Molecular property prediction is essential in chemistry, especially for drug
discovery applications. However, available molecular property data is often
limited, encouraging the transfer of information from related data. Transfer
learning has had a tremendous impact in fields like Computer Vision and Natural
Language Processing signaling for its potential in molecular property
prediction. We present a pre-training procedure for molecular representation
learning using reaction data and use it to pre-train a SMILES Transformer. We
fine-tune and evaluate the pre-trained model on 12 molecular property
prediction tasks from MoleculeNet within physical chemistry, biophysics, and
physiology and show a statistically significant positive effect on 5 of the 12
tasks compared to a non-pre-trained baseline model.

本文旨在使用反应数据进行预训练分子表示的过程，运用在 MoleculeNet 的 12 个分子属性预测任务上，结果显著地提高了预测模型的表现。

使用反应预测预训练变形金刚进行分子属性预测

Pre-training Transformers for Molecular Property Prediction Using  Reaction Prediction

In drug-discovery-related tasks such as virtual screening, machine learning
is emerging as a promising way to predict molecular properties. Conventionally,
molecular fingerprints (numerical representations of molecules) are calculated
through rule-based algorithms that map molecules to a sparse discrete space.
However, these algorithms perform poorly for shallow prediction models or small
datasets. To address this issue, we present SMILES Transformer. Inspired by
Transformer and pre-trained language models from natural language processing,
SMILES Transformer learns molecular fingerprints through unsupervised
pre-training of the sequence-to-sequence language model using a huge corpus of
SMILES, a text representation system for molecules. We performed benchmarks on
10 datasets against existing fingerprints and graph-based methods and
demonstrated the superiority of the proposed algorithms in small-data settings
where pre-training facilitated good generalization. Moreover, we define a novel
metric to concurrently measure model accuracy and data efficiency.

本文介绍了一种基于 SMILES Transformer 的分子指纹预测方法，该方法在虚拟筛选和其他药物发现任务中表现出优越性，在小数据集和复杂分析环境中表现良好。