Molecular Diversity最新文献_第2页

QMGBP-DL: a deep learning and machine learning approach for quantum molecular graph band-gap prediction. QMGBP-DL：量子分子图带隙预测的深度学习和机器学习方法。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-04-19 DOI: 10.1007/s11030-025-11178-7

Outhman Abbassi, Soumia Ziti

{"title":"QMGBP-DL: a deep learning and machine learning approach for quantum molecular graph band-gap prediction.","authors":"Outhman Abbassi, Soumia Ziti","doi":"10.1007/s11030-025-11178-7","DOIUrl":"10.1007/s11030-025-11178-7","url":null,"abstract":"Predicting molecular and quantum material properties, especially the band gap, is crucial for accelerating discoveries in drug design and material science. Although graph neural networks and probabilistic encoders are well established in molecular data analysis, their targeted integration and application for band-gap prediction remain an active research area. This paper introduces QMGBP-DL, a deep learning approach that combines a molecular graph encoder with machine learning models to improve the prediction accuracy of molecular and material band-gap energy. The encoder uses graph convolutional networks to derive latent representations of chemical structures from SMILES strings, optimized via Kullback-Leibler divergence loss. These representations serve as inputs for training various machine learning models to predict properties. QMGBP-DL's effectiveness is assessed using the QM9, PCQM4M, and OPV datasets, demonstrating significant improvements, particularly with a random forest model for property prediction. A comparative analysis against established approaches DenseGNN, MEGNet, and ALIGNN reveals that QMGBP-DL excels in predicting HOMO, LUMO, and band gap, achieving notably lower MAE values. The integration of GCN-derived latent spaces with traditional machine learning models, especially Random Forest, provides a powerful approach for band-gap prediction. The results highlight the efficacy of our integrated approach, showcasing that graph-based molecular encoding combined with machine learning, particularly Random Forest, is highly effective for accurate band-gap prediction, thereby facilitating material discovery and design.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3501-3515"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143954943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Python tools for structural tasks in chemistry. 用于化学结构任务的 Python 工具。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2024-05-14 DOI: 10.1007/s11030-024-10889-7

Fedor V Ryzhkov, Yuliya E Ryzhkova, Michail N Elinson

引用次数: 0

GraphkmerDTA: integrating local sequence patterns and topological information for drug-target binding affinity prediction and applications in multi-target anti-Alzheimer's drug discovery. GraphkmerDTA：整合局部序列模式和拓扑信息进行药物-靶点结合亲和力预测及在多靶点抗阿尔茨海默病药物发现中的应用。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-01-10 DOI: 10.1007/s11030-024-11065-7

Zuolong Zhang, Gang Luo, Yixuan Ma, Zhaoqi Wu, Shuo Peng, Shengbo Chen, Yi Wu

{"title":"GraphkmerDTA: integrating local sequence patterns and topological information for drug-target binding affinity prediction and applications in multi-target anti-Alzheimer's drug discovery.","authors":"Zuolong Zhang, Gang Luo, Yixuan Ma, Zhaoqi Wu, Shuo Peng, Shengbo Chen, Yi Wu","doi":"10.1007/s11030-024-11065-7","DOIUrl":"10.1007/s11030-024-11065-7","url":null,"abstract":"Identifying drug-target binding affinity (DTA) plays a critical role in early-stage drug discovery. Despite the availability of various existing methods, there are still two limitations. Firstly, sequence-based methods often extract features from fixed length protein sequences, requiring truncation or padding, which can result in information loss or the introduction of unwanted noise. Secondly, structure-based methods prioritize extracting topological information but struggle to effectively capture sequence features. To address these challenges, we propose a novel deep learning model named GraphkmerDTA, which integrates Kmer features with structural topology. Specifically, GraphkmerDTA utilizes graph neural networks to extract topological features from both molecules and proteins, while fully connected networks learn local sequence patterns from the Kmer features of proteins. Experimental results indicate that GraphkmerDTA outperforms existing methods on benchmark datasets. Furthermore, a case study on lung cancer demonstrates the effectiveness of GraphkmerDTA, as it successfully identifies seven known EGFR inhibitors from a screening library of over two thousand compounds. To further assess the practical utility of GraphkmerDTA, we integrated it with network pharmacology to investigate the mechanisms underlying the therapeutic effects of Lonicera japonica flower in treating Alzheimer's disease. Through this interdisciplinary approach, three potential compounds were identified and subsequently validated through molecular docking studies. In conclusion, we present not only a novel AI model for the DTA task but also demonstrate its practical application in drug discovery by integrating modern AI approaches with traditional drug discovery methodologies.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3147-3164"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142942360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning-based activity prediction of phenoxy-imine catalysts and its structure-activity relationship study. 基于机器学习的苯氧基亚胺催化剂活性预测及其构效关系研究。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-03-07 DOI: 10.1007/s11030-025-11147-0

