Computational Biology and Chemistry最新文献

{"title":"Design and synthesis of tunable schiff base complexes from bis-(2-oxoindolin-3-ylidene)anthracene-9,10-dione: Integrated structural, biological, and molecular modeling insights","authors":"Ahmed M. Abu-Dief ,&nbsp;Eida S. Al-Farraj ,&nbsp;Mohamed Abdel-Hameed ,&nbsp;Nadiyah Alahmadi ,&nbsp;Maher Fathalla ,&nbsp;Abdullah Yahya Abdullah Alzahrani ,&nbsp;Mashael A. Alghamdi ,&nbsp;Aly Abdou","doi":"10.1016/j.compbiolchem.2025.108682","DOIUrl":"10.1016/j.compbiolchem.2025.108682","url":null,"abstract":"<div><div>Three novel compounds, each featuring a tetra-dentate ligand known as 1-((2-oxoindolin-3-ylidene)amino)-2-((2-oxoindolin-3-ylidene)amino)anthracene-9,10-dione (BIA), have been successfully synthesized. These molecules exhibit the unique characteristic of forming complexes with Cu(II), Ru(III), and VO(II) metal ions, resulting in distinct metal-organic structures.Structural characterization was performed using elemental analysis, magnetic properties measurement, FT-IR spectroscopy, and electronic spectroscopy. Moreover, the stoichiometry in solution was determined through both continuous variation and molar ratio analysis. These analyses have shown that the copper and ruthenium complexes exhibit an octahedral geometric configuration. Conversely, the vanadyl (VO) complex demonstrates a distinct square pyramidal structure.Density functional theory (DFT) computations were employed to confirm the geometrical configurations of the prepared complexes. The synthesized <strong>BIA</strong> ligand and its corresponding metal complexes were assessed for their <em>in vitro</em> antimicrobial. The results indicated that the RuBIA complex emerged as the most efficacious agent against both bacterial and fungal growth, outperforming established medications like Ofloxacin and Fluconazole as standard drugs with sequence<strong>BIA &lt; VOBIA &lt; CuBIA &lt;RuBIA</strong>complex.Additionally, the study evaluated the in vitro cytotoxicity of the synthesized compounds against Hep-G2, MCF-7, and HCT-116 cancer cell lines. The results suggested that the <strong>RuBIA</strong> complex had the highest potency (IC50 =3.42–6.45 <strong>µg/µl)</strong>, followed by <strong>CuBIA</strong>(IC50 =4.42–7.85 <strong>µg/µl)</strong>, and <strong>VOBIA</strong>(IC50 =5.72–8.35 <strong>µg/µl)</strong>, indicating their potential as promising anticancer agents. The DPPH radical scavenging activity was also assessed, and all complexes displayed greater efficacy than Ascorbic acid. Investigations employing molecular docking methodologies were undertaken to discern the interaction mechanisms of the aforementioned complexes. The findings revealed that the incorporation of metal ions substantially bolstered the molecular affinities, with the sequence of binding potency as follows: RuBIA&gt; CuBIA &gt; VOBIA complex&gt;BIA ligand.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108682"},"PeriodicalIF":3.1,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of artificial intelligence in classification of connective tissue diseases in patients with anti-nuclear antibody (ANA) positivity","authors":"Burcu Bosnalı ,&nbsp;Erdinç Türk ,&nbsp;Tahir Saygın Öğüt ,&nbsp;Mert Ünal ,&nbsp;Taner Danışman ,&nbsp;Hatice Yazısız ,&nbsp;Funda Erbasan ,&nbsp;Mustafa Ender Terzioğlu ,&nbsp;Veli Yazisiz","doi":"10.1016/j.compbiolchem.2025.108679","DOIUrl":"10.1016/j.compbiolchem.2025.108679","url":null,"abstract":"<div><h3>Objectives</h3><div>The study aimed to investigate the classification performance of artificial intelligence (AI) in diagnosing connective tissue diseases(CTD). This was done by analyzing laboratory data, including additional markers, in patients who tested positive for antinuclear antibody(ANA).</div></div><div><h3>Material/Methods</h3><div>The research included 663 ANA-positive patients. An automated machine learning approach, specifically Auto-Weka, was used to classify these patients based on 75 features, including age, sex, and various laboratory tests.