MolXPT: Wrapping Molecules with Text for Generative Pre-training
This work addresses the challenge of combining scientific text and molecular data for researchers in computational chemistry and drug discovery, representing an incremental advancement in multimodal pre-training.
The authors tackled the problem of integrating molecular and textual data for generative pre-training by proposing MolXPT, a unified language model that wraps molecules with text, resulting in improved molecular property prediction on MoleculeNet, competitive text-molecule translation with fewer parameters, and zero-shot molecular generation.
Generative pre-trained Transformer (GPT) has demonstrates its great success in natural language processing and related techniques have been adapted into molecular modeling. Considering that text is the most important record for scientific discovery, in this paper, we propose MolXPT, a unified language model of text and molecules pre-trained on SMILES (a sequence representation of molecules) wrapped by text. Briefly, we detect the molecule names in each sequence and replace them to the corresponding SMILES. In this way, the SMILES could leverage the information from surrounding text, and vice versa. The above wrapped sequences, text sequences from PubMed and SMILES sequences from PubChem are all fed into a language model for pre-training. Experimental results demonstrate that MolXPT outperforms strong baselines of molecular property prediction on MoleculeNet, performs comparably to the best model in text-molecule translation while using less than half of its parameters, and enables zero-shot molecular generation without finetuning.