使用它们预测的合成途径预测分子的价格
Massina Abderrahmane1, Hamza Tajmouati1, Vinicius Barros Ribeiro da Silva1
1Iktos, 65 rue de Prony, 75017, Paris, France.
Molecular informatics
|January 31, 2025
概括
预测新型分子的成本对于药物发现至关重要. 本研究介绍了RetroPriceNet,这是一种深度学习模型,使用合成路径和起始材料成本估计复合价格,优于现有方法.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 机器学习是机器学习.
背景情况:
- 诸如QSAR模型和对接分数等众多指标有助于过用于合成的虚拟分子库.
- 现有的指标不考虑新型虚拟分子的成本,包括起始材料的可用性和价格.
- 准确的成本预测可以显著改善和加速药物开发和商品成本分析中的决策.
研究的目的:
- 调查预测的回合成路径和起始材料价格作为预测新分子成本的特征的实用性.
- 开发和展示一个深度学习模型,RetroPriceNet,用于准确的分子价格预测.
- 将合成可行性和起始材料经济学整合到计算药物设计中.
主要方法:
- 利用计算机辅助合成规划 (CASP) 来预测虚拟分子的回合成路径.
- 开发了一个深度学习模型,RetroPriceNet,利用预测的合成路线和起始材料价格.
- 在已知合成途径和相关成本的分子数据集上训练和评估模型.
主要成果:
- RetroPriceNet在预测分子价格方面表现出优异的表现,与持久测试集上的最先进模型相比.
- 该模型有效地将合成路径信息和起始材料成本纳入其价格预测中.
- 开发的方法为评估合成虚拟化合物的经济可行性提供了一个新的指标.
结论:
- 预测的回合成途径和起始材料价格是估计新分子成本的有价值特征.
- RetroPriceNet提供了一个强大的工具,通过提供准确的成本预测来加速药物发现的决策.
- 这项工作突出了将计算合成计划与分子设计中的经济因素相结合的潜力.
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