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.
@article{arxiv.2310.03030,
title = {GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction},
author = {Suryanarayanan Balaji and Rishikesh Magar and Yayati Jadhav and Amir Barati Farimani},
journal= {arXiv preprint arXiv:2310.03030},
year = {2023}
}
Comments
Paper has 17 pages, 4 figures and 4 tables, along with 71 references