用开源机器学习方法评估分子复杂性,以预测过程质量强度
Nicole Tin1, Mandeep Chauhan2, Kennedy Agwamba1
1Analytical Sciences, Gilead Sciences Inc, Foster City, California 94404-1147, United States.
ACS omega
|July 8, 2024
概括
制药制造中的绿色化学得到了新的开源工具的推进,该工具可以预测工艺质量强度 (PMI) 目标. 该工具使用分子复杂性来优化资源效率并推动可持续实践.
科学领域:
- 制药制造业 制药制造业 制药制造业
- 绿色化学 绿色化学
- 计算化学计算化学
背景情况:
- 过程质量强度 (PMI) 是制药制造业资源效率的一个关键指标.
- 准确的PMI目标设定对于可持续实践来说具有挑战性和关键性.
- 现有的机器学习工具显示出潜力,但需要进一步开发以获得更广泛的应用.
研究的目的:
- 将SMART-PMI工具改进和扩展为一个开源模型和应用程序.
- 提高用于设定PMI目标的分子复杂性的预测.
- 在制药工艺开发中促进可解释性和知情决策.
主要方法:
- 开发了一个基于四个分子描述符的开源机器学习模型:异原子数,立体中心数,独特的拓扭曲和连接指数chi4n.
- 与分子复杂性相关的分子特征,以预测PMI目标.
- 创建了一个用户友好的应用程序,接受SDF文件,以快速量化复杂性和生成PMI目标.
主要成果:
- 改进后的模型实现了82.6%的预测准确度和0.349RMSE的分子复杂性.
- 开发的应用程序提供了可访问的PMI目标,以指导流程开发.
- 该模型强调可解释性和节性,以更好地理解和决策.
结论:
- 开源的SMART-PMI工具提供了一种灵活和可解释的方法来优化制药制造流程.
- 这一进步支持绿色化学原则和可持续实践的采用.
- 综合工具促进了数据驱动的流程开发,以提高资源效率.
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