当收益率预测没有收益率预测时:对当前挑战的概述
Varvara Voinarovska1,2, Mikhail Kabeshov1, Dmytro Dudenko3
1Molecular AI, Discovery Sciences R&D, AstraZeneca, 431 83 Gothenburg, Sweden.
Journal of chemical information and modeling
|December 20, 2023
概括
机器学习 (ML) 模型由于高维数据而难以预测复杂的化学性质. 本综述评估了化学信息学中的ML方法,强调了先进化学预测数据可用性和可转移性的挑战.
科学领域:
- 化学信息学是一种化学信息学.
- 计算化学的计算化学
- 机器学习 机器学习
背景情况:
- 预测先进的化学性质,如产量和合成可行性,对于化学研究至关重要.
- 当前的机器学习 (ML) 模型面临着挑战,因为化学预测涉及的高维度和众多变量.
研究的目的:
- 系统地评估化学信息学当前ML方法的有效性.
- 确定ML在预测化学性质方面的里程碑和局限性.
- 通过案例研究来研究数据可用性和可转移性问题.
主要方法:
- 对化学信息学中应用的ML方法的系统审查.
- 评估ML模型的性能,以预测化学性质.
- 案例研究分析侧重于数据的可用性和可转移性.
主要成果:
- 目前的ML技术显示出希望,但在预测复杂的化学性质方面面临重大障碍.
- 高维度和众多变量 (反应物,催化剂,条件) 使模型开发复杂化.
- 数据的可用性和可转移性仍然是该领域的关键挑战.
结论:
- 可靠的ML模型可以优化高通量实验,并增强反合成预测.
- 需要进行进一步的研究,以解决数据的局限性,并提高化学信息学中的模型通用性.
- 解决数据挑战是释放ML在化学性质预测中的全部潜力的关键.
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