVID-19 infections.

Computer-aided drug design for virtual-screening and active-predicting of main protease (Mpro) inhibitors against SARS-CoV-2.

Introduction: SARS-CoV-2 is a novel coronavirus with highly contagious and has posed a significant threat to global public health. The main protease (Mpro) is a promising target for antiviral drugs against SARS-CoV-2. Methods: In this study, we have used pharmacophore-based drug design technology to identify potential compounds from drug databases as Mpro inhibitors. Results: The procedure involves pharmacophore modeling, validation, and pharmacophore-based virtual screening, which identifies 257 compounds with promising inhibitory activity. Discussion: Molecular docking and non-bonding interactions between the targeted protein Mpro and compounds showed that ENA482732 was the best compound. These results provided a theoretical foundation for future studies of Mpro inhibitors against SARS-CoV-2.

A Review of the Therapeutic Importance of Indole Scaffold in Drug Discovery.

Indole is known as a versatile heterocyclic building block for its multiple pharmacological activities and has a high probability of success in the race for drug candidates. Many natural products, alkaloids, and bioactive heterocycles contain indole as the active principle pharmacophore. These encourage the researchers to explore it as a lead in the drug development process. The current manuscript will serve as a torchbearer for understanding the structurally diverse class of indole derivatives with extensive pharmacological activity. The current manuscript describes the intermediates and their functional groups responsible for superior biological activity compared to the standard. The review is written to help researchers to choose leads against their target but also to provide crucial insight into the design of a hybrid pharmacophore-based approach in drug design with enhanced potential. The present reviews on the indole derivatives correlate the structures with biological activities as well as essential pharmacophores, which were highlighted. The discussion was explored under challenging targets like dengue, chikungunya (anti-viral), antihypertensive, diuretic, immunomodulator, CNS stimulant, antihyperlipidemic, antiarrhythmic, anti-Alzheimer's, and neuroprotective, along with anticancer, antitubercular, antimicrobial, anti-HIV, antimalarial, anti-inflammatory, antileishmanial, antianthelmintic, and enzyme inhibitors. So, this review includes a discussion of 19 different pharmacological targets for indole derivatives that could be utilized to derive extensive information needed for ligand-based drug design. The article will guide the researchers in the selection, design of lead and pharmacophore, and ligand-based drug design using indole moiety.

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.

Small-Molecule Conformer Generators: Evaluation of Traditional Methods and AI Models on High-Quality Data Sets.

Small-molecule conformer generation (SMCG) is an extremely important task in both ligand- and structure-based computer-aided drug design, especially during the hit discovery phase. Recently, a multitude of artificial intelligence (AI) models tailored for SMCG have emerged. Despite developers typically furnishing performance evaluation data upon releasing their AI models, a comprehensive and equitable performance comparison between AI models and conventional methods is still lacking. In this study, we curated a new benchmarking data set comprising 3354 high-quality ligand bioactive conformations. Subsequently, we conducted a systematic assessment of the performance of four widely adopted traditional methods (i.e., ConfGenX, Conformator, OMEGA, and RDKit ETKDG) and five AI models (i.e., ConfGF, DMCG, GeoDiff, GeoMol, and torsional diffusion) in the tasks of reproducing bioactive and low-energy conformations of small molecules. In the former task, the AI models have no advantage, particularly with a maximum ensemble size of 1. Even the best-performing AI model GeoMol is still worse than any of the tested traditional methods. Conversely, in the latter task, the torsional diffusion model shows obvious advantages, surpassing the best-performing traditional method ConfGenX by 26.09 and 12.97% on the COV-R and COV-P metrics, respectively. Furthermore, the influence of force field-based fine-tuning on the quality of the generated conformers was also discussed. Finally, a user-friendly Web server called fastSMCG was developed to enable researchers to rapidly and flexibly generate small-molecule conformers using both traditional and AI methods. We anticipate that our work will offer valuable practical assistance to the scientific community in this field.

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.