Xiaoke Zhou, Sisi He, Min Xiao, Jing He, Yuan Wang, Yuanqin Zhu, Haixiang He

{"title":"Machine learning-based activity prediction of phenoxy-imine catalysts and its structure-activity relationship study.","authors":"Xiaoke Zhou, Sisi He, Min Xiao, Jing He, Yuan Wang, Yuanqin Zhu, Haixiang He","doi":"10.1007/s11030-025-11147-0","DOIUrl":"10.1007/s11030-025-11147-0","url":null,"abstract":"This study systematically investigates the structure-activity relationships of 30 Ti-phenoxy-imine (FI-Ti) catalysts using machine learning (ML) approaches. Among the tested algorithms, XGBoost demonstrated superior predictive performance, achieving R2 values of 0.998 (training set) and 0.859 (test set), with a cross-validated Q2 of 0.617. Feature importance analysis identified three composite descriptors-ODI_HOMO_1_Neg_Average GGI2, ALIEmax GATS8d, and Mol_Size_L-as critical contributors, collectively accounting for > 63% of the model's predictive power. Polynomial feature expansion effectively captured nonlinear interactions between descriptors, while SHAP and ICE analyses enhanced interpretability, revealing threshold effects and descriptor-specific trends. However, the model's generalizability may be constrained by the limited dataset size (30 samples) and reliance on density functional theory (DFT)-derived descriptors, necessitating experimental validation. Additionally, the study focused solely on ethylene polymerization at 40 °C; broader applicability to diverse catalytic systems or reaction conditions requires further validation. These findings provide a data-driven framework for catalyst design, though future work should integrate experimental validation and expand datasets to refine predictive robustness.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3411-3422"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing antimalarial drug discovery: ensemble machine learning models for predicting PfPK6 inhibitor activity. 推进抗疟药物的发现：预测PfPK6抑制剂活性的集成机器学习模型。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-04-22 DOI: 10.1007/s11030-025-11203-9

Maryam Gholami, Mohammad Asadollahi-Baboli

{"title":"Advancing antimalarial drug discovery: ensemble machine learning models for predicting PfPK6 inhibitor activity.","authors":"Maryam Gholami, Mohammad Asadollahi-Baboli","doi":"10.1007/s11030-025-11203-9","DOIUrl":"10.1007/s11030-025-11203-9","url":null,"abstract":"Malaria is a significant global health challenge, causing high morbidity and mortality. The rise of drug resistance highlights the urgent need for new antimalarial agents. This study focuses on predictive modeling of 104 Plasmodium falciparum protein kinase 6 (PfPK6) inhibitors, employing a range of machine learning techniques to develop ensemble regression and classification models. Molecular descriptors were refined using classification and regression trees (CART) to identify the most relevant features. Six machine learning algorithms (Random Forest (RF), Relevance Vector Machine (RVM), Support Vector Machine (SVM), Cubist, Artificial Neural Networks (ANN), and XGBoost) were utilized to construct regression models. The consensus model demonstrated superior predictive performance, achieving R2Test = 0.94, SETest = 0.20, Q2CV = 0.90, and SECV = 0.25, outperforming individual models. For classification tasks, five algorithms were evaluated and a majority voting approach yielded an accuracy of 91% and a sensitivity of 93%. The robustness of the models was confirmed through applicability domain analysis (96% coverage) and y-randomization tests, ensuring that the predictive outcomes were not due to chance correlations. This study highlights the effectiveness of ensemble machine learning approaches in predictive modeling and provides critical insights for the rational design of novel PfPK6 inhibitors.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3575-3586"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143951382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Targeting Poly (ADP-ribose) polymerase-1 (PARP-1) for DNA repair mechanism through QSAR-based virtual screening and MD simulation. 通过基于qsar的虚拟筛选和MD模拟，靶向聚（adp -核糖）聚合酶1 （PARP-1）的DNA修复机制。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-04-14 DOI: 10.1007/s11030-025-11184-9

