在药物发现中积极学习的现状和挑战
Lei Wang1, Zhenran Zhou2, Xixi Yang2
1Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, China.
Drug discovery today
|April 20, 2024
概括
积极学习 (AL) 在药物发现中有效地找到有价值的数据,即使信息有限. 本综述涵盖了跨药物开发阶段的AL应用,强调了挑战和未来方向.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习是机器学习.
背景情况:
- 药物发现面临诸如巨大的化学空间和有限的标记数据等挑战.
- 积极学习 (AL) 为数据识别提供了一个高效的代反过程.
研究的目的:
- 综合审查AL在药物发现中的应用.
- 讨论在药物开发中对AL的当前挑战和未来前景.
主要方法:
- 在药物发现阶段对AL应用的文献综述.
- 分析AL在预测,选,生成和优化中的作用.
主要成果:
- 对于化合物-标相互作用预测和虚拟查来说,AL非常有价值.
- AL有助于分子生成,优化和属性预测.
- AL解决了药物发现中的数据局限性.
结论:
- 积极学习在药物开发中越来越突出.
- 需要进一步的研究来克服挑战,并实现AL在药物发现中的全部潜力.
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