推进算法药物产品开发:在药物配方中推机器学习方法
Jack D Murray1, Justus J Lange2, Harriet Bennett-Lenane1
1School of Pharmacy, University College Cork, Cork, Ireland.
人工智能 (AI) 和机器学习 (ML) 可以彻底改变制药开发. 这次审查突出了低于最佳的建模实践,并建议可靠,透明和可靠的ML方法用于药物配方.
科学领域:
- 制药科学 制药科学
- 计算化学的计算化学
- 生物技术是生物技术.
背景情况:
- 人工智能 (AI) 在制药行业提供了变革性的潜力,影响药物发现,开发和临床实践.
- 机器学习 (ML) 是人工智能的子集,在模拟和临床翻译方面取得了重大进展.
- 目前药物配方开发中的数据驱动建模面临挑战,包括有限的具体指导和低于最佳的做法,导致不可靠的预测.
研究的目的:
- 审查药物配方开发中的数据驱动建模方法.
- 在制药开发中识别当前机器学习应用中的局限性和次优实践.
- 为药物产品开发中可靠,透明和可靠的机器学习研究提供建议.
主要方法:
- 关于制药配方中的数据驱动建模和机器学习现有文献的审查.
- 对应用机器学习到药物产品开发中的当前趋势和实践的分析.
- 识别建模指导中的差距,探索未充分利用的方法,如数据取和组合建模.
主要成果:
- 趋向于低于最佳的建模实践和缺乏药物产品开发的具体指导.
- 过度强调基准实验结果,并偏爱高精度的黑子模型而不是可解释的模型.
- 数据取和组合建模方法的有限探索,阻碍了机器学习的全部潜力.
结论:
- 由于缺乏透明度和可解释性,目前在药物配方中的机器学习实践可能会产生不可靠的预测.
- 提出了建议,以提高机器学习模型在药物开发中的可信度,透明度和可靠性.
- 未来的研究应该专注于开发可靠的模型,为制定者提供实际指导,超越黑子方法.
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