一个系统的研究关键元素的基础分子属性预测的系统研究
Jianyuan Deng1, Zhibo Yang2, Hehe Wang3
1Stony Brook University, Department of Biomedical Informatics, Stony Brook, NY, 11794, USA.
Nature communications
|October 13, 2023
概括
人工智能 (AI) 在药物发现的分子性质预测方面取得了有限的成功. 数据集的大小对于表示学习模型的良好表现至关重要.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 药物发现 药物发现
背景情况:
- 人工智能 (AI) 在药物发现中越来越多地用于分子性质预测.
- 分子表示学习技术已经进步,但它们在预测中的潜在机制仍然不清楚.
- 这阻碍了人工智能驱动的药物发现的进展.
研究的目的:
- 通过使用多样化的表示,广泛评估用于分子性质预测的AI模型.
- 调查不同数据集大小的模型性能,包括低数据场景.
- 确定影响预测准确度和模型局限性的关键因素.
主要方法:
- 通过固定表示,SMILES序列和分子图表训练了62,820个模型.
- 在MoleculeNet上评估模型,与阿片类药物相关的和额外的活动数据集.
- 通过使用不同大小的数据集,在低数据和高数据制度中评估预测能力.
主要成果:
- 代表性学习模型在大多数分子性质预测任务上表现有限.
- 一些关键因素被确定为显著影响预测评估结果的关键因素.
- 发现活动悬崖在很大程度上影响了模型预测.
- 模型性能与数据集大小有很强的相关性,更大的数据集对于表示学习至关重要.
结论:
- 当前的代表性学习模型在分子性质预测方面表现出有限的有效性.
- 数据集大小是代表性学习在这个领域的成功的一个关键决定因素.
- 需要进一步的研究来理解和克服AI模型在药物发现中的局限性.
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