Since the introduction of artificial intelligence in medicinal chemistry, the
necessity has emerged to analyse how molecular property variation is modulated
by either single atoms or chemical groups. In this paper, we propose to train
graph-to-graph neural network using semi-supervised learning for attributing
structure-property relationships. As initial case studies we apply the method
to solubility and molecular acidity while checking its consistency in
comparison with known experimental chemical data. As final goal, our approach
could represent a valuable tool to deal with problems such as activity cliffs,
lead optimization and de-novo drug design.

本文提出使用半监督学习训练图形神经网络来归因结构 - 属性关系，并将此方法应用于溶解度和分子酸度两个案例研究，以验证其与已知实验化学数据的一致性。该方法可为活性崖、引物优化和新药设计等问题提供有价值的工具。

半监督 GCN 学习分子结构 - 活性相关性

Semi-Supervised GCN for learning Molecular Structure-Activity Relationships

Rationalizing which parts of a molecule drive the predictions of a molecular
graph convolutional neural network (GCNN) can be difficult. To help, we propose
two simple regularization techniques to apply during the training of GCNNs:
Batch Representation Orthonormalization (BRO) and Gini regularization. BRO,
inspired by molecular orbital theory, encourages graph convolution operations
to generate orthonormal node embeddings. Gini regularization is applied to the
weights of the output layer and constrains the number of dimensions the model
can use to make predictions. We show that Gini and BRO regularization can
improve the accuracy of state-of-the-art GCNN attribution methods on artificial
benchmark datasets. In a real-world setting, we demonstrate that medicinal
chemists significantly prefer explanations extracted from regularized models.
While we only study these regularizers in the context of GCNNs, both can be
applied to other types of neural networks

通过使用 Batch Representation Orthonormalization (BRO) 和 Gini regularization 的方法，在图卷积神经网络（GCNN）的训练过程中使预测更加准确。这种方法可以应用于其他类型的神经网络，并在药物化学领域实现了成功的应用。

正交化和感应稀疏化改进分子图神经网络解释性

Improving Molecular Graph Neural Network Explainability with  Orthonormalization and Induced Sparsity

Organic synthesis is one of the key stumbling blocks in medicinal chemistry.
A necessary yet unsolved step in planning synthesis is solving the forward
problem: given reactants and reagents, predict the products. Similar to other
work, we treat reaction prediction as a machine translation problem between
SMILES strings of reactants-reagents and the products. We show that a
multi-head attention Molecular Transformer model outperforms all algorithms in
the literature, achieving a top-1 accuracy above 90% on a common benchmark
dataset. Our algorithm requires no handcrafted rules, and accurately predicts
subtle chemical transformations. Crucially, our model can accurately estimate
its own uncertainty, with an uncertainty score that is 89% accurate in terms of
classifying whether a prediction is correct. Furthermore, we show that the
model is able to handle inputs without reactant-reagent split and including
stereochemistry, which makes our method universally applicable.

本文旨在提高有机合成中的反应预测的准确性，通过使用多头注意力分子变压器模型，能够在保持普适性的基础上，取得 90% 以上的前 1 准确率和 89% 的准确的不确定性估计得分，而无需手工制定规则。