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解开分子设计的变压器模型的学习特性
Jannik P Roth1,2, Jürgen Bajorath1,2
1Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany.
药物设计中的变压器模型需要显著的数据相似性来准确地预测化合物. 这些模型将序列模式与分子结构关联起来,但没有学习与蛋白质-连接体相互作用相关的目标序列信息.
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 机器学习
背景情况:
- 从自然语言处理中调整的变压器网络越来越多地用于药物设计.
- 它们在基于序列的生成化合物设计中的应用要求对它们的学习能力进行调查.
研究的目的:
- 在基于序列的复合设计中探索变压器模型的学习特征.
- 确定这些模型是否学习了与蛋白质 - 配体相互作用相关的信息.
主要方法:
- 利用基于序列的生成化合物设计作为模型系统.
- 分析了基于训练和测试数据特征的活性化合物的变压器模型预测.
主要成果:
- 变压器模型需要训练和测试数据之间的至少60%的序列相似性才能进行准确的预测.
- 预测是由序列模式和分子结构之间的统计关联驱动的,这表明记忆效应.
- 模型没有学习对带结合至关重要的目标序列信息.
结论:
- 使用变压器的基于序列的复合设计严重依赖于数据相似性和统计模式匹配.
- 这些模型本质上没有学习生物相关的带结合的目标序列信息.
- 建议谨慎避免在这个领域过度解释当前变压器模型的功能.
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