</div></div><div><h3>Results</h3><div>The Bayes Network achieved the highest overall performance with 93.1 % accuracy, 77.7 % sensitivity, and 96.0 % specificity in the classification of all patients. The most successful models were <em>Locally Weighted Learning</em> for systemic lupus erythematosus(SLE), with an accuracy of 93.4 %; <em>Logistic Model Trees</em> for primary Sjogren's syndrome(pSS), with an accuracy of 91.4 %; <em>AdaBoostM</em> for rheumatoid arthritis(RA), with an accuracy of 95.2 %; and <em>Sequential Minimal Optimization</em> for systemic sclerosis(SSc), with an accuracy of 92.0 %. Sensitivity and specificity rates for SLE, pSS, RA and SSc were found to be 69.4 %, 72.0 %, 78.5 %, 75.3 % and 98.7 %, 96.2 %, 98.9 %, 94.9 %, respectively. The area under the ROC curve in the general distribution of the groups was 95.6 %, the highest value in distinguishing was 99.1 % for RA and the lowest was 85.1 % for SSc. The most predictive markers identified were hematocrit for SLE, anti-SSA for pSS, rheumatoid factor for RA, and anti-centromere B positivity for SSc.</div></div><div><h3>Conclusion</h3><div>AI models are highly successful in classifying ANA-positive patients with great accuracy. AI-based approaches have the potential to assist clinicians in diagnosing autoimmune diseases by providing more accurate and faster results.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108679"},"PeriodicalIF":3.1,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring biological research hotspots through a novel bibliometric approach","authors":"Shan Chen ,&nbsp;Junsha Wang ,&nbsp;Xinyu Huang ,&nbsp;Kailin Chen ,&nbsp;Limei Fu ,&nbsp;Yuanzhao Ding","doi":"10.1016/j.compbiolchem.2025.108680","DOIUrl":"10.1016/j.compbiolchem.2025.108680","url":null,"abstract":"<div><div>Biological research is a crucial field of study, profoundly impacting every aspect of human life. The objective of this study is to utilize an innovative bibliometric analysis method to understand current research hotspots and future trends in biology. This novel bibliometric analysis method, based on the R programming language, offers a completely different approach than traditional VOSviewer, providing a more in-depth analysis. Based on the bibliometric analysis results, this paper also proposes potential future developments, namely, integrating big data with machine learning. By integrating existing data into large databases and then training models, this approach can provide deep insights and accurate predictions for the future.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108680"},"PeriodicalIF":3.1,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvement of an eye disease detection model by using the denoising diffusion implicit model","authors":"Ping-Huan Kuo ,&nbsp;Eirene Du ,&nbsp;Chiou-Jye Huang ,&nbsp;Wei-Chuan Lan ,&nbsp;Shu-Hung Chou ,&nbsp;Ting-Chun Yao ,&nbsp;Chao-Chung Peng","doi":"10.1016/j.compbiolchem.2025.108677","DOIUrl":"10.1016/j.compbiolchem.2025.108677","url":null,"abstract":"<div><div>With rapid developments in artificial intelligence (AI), the discussion about and applications of generative AI have increased substantially. Generative AI has extensive and valuable applications in many industrial and medical fields and is a possible solution for industries that struggle to collect large quantities of data. The present study evaluated the use of generative AI in eye disease prediction. Because retinal images are difficult to acquire, this study used a generative AI model [i.e., the denoising diffusion implicit model (DDIM)] to conduct data augmentation, thereby improving the accuracy of a convolutional neural network (CNN) model developed for eye disease detection. This study adopted the DDIM primarily for its high inference speed and ability to consistently generate high-quality samples in a limited number of steps, making it suitable for tasks that require high-quality medical images. With the increasing prevalence of electronic products, the number of patients with retinopathy or optic neuropathy is increasing annually, and patients are experiencing these diseases at increasingly younger ages. Moreover, eye diseases such as glaucoma and macular degeneration are becoming increasingly common in modern society. The developed CNN model exhibited a 3 % higher accuracy when it was trained using the data generated by the DDIM than when it was trained without these data. This CNN model can screen eye disease symptoms early to enable patients to receive timely treatment, thereby mitigating the risk and consequences of eye diseases. The results of this study indicate that the training data generated using the DDIM can enhance the accuracy of early eye disease detection.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108677"},"PeriodicalIF":3.1,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rational design, biological and in-silico evaluation of quinoline-chalcone hybrids: A new series of antimicrobial and anticancer agents","authors":"Ilker Kiliccioglu ,&nbsp;Ahmad Badreddin Musatat ,&nbsp;Gorkem Dulger ,&nbsp;Alparslan Atahan ,&nbsp;Basaran Dulger ,&nbsp;Mustafa Zengin","doi":"10.1016/j.compbiolchem.2025.108675","DOIUrl":"10.1016/j.compbiolchem.2025.108675","url":null,"abstract":"<div><div>This study investigates the synthesis, antimicrobial, anticancer, and in silico properties of novel quinoline-chalcone hybrids (<strong>nQCa-l</strong>), which were synthesized and characterized. Their antimicrobial activity revealed broad-spectrum efficacy, with compound 2QC-h demonstrating superior potency compared to several standard antibiotics and antifungals. The anticancer potential was assessed against gastrointestinal system cancer cell lines (AGS, HepG2, HCT116), where 2QC-h emerged as the most potent antiproliferative agent, often surpassing oxaliplatin in efficacy, particularly in AGS gastric cancer cells. Mechanistic studies have demonstrated that 2QC-h synergistically induces apoptosis and inhibits epithelial-mesenchymal transition (EMT) in AGS cells through the intrinsic mitochondrial pathway, thereby enhancing the anticancer effect of oxaliplatin. Crucially, 2QC-h exhibited selective cytotoxicity towards gastrointestinal system cancer cells (AGS cells: 4.85 ± 0.22 µg/mL and 2.66 ± 0.58 µg/mL, HCT116 cells: 6.61 ± 0.29 µg/mL and 2.39 ± 0.57 µg/mL, and HepG2 cells: 9.14 ± 0.49 µg/mL and 6.15 ± 0.27 µg/mL for 24 h and 48 h, respectively) and minimal morphological effects on healthy HUVEC cells. Computational studies, including DFT analysis, MEP, RDG, ELF, LOL, and ALIE, provided comprehensive insights into the electronic structure, reactivity, and non-covalent interactions, elucidating the structure-activity relationships (SAR). Molecular docking simulations identified VEGFR-2 and EGFR as the preferential targets for these derivatives, with nanomolar binding affinities, which correlated strongly with experimental cytotoxic potencies. ADME highlighted favorable drug-likeness properties while identifying areas for further optimization. Overall, this research establishes quinoline-chalcone hybrids as promising multi-target therapeutic agents with significant potential for developing novel antimicrobial and anticancer drugs.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108675"},"PeriodicalIF":3.1,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis of leukemia using microarray analysis based on Hidden Markov Model and Random Convolutional Kernel Transform","authors":"Sareh Baqeri Matak ,&nbsp;Elham Askari ,&nbsp;Sara Motamed","doi":"10.1016/j.compbiolchem.2025.108676","DOIUrl":"10.1016/j.compbiolchem.2025.108676","url":null,"abstract":"<div><h3>Introduction</h3><div>Leukemia is one of the most prevalent cancers worldwide, and early detection is critical for effective treatment. Microarray data is a key tool in this process, given the vast number of genes involved, which makes the analysis complex and time-consuming. Identifying relevant genes is a crucial step in disease diagnosis.</div></div><div><h3>Material and methods</h3><div>This study aims to improve the diagnostic accuracy of various leukemia types by using microarray data in combination with advanced deep learning techniques. The proposed model begins with selecting essential features and sequences relevant to diagnosis. These data sequences are processed using a Generative Adversarial Network (GAN) with a U-Net architecture to generate synthetic data. Both the synthetic and original data are then labeled for analysis. Feature ranking is conducted using a Hidden Markov Model (HMM), followed by classification using the Random Convolutional Kernel Transformation (ROCKET) approach. This process ultimately predicts five leukemia categories within the sample.</div></div><div><h3>Results</h3><div>The results demonstrate that the proposed model achieves a high classification accuracy of 99.26 %, outperforming existing methods.