Kun Cao, Ruonan Wang, Siyu Wu, Dong Ou, Ruixue Li, Lianhai Li, Xinguang Liu

{"title":"Targeting Poly (ADP-ribose) polymerase-1 (PARP-1) for DNA repair mechanism through QSAR-based virtual screening and MD simulation.","authors":"Kun Cao, Ruonan Wang, Siyu Wu, Dong Ou, Ruixue Li, Lianhai Li, Xinguang Liu","doi":"10.1007/s11030-025-11184-9","DOIUrl":"10.1007/s11030-025-11184-9","url":null,"abstract":"Poly (ADP-ribose) polymerase-1 (PARP-1) is a key enzyme in the base excision repair pathway, crucial for maintaining genomic stability by repairing DNA breaks. In cancers with mutations in DNA repair genes, such as BRCA1 and BRCA2, PARP-1 activity becomes essential for tumor cell survival, making it a promising target for therapeutic intervention. This study employs QSAR modeling, virtual screening, and molecular dynamics (MD) simulations to identify potential PARP-1 inhibitors. A dataset of inhibitors was analyzed using 12 molecular fingerprint descriptors to develop robust QSAR models, with the optimal model based on the CDK descriptor achieving R2 = 0.96, Q2_CV = 0.78, and Q2_Ext = 0.80. The model was applied to virtually screen three chemical libraries-ZINC, FDA, and NPA-identifying promising candidates for PARP-1 inhibition. Molecular docking revealed that compounds ZINC13132446, Z2037280227, and NPC193377 have strong binding affinity for the PARP-1 active site. MD simulations and MM-PBSA confirmed the stability of these complexes, with Z2037280227 and NPC193377 exhibiting the most stable interactions. These results underscore the potential of targeting PARP-1 as a therapeutic strategy for cancers with homologous recombination deficiencies, including prostate, breast, and ovarian cancer, particularly in patients with DNA repair deficiencies.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3517-3535"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143957585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HDAC3_VS_assistant: cheminformatics-driven discovery of histone deacetylase 3 inhibitors. HDAC3_VS_assistant：化学信息学驱动的组蛋白去乙酰化酶3抑制剂的发现。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2024-12-23 DOI: 10.1007/s11030-024-11066-6

Oleg V Tinkov, Veniamin Y Grigorev

{"title":"HDAC3_VS_assistant: cheminformatics-driven discovery of histone deacetylase 3 inhibitors.","authors":"Oleg V Tinkov, Veniamin Y Grigorev","doi":"10.1007/s11030-024-11066-6","DOIUrl":"10.1007/s11030-024-11066-6","url":null,"abstract":"Histone deacetylase 3 (HDAC3) inhibitors keep significant therapeutic promise for treating oncological, neurodegenerative, and inflammatory diseases. In this work, we developed robust QSAR regression models for HDAC3 inhibitory activity and acute toxicity (LD50, intravenous administration in mice). A total of 1751 compounds were curated for HDAC3 activity, and 15,068 for toxicity. The models employed molecular descriptors such as Morgan fingerprints, MACCS-166 keys, and Klekota-Roth, PubChem fingerprints integrated with machine learning algorithms including random forest, gradient boosting regressor, and support vector machine. The HDAC3 QSAR models achieved Q2test values of up to 0.76 and RMSE values as low as 0.58, while toxicity models attained Q2test values of 0.63 and RMSE values down to 0.41, with applicability domain (AD) coverage exceeding 68%. Internal validation by fivefold cross-validation (Q2cv = 0.70 for HDAC3 and 0.60 for toxicity) and y-randomization confirmed model reliability. Shapley additive explanation (SHAP) was also used to explain the influence of modeling features on model prediction results. The most predictive QSAR models are integrated into the developed HDAC3_VS_assistant application, which is freely available at https://hdac3-vs-assistant-v2.streamlit.app/ . Virtual screening conducted using the HDAC3_VS_assistant web application allowed us to reveal a number of potential inhibitors, and the nature of their bonds with the active HDAC3 site was additionally investigated by molecular docking.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3165-3187"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating machine learning and structural dynamics to explore B-cell lymphoma-2 inhibitors for chronic lymphocytic leukemia therapy. 结合机器学习和结构动力学探索b细胞淋巴瘤-2抑制剂治疗慢性淋巴细胞白血病。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-01-09 DOI: 10.1007/s11030-024-11079-1

