变压器用于分子性质预测:从过去五年中吸取的教训
Afnan Sultan1, Jochen Sieg2, Miriam Mathea2
1Data Driven Drug Design, Center for Bioinformatics, Saarland University, Saarbrücken 66123, Germany.
Journal of chemical information and modeling
|August 13, 2024
概括
变压器模型在药物发现和环境科学中对分子性质预测 (MPP) 有希望. 本综述分析了当前的模型,培训策略,并确定了促进MPP的研究差距.
科学领域:
- 计算化学的计算化学
- 机器学习 机器学习
- 药物发现 药物发现 药物发现
背景情况:
- 分子性质预测 (MPP) 对于药物发现,作物保护和环境科学至关重要.
- 针对MPP的计算技术已经从经典的机器学习演变为深度学习.
- 变压器模型是MPP的最新进展.
研究的目的:
- 审查和提炼关于MPP变压器模型应用的见解.
- 分析现有的变压器模型,并确定其培训和微调的关键考虑因素.
- 突出未充分探索的研究领域和现场挑战.
主要方法:
- 对MPP的变压器模型进行当前研究的分析.
- 探索变压器模型培训和微调中的关键问题.
- 识别模型比较和标准化中的挑战.
主要成果:
- 分析了关于MPP中变压器模型预训练数据,架构和目标的关键问题.
- 发现了当前研究中的差距,并提出了未来探索的途径.
- 突出了比较不同MPP模型的挑战,强调了标准化的必要性.
结论:
- 变压器模型为推进MPP提供了巨大的潜力.
- 需要进一步的研究来优化预训练策略和解决模型比较挑战.
- 标准化的方法对于基于变压器的MPP的强有力的评估和进展至关重要.
关键词:
分子属性预测的预测斯米莱斯 (SMILES) 是一个有趣的小孩.基准测试 (benchmarking) 是一种比较的方法.化学语言模型 化学语言模型深度学习是一种深度学习.域名知识域名知识域名知识精细调整 精细调整预训练的预训练基于序列的化学模型.系统分析系统分析.变压器 变压器更多相关视频
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