モンテカルロ法を用いた有機化学物質の生態毒性予測におけるQSTRモデルの構築
This study developed Quantitative Structure-Toxicity Relationship (QSTR) models to evaluate the acute aquatic toxicity of 334 organic compounds toward green algae, expressed as EC50 and EC10 values. Using CORAL software, hybrid optimal descriptors derived from SMILES notation and hydrogen-suppressed molecular graphs (HSG) were combined with the index of ideality of correlation (IIC) as the target function. The best-performing models (Split 3) yielded validation set R² values of 0.7849 for pEC50 and 0.8150 for pEC10, indicating good predictive capability. Structural fragment analysis revealed that hydrophilic moieties reduce algal toxicity, whereas lipophilic carbon-chain fragments increase it. These findings support the use of Monte Carlo-based QSTR modeling as a computational tool for ecotoxicological risk screening.
Hydrophilic structural fragments reduce aquatic toxicity toward green algae, while lipophilic carbon-chain fragments increase molecular lipophilicity and consequently enhance algal toxicity, as identified through QSTR descriptor analysis.
The delivery route is not clearly identifiable from this paper. For hydrogen intake, inhalation is the most efficient route; inhalation, however, carries explosion risk (empirical LFL of 10%; high-concentration devices are not recommended).
See also:
https://h2-papers.org/en/papers/36199875