Breast cancer resistance protein (BCRP), also known as ABCG2, plays a critical role in multidrug resistance by actively effluxing chemotherapeutic agents from cancer cells. This ATP-dependent transporter is overexpressed in various tumor types, leading to reduced drug accumulation and treatment failure. Natural products derived from plants have emerged as promising candidates for modulating BCRP activity due to their structural diversity and favorable safety profiles. The present study aimed to identify key molecular fingerprints responsible for the inhibition of BCRP using a multi-strategy computational approach. A dataset of 124 natural compounds with experimentally determined BCRP inhibitory activity was compiled from published literature. Compounds exhibiting ≤50% transport relative to control were classified as active inhibitors (n = 45), while those above 50% were deemed inactive (n = 74).
Monte Carlo optimization combined with correlation and logic (CORAL) software was employed to develop robust classification models. Twenty-one models were generated using diverse descriptors including SMILES strings, graph-based indices, and Morgan connectivity fingerprints (0ECk, 1ECk). The best-performing model (M3) utilized SMILES and GAO descriptors with 1ECk, achieving high sensitivity (1.00), specificity (0.947), accuracy (0.90), and Matthews correlation coefficient (0.7826). Structural and physicochemical interpretation (SPCI) analysis further revealed that unsubstituted phenyl rings, aromatic rings with branching, oxygen atoms bonded to sp³ carbons or aromatic rings, and ketone groups positively correlated with inhibitory activity. Conversely, ester linkages (COO), carboxylic acids (COOH), aliphatic hydroxyls (OH), and sp² carbons with double-bonded oxygen were identified as hinderers.ALPI Antibody Cancer
A QSAR-Co framework was used to build and validate classification models based on linear discriminant analysis (LDA) and random forest (RF). The RF model demonstrated superior performance with balanced accuracy of 0.938 and area under the ROC curve (AUROC) of 0.938. This model was applied to screen 573 natural anticancer compounds from the NPACT database, yielding 110 potential hits. SwissADME analysis filtered these based on Lipinski’s Rule of Five, lipophilicity, water solubility, and drug-likeness parameters. Medicinal chemistry alerts such as PAINS and Brenk toxicophores were excluded. Ultimately, eleven compounds—apigenin, alpinone, rohitukine, tetra-o-methylscutellarine, tricin, (S)-5-hydroxy-7,4′-dimethoxyflavanone, 3,3′-di-O-methylquercetin, hispidulin, 3,5,7-trihydroxyflavanol, 7-methoxy-beta-carboline-1-propionic acid, and secundiflorol H—were predicted to be potent, safe, and orally bioavailable BCRP inhibitors.WIBG Antibody web
Molecular docking studies were conducted against the human BCRP crystal structure (PDB ID: 6ETI).PMID:34713422 All eleven compounds bound within the primary cavity (cavity-1), with apigenin showing the strongest binding affinity (-9.0 kcal/mol). It formed hydrogen bonds with Thr435, π-π stacking with Phe439, and multiple van der Waals interactions with Met549, Val546, Thr542, and Leu555. These interactions closely resembled those of the co-crystallized ligand MZ29. The findings confirm that specific structural motifs—such as unsubstituted aromatics, methoxy groups, and ketones—are crucial for BCRP inhibition, while certain functional groups reduce efficacy. This work provides a rational foundation for designing next-generation natural-derived BCRP inhibitors to overcome multidrug resistance in breast cancer therapy.MedChemExpress (MCE) offers a wide range of high-quality research chemicals and biochemicals (novel life-science reagents, reference compounds and natural compounds) for scientific use. We have professionally experienced and friendly staff to meet your needs. We are a competent and trustworthy partner for your research and scientific projects.Related websites: https://www.medchemexpress.com