</div></div><div><h3>Conclusion</h3><div>This research highlights the importance of leveraging DNA alterations associated with genetic mutations to improve leukemia diagnostics, emphasizing the potential for early detection and intervention. In simpler terms, identifying DNA modifications across the genome can help predict an individual's likelihood of developing leukemia. Detecting these changes can significantly aid in diagnosis.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108676"},"PeriodicalIF":3.1,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MediFlora-Net: Quantum-enhanced deep learning for precision medicinal plant identification","authors":"Uma K.V. ,&nbsp;Sarvika P ,&nbsp;Jayaa Sri K ,&nbsp;Lakshmi Aiswarya C","doi":"10.1016/j.compbiolchem.2025.108674","DOIUrl":"10.1016/j.compbiolchem.2025.108674","url":null,"abstract":"<div><div>The accurate identification and classification of medicinal plants are crucial for botanical research, pharmacology, and traditional medicine, where wrong identification or categorization of the plant species may lead to worse medical effects. In this research, MediFlora-Net, a novel Deep Learning (DL) model is created and ideal for the accurate identification of medicinal plants. The proposed MediFlora-Net uses multi-modal DL methodologies, quantum-assisted feature extraction and hybrid ensembling methodologies in constructing the plant recognition model. Besides, the methodology uses Vision Transformer (ViT), Convolutional Neural Networks (CNNs) and Proposed Med-Plant-Generative Adversarial Networks (GANs). This makes the framework to be capable of handling multiple imaging modalities such as RGB, and Hyperspectral Botanical Imagery. Also, a new quantum-inspired feature extraction technique is integrated into the model in which quantum probabilistic feature mapping and entanglement-based representation are utilized to extract higher-order botanical features. The framework also includes separate ‘feature fusion’, fine-tuned attention, and probabilistic decision-making. The proposed MediFlora-Net advances medicinal plant identification to greater precision and flexibility for practical use in the conservation of biological diversity, ethnobotanical studies, and pharmacology. This work effectively exploits DL techniques and quantum-inspired approaches to tackle the inherent issues of botanical identification to enable the design of better-advanced systems of plant identification. The implementation and source code are available at <span><span>https://github.com/kvuma02-svg/MEDICINAL-PLANT-IDENTIFICATION</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108674"},"PeriodicalIF":3.1,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular mechanism underlying radiation resistance in esophageal squamous cell carcinoma","authors":"Gang Ran ,&nbsp;Mao Hu ,&nbsp;Jinyao Zhang ,&nbsp;Wenkun Zhu ,&nbsp;Yuanyuan Liang ,&nbsp;Tangzhi Dai ,&nbsp;Yan Zhou ,&nbsp;Xiaoan Li ,&nbsp;Qing Wang","doi":"10.1016/j.compbiolchem.2025.108666","DOIUrl":"10.1016/j.compbiolchem.2025.108666","url":null,"abstract":"<div><div>Esophageal squamous cell carcinoma (ESCC) is a major global health challenge, especially in Asia, due to its high incidence, mortality and poor prognosis. As there are no reliable early - diagnosis biomarkers, ESCC is often detected at an advanced stage, when radiotherapy becomes the main treatment. However, the emergence of radioresistance significantly compromises treatment efficacy, leading to tumor recurrence and metastasis. Although some research has been done on the mechanisms of ESCC radiation resistance, a comprehensive understanding remains elusive. To address this knowledge gap and identify more molecular targets for overcoming radiation resistance, we established a radioresistant ESCC cell model and conducted systematic 4D label-free proteomic profiling. Quantitative analysis revealed 364 differentially expressed proteins, predominantly enriched in nucleotide excision repair, glutathione metabolism, and insulin resistance pathways. Functional validation identified TXNDC12 as a critical regulator of radioresistance, and its overexpression is significantly associated with enhanced glutathione synthesis and intracellular ROS scavenging. This study provides the first proteomic evidence linking redox homeostasis modulation through TXNDC12-GSH axis activation to ESCC radioresistance, offering novel therapeutic targets for overcoming radiation resistance and improving clinical outcomes in advanced ESCC management.