配体生成器：一种用于预测化合物性质的图神经网络，具有鲁棒的解释
Ligandformer: A Graph Neural Network for Predicting Compound Property with Robust Interpretation
AI总结：
本文提出Ligandformer，一种基于多层自注意力机制的图神经网络，用于预测化合物性质并提供鲁棒解释，提升了预测的准确性和泛化能力。
摘要：
鲁棒且高效的QSAR方法解释对于验证AI预测的合理性、理解复杂化学或生物过程机制以及为制药行业提供结构优化的启发性想法非常有用。为此，我们构建了一个基于多层自注意力的图神经网络框架，即Ligandformer，用于预测化合物性质并提供解释。Ligandformer整合了不同网络块中的化合物结构注意力图。整合的注意力图反映了机器对化合物结构的局部兴趣，并表明预测的化合物性质与其结构之间的关系。本工作主要贡献有三个方面：1. Ligandformer直接打开深度学习方法的黑箱，提供化学结构的局部预测理由。2. Ligandformer在不同实验轮次中提供稳健的预测，克服了深度学习方法的普遍预测不稳定性。3. Ligandformer可以推广到预测不同化学或生物性质，具有高性能。此外，Ligandformer可以同时输出特定性质分数和结构上的可见注意力图，这有助于研究人员高效地研究化学或生物性质并优化结构。我们的框架在准确度、鲁棒性和泛化能力方面优于其他方法，可以应用于复杂系统研究。
英文摘要：
Robust and efficient interpretation of QSAR methods is quite useful to validate AI prediction rationales with subjective opinion (chemist or biologist expertise), understand sophisticated chemical or biological process mechanisms, and provide heuristic ideas for structure optimization in pharmaceutical industry. For this purpose, we construct a multi-layer self-attention based Graph Neural Network framework, namely Ligandformer, for predicting compound property with interpretation. Ligandformer integrates attention maps on compound structure from different network blocks. The integrated attention map reflects the machine's local interest on compound structure, and indicates the relationship between predicted compound property and its structure. This work mainly contributes to three aspects: 1. Ligandformer directly opens the black-box of deep learning methods, providing local prediction rationales on chemical structures. 2. Ligandformer gives robust prediction in different experimental rounds, overcoming the ubiquitous prediction instability of deep learning methods. 3. Ligandformer can be generalized to predict different chemical or biological properties with high performance. Furthermore, Ligandformer can simultaneously output specific property score and visible attention map on structure, which can support researchers to investigate chemical or biological property and optimize structure efficiently. Our framework outperforms over counterparts in terms of accuracy, robustness and generalization, and can be applied in complex system study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
