深度学习的应用用于预测药品的持久性,生物积累和毒性
Dominga Evangelista1, Elliot Nelson2, Rachael Skyner2
1Department of Pharmacy and Biotechnology, Alma Mater Studiorum─University of Bologna, Via Belmeloro 6, Bologna 40126, Italy.
Journal of chemical information and modeling
|April 3, 2025
概括
这项研究使用深度学习模型来预测环境风险,如药品中的持久性,生物积累和毒性. 它在药物设计的早期确定了有关化学结构,以促进更绿色的药物开发.
科学领域:
- 计算化学计算化学
- 环境科学 环境科学
- 药物发现 药物发现 药物发现
背景情况:
- 制药发展在评估环境影响方面面临挑战.
- 预测持久性,生物积累和毒性 (PBT) 对可持续的药物设计至关重要.
- 早期识别PBT风险可以防止环境污染.
研究的目的:
- 应用深度学习模型来预测药物化合物的PBT特性.
- 在药物相关分子中识别潜在的PBT化学物质.
- 为早期药物设计警告提取PBT相关的子结构.
主要方法:
- 通过Chemprop利用传递信息的神经网络 (MPNN) 来进行PBT预测.
- 采用集群策略来进行可靠的模型性能评估.
- 将训练的模型应用于药物分子数据集.
主要成果:
- 深度学习模型成功预测了PBT的特征.
- 确定了与药品中的PBT问题相关的特定子结构.
- 突出显示具有潜在PBT问题的药物相关分子.
结论:
- 像MPNNs这样的深度学习模型是预测PBT属性的有效工具.
- 提取的基层结构可以作为药物设计中的环境风险评估的有价值的标志.
- 整合这些预测可以导致更环保的药物候选者.
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