IUPAC-GPT: an IUPAC-based large-scale molecular pre-trained model for property prediction and molecule generation.

Abstract

The international union of pure and applied chemistry (IUPAC) name nomenclature constitutes a universally recognized standard naming system for allocating names to chemical compounds and is a human-friendly, substructure molecular language. Simplified molecular input line entry system (SMILES) string is currently the most popular molecular representation language and is a computer-friendly, atomic-level molecular language. Considering the readability of IUPAC name and the advantages of SMILES string, it becomes significant to investigate the distinctions of these two molecular languages in term of molecular generation and regression/classification tasks. Thus, we have developed a chemical language model named IUPAC-GPT. Besides molecular generation, we have also incorporated the freezing of IUPAC-GPT model parameters and the attachment of trainable lightweight networks for fine-tuning regression/classification tasks. The results indicate that pre-trained IUPAC-GPT can grasp general knowledge that can be effectively transferred to downstream tasks such as molecular generation, binary classification, and property regression prediction. Furthermore, when utilizing the same configuration, IUPAC-GPT exhibited superior performance compared to the smilesGPT model in term of some property prediction tasks. Overall, transformer-like language models pretrained on IUPAC corpora emerge as promising alternatives, offering improved performance in terms of interpretability and semantic abstraction (chemical groups and modifications) when compared to models pretrained on SMILES corpora.