SELF-BART : A Transformer-based Molecular Representation Model using SELFIES

TL;DR

This paper introduces SELF-BART, a transformer-based model trained on SELFIES for molecular representation and molecule generation, outperforming existing methods in various chemical data tasks.

Contribution

The paper presents a novel encoder-decoder BART-based model trained on SELFIES, enabling improved molecular representation and molecule generation capabilities.

Findings

Outperforms existing baselines in downstream tasks

Effective in molecular data analysis and manipulation

Capable of generating new molecules

Abstract

Large-scