卷积神经网络与SMILES多重性的协同作用改进分子预测
Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction
摘要：
在我们的研究中，我们证明了卷积神经网络和SMILES的多重性之间的协同效应。我们提出的模型，即所谓的卷积神经指纹（CNF）模型，达到了传统描述符的准确性，如Dragon（Mauri等人。[22]），RDKit（Landrum [18]），CDK2（Willighagen等人。[43]）和PyDescriptor（Masand和Rastija [20]）。此外，CNF模型通常比高度微调的传统描述符表现得更好，特别是在小数据集上，这对于数据集通常由于实验成本、分子的可用性或对私有数据库的可访问性而较小的化学领域来说是非常感兴趣的。我们在训练和测试期间评估CNF模型以及SMILES增强。据我们所知，这是第一次提出这种方法。我们证明了在训练过程中使用SMILES的多重性作为正则化器，因此避免了过拟合，并且在考虑进行测试时可以被视为集成学习。
英文摘要：
In our study, we demonstrate the synergy effect between convolutional neural networks and the multiplicity of SMILES. The model we propose, the so-called Convolutional Neural Fingerprint (CNF) model, reaches the accuracy of traditional descriptors such as Dragon (Mauri et al. [22]), RDKit (Landrum [18]), CDK2 (Willighagen et al. [43]) and PyDescriptor (Masand and Rastija [20]). Moreover the CNF model generally performs better than highly fine-tuned traditional descriptors, especially on small data sets, which is of great interest for the chemical field where data sets are generally small due to experimental costs, the availability of molecules or accessibility to private databases. We evaluate the CNF model along with SMILES augmentation during both training and testing. To the best of our knowledge, this is the first time that such a methodology is presented. We show that using the multiplicity of SMILES during training acts as a regulariser and therefore avoids overfitting and can be seen as ensemble learning when considered for testing.
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