An integrated environmental toxicity risk assessment framework combining deep learning and molecular simulation: A case study on pyrethrins and breast cancer
Jinghui Sung, Zikang Jiang, Wen‐Pei Sung, Weijie Li, Yixin Zhuang, Yuanpeng Huang · Biochemistry and Biophysics Reports · 2025
This study develops and validates a multi-scale integrative computational toxicology framework to systematically investigate the potential association between the natural pesticides pyrethrin I and II and breast cancer risk. The proposed approach integrates deep learning–based drug–target interaction prediction (via DeepPurpose), molecular docking and dynamics (MD) simulations, and protein–protein interaction (PPI) network modeling, forming a traceable risk inference chain from molecular-level interactions to clinically relevant outcomes. Experimental results revealed that pyrethrin I exhibits stable and high-affinity binding to key breast cancer–related proteins, including RPS6KB1, TNKS2, and MAOB, with a minimum binding free energy (ΔG) reaching −27.37 kcal/mol. These interactions potentially modulate tumor progression through key oncogenic pathways such as PI3K/AKT, Wnt/β-catenin, and metabolic reprogramming. RPS6KB1 is implicated in estrogen receptor–positive (ER+) breast cancer proliferation, while TNKS2 is closely associated with stemness maintenance and the aggressiveness of triple-negative breast cancer (TNBC). MAOB demonstrates the highest structural stability among complexes, making it a promising candidate for toxicological modeling. The study further introduces a cross-scale risk indicator modeling strategy, constructing a mechanistically interpretable chain from compound structure to protein modules, carcinogenic pathways, and clinical risks. This integrative methodology supports environmental exposure monitoring and toxicological policy development. The open-source, containerized analytical toolchain developed herein is highly extensible and adaptable for future toxicological evaluations of other natural compounds and emerging environmental pollutants. • First multi-scale framework linking molecules to clinical toxicology outcomes. • Pyrethrin I binds key breast cancer proteins with high affinity (ΔG ≤ −27.37). • MAOB validated as a key hub via MD simulations and network analysis. • Cross-scale model connects binding strength to clinical GWAS risk markers. • Open-source pipeline supports toxicology studies of natural and emerging toxins.