机器学习能够准确预测药物代谢过程中子形成的情况
1Department of pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar 160062, Punjab, India.
研究人员开发了机器学习模型,以预测类代谢物,即与药物毒性相关的反应性中间体. 最好的模型实现了86.27%的准确性,超过了现有的工具,并帮助早期发现药物.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药物新陈代谢 药物新陈代谢
背景情况:
- 药物代谢对于消除异生菌来说至关重要,但可以将药物生物活化为对毒性负责的反应性代谢物.
- 昆占已识别的活性代谢物的40%以上,在药物开发中构成重大挑战.
- 早期识别潜在的类形成药物候选药物对于减轻药物发现中的风险至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 和深度学习 (DL) 模型,用于预测类代谢物的形成.
- 将开发模型的性能与现有工具 (如Xenosite web服务器) 的性能进行比较.
- 识别与子形成相关的分子描述符,以指导药物设计.
主要方法:
- 为训练和测试预测模型,策划了510个分子的数据集.
- 分子表示包括2D描述符,PubChem指纹,E状态指纹和代谢反应性描述符.
- 模型在一个由102个分子组成的独立测试集上进行了评估,并与Xenosite.com进行了比较.
主要成果:
- 最好的ML模型在预测子形成方面实现了86.27%的准确性,明显超过了Xenosite的52.94%准确性.
- 分析表明,极地部分阻碍了子的形成.
- 芳香原子和特定的SMARTCyp代谢体 (V51,V52,V53) 的存在降低了子生成的可能性.
结论:
- 开发的ML模型为预测类代谢物形成提供了可靠和准确的方法.
- 这些发现提供了对影响代谢的结构特征的见解,有助于设计更安全的候选药物.
- 研究人员可以使用基于这些模型的公开可访问工具来评估子形成潜力.
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