Rima Bharadwaj, Amer M Alanazi, Vivek Dhar Dwivedi, Sarad Kumar Mishra

{"title":"Integrating machine learning and structural dynamics to explore B-cell lymphoma-2 inhibitors for chronic lymphocytic leukemia therapy.","authors":"Rima Bharadwaj, Amer M Alanazi, Vivek Dhar Dwivedi, Sarad Kumar Mishra","doi":"10.1007/s11030-024-11079-1","DOIUrl":"10.1007/s11030-024-11079-1","url":null,"abstract":"Chronic lymphocytic leukemia (CLL) is a malignancy caused by the overexpression of the anti-apoptotic protein B-cell lymphoma-2 (BCL-2), making it a critical therapeutic target. This study integrates computational screening, molecular docking, and molecular dynamics to identify and validate novel BCL-2 inhibitors from the ChEMBL database. Starting with 836 BCL-2 inhibitors, we performed ADME and Lipinski's Rule of Five (RO5) filtering, clustering, maximum common substructure (MCS) analysis, and machine learning models (Random Forest, SVM, and ANN), yielding a refined set of 124 compounds. Among these, 13 compounds within the most common substructure (MCS1) cluster showed promising features and were prioritized. A docking-based re-evaluation highlighted four lead compounds-ChEMBL464268, ChEMBL480009, ChEMBL464440, and ChEMBL518858-exhibiting notable binding affinities. Although a reference molecule outperformed in docking, molecular dynamics (MD), and binding energy analyses, it failed ADME and Lipinski criteria, unlike the selected leads. Further validation through MD simulations and MM/GBSA energy calculations confirmed stable binding interactions for the leads, with ChEMBL464268 showing the highest stability and binding affinity (ΔGtotal = - 80.35 ± 11.51 kcal/mol). Free energy landscape (FEL) analysis revealed stable energy minima for these complexes, underscoring conformational stability. Despite moderate activity (pIC₅₀ values from 4.3 to 5.82), the favorable pharmacokinetic profiles of these compounds position them as promising BCL-2 inhibitor leads, with ChEMBL464268 emerging as the most promising candidate for further CLL therapeutic development.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3233-3252"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142942361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

General structure-activity relationship models for the inhibitors of Adenosine receptors: A machine learning approach. 腺苷受体抑制剂的一般结构-活性关系模型：机器学习方法。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-01-20 DOI: 10.1007/s11030-024-11096-0

M Janbozorgi, S Kaveh, M S Neiband, A Mani-Varnosfaderani

{"title":"General structure-activity relationship models for the inhibitors of Adenosine receptors: A machine learning approach.","authors":"M Janbozorgi, S Kaveh, M S Neiband, A Mani-Varnosfaderani","doi":"10.1007/s11030-024-11096-0","DOIUrl":"10.1007/s11030-024-11096-0","url":null,"abstract":"Adenosine receptors (A1, A2a, A2b, A3) play critical roles in cellular signaling and are implicated in various physiological and pathological processes, including inflammations and cancer. The main aim of this research was to investigate structure-activity relationships (SAR) to derive models that describe the selectivity and activity of inhibitors targeting Adenosine receptors. Structural information for 16,312 inhibitors was collected from BindingDB and analyzed using machine learning methods. 450 molecular descriptors were calculated for each molecule and compounds were classified based on their activity levels and therapeutic targets. The variable importance in projection (VIP) algorithm identified key discriminating features. Classification models were built using supervised Kohonen networks (SKN) and counter-propagation artificial neural networks (CPANN) algorithms. Model validity was assessed via cross-validation, applicability domain analysis, and test sets. These models were then used to screen a random subset of 2 million molecules from the ZINC database. Three descriptors-hydrophilic factor (Hy), ratio of multiple path count over path count (PCR), and asphericity (ASP)-were identified as critical for discriminating active and inactive inhibitors. SKN models exhibited high sensitivity (0.88-0.99) and yielded an average area under the curve (AUC) of 0.922 for virtual screening. This study aimed to enhance the development of highly selective Adenosine receptor ligands for diverse therapeutic applications by identifying critical molecular features specific to each isoform.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3253-3272"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142998221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing kinase and PARP inhibitor combinations through machine learning and in silico approaches for targeted brain cancer therapy. 通过机器学习和计算机方法优化激酶和PARP抑制剂组合用于靶向脑癌治疗。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-01-22 DOI: 10.1007/s11030-025-11114-9

Alireza Poustforoosh

{"title":"Optimizing kinase and PARP inhibitor combinations through machine learning and in silico approaches for targeted brain cancer therapy.","authors":"Alireza Poustforoosh","doi":"10.1007/s11030-025-11114-9","DOIUrl":"10.1007/s11030-025-11114-9","url":null,"abstract":"The drug combination is an attractive approach for cancer treatment. PARP and kinase inhibitors have recently been explored against cancer cells, but their combination has not been investigated comprehensively. In this study, we used various drug combination databases to build ML models for drug combinations against brain cancer cells. Some decision tree-based models were used for this purpose. The results were further evaluated using molecular docking and molecular dynamics (MD) simulation. The possibility of the hit drug combinations for crossing the Blood-brain barrier (BBB) was also examined. Based on the obtained results, the combination of niraparib, as the PARP inhibitor, and lapatinib, as the kinase inhibitor, exhibited more considerable outcomes with a remarkable model performance (accuracy of 0.915) and prediction confidence of 0.92. The protein tweety homolog 3 and BTB/POZ domain-containing protein 2 are the main targets of niraparib and lapatinib with - 10.2 and - 8.5 scores, respectively. Due to the outcomes, this drug combination can use the CAT1 transporter on the BBB surface and effectively cross the BBB. Based on the obtained results, niraparib-lapatinib can be a promising drug combination candidate for brain cancer treatment. This combination is worth to be examined by experimental investigation in vitro and in vivo.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3293-3303"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142998058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0