With the emergence of Transformer architectures and their powerful
understanding of textual data, a new horizon has opened up to predict the
molecular properties based on text description. While SMILES are the most
common form of representation, they are lacking robustness, rich information
and canonicity, which limit their effectiveness in becoming generalizable
representations. Here, we present GPT-MolBERTa, a self-supervised large
language model (LLM) which uses detailed textual descriptions of molecules to
predict their properties. A text based description of 326000 molecules were
collected using ChatGPT and used to train LLM to learn the representation of
molecules. To predict the properties for the downstream tasks, both BERT and
RoBERTa models were used in the finetuning stage. Experiments show that
GPT-MolBERTa performs well on various molecule property benchmarks, and
approaching state of the art performance in regression tasks. Additionally,
further analysis of the attention mechanisms show that GPT-MolBERTa is able to
pick up important information from the input textual data, displaying the
interpretability of the model.

利用自然语言处理模型 GPT-MolBERTa，通过分析分子的详细文本描述来预测其性质，并展示模型的可解释性。