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108666"},"PeriodicalIF":3.1,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering synergistic interactions between Curcumin, Piperine, and milk proteins using accurate theoretical methods","authors":"Madhesh Palanisamy ,&nbsp;Gayathri Krishnamoorthy ,&nbsp;Vidya Ravindran","doi":"10.1016/j.compbiolchem.2025.108672","DOIUrl":"10.1016/j.compbiolchem.2025.108672","url":null,"abstract":"<div><div>This work focuses on studying the interaction between the active biomolecules found in turmeric and pepper with key milk proteins, which is a popularly adopted in the <em>Siddha</em>, one of the Indian Traditional Medicinal Systems, to treat cold and throat-related illnesses. Curcumin and Piperine are the active biomolecules in turmeric and pepper, respectively. Hence, we have analyzed their interaction with key milk proteins such as Bovine Serum Albumin (BSA), Lactaglobulin, and Lactalbumin. The interactions were computationally investigated to elucidate the underlying mechanism behind the efficacy of the aforementioned formulation using accurate first-principle calculations based on Density Functional Theory (DFT) and Molecular Docking simulations. The formation of the Curcumin-Piperine (CP) complex, as well as its binding with milk proteins, was evaluated using computational techniques. We have predicted the allosteric sites of the milk proteins and investigated the allosteric regulation effect in these proteins by Curcumin and Piperine. The results revealed the formation and increased bioactivity of the drug complex, thereby providing a molecular basis for the observed synergistic efficacy of this traditional formulation.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108672"},"PeriodicalIF":3.1,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeting TNBC metastasis: In-silico identification of natural origin ROCK inhibitors via virtual screening, ADMET profiling, MM-GBSA, DFT, and molecular dynamics","authors":"Krishna Shevate ,&nbsp;Kalirajan Rajagopal ,&nbsp;Gowramma Byran ,&nbsp;Apsara Unni ,&nbsp;Justin Antony","doi":"10.1016/j.compbiolchem.2025.108671","DOIUrl":"10.1016/j.compbiolchem.2025.108671","url":null,"abstract":"<div><h3>Objective</h3><div>Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer characterized by lower survival and over 90 % of deaths from metastases. Rho-associated coiled-coil kinase (ROCK) plays a key role in TNBC progression by regulating cell motility and invasion. This study aims to identify novel ROCK inhibitors of natural origin that may suppress metastasis in TNBC, leveraging a computational drug discovery approach.</div></div><div><h3>Methods</h3><div>The Coconut database (1,23,971 natural compounds) was utilized for virtual screening. Further pharmacokinetic and pharmacodynamic properties were predicted using the SMARTCyp web server and the Schrodinger suite QikProp module. The top-ranking hits were further investigated using MM-GBSA binding free energy calculations, DFT analysis for electronic property evaluation, and MD simulations to assess structural stability and dynamic behavior of the ligand–protein complexes.</div></div><div><h3>Results</h3><div>Natural compounds (CNP0115371, CNP0232719, CNP0328678, CNP0182511, and CNP0108029) predicted to exhibit strong ROCK binding affinity (-13.18 to −12.04 kcal/mol), favorable ADMET properties, and stable interaction profiles. DFT calculations revealed high chemical reactivity and electron distribution suitable for biological interaction. MD simulations confirmed stable protein-ligand interactions over a 100-ns trajectory, supporting the compounds' potential as ROCK inhibitors. The study identified five promising naturally origin compounds as potential ROCK inhibitors with anti-metastatic relevance for TNBC treatment. These findings need further experimental validation to confirm their therapeutic efficacy and develop novel anti-TNBC agents from natural origins.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108671"},"PeriodicalIF":3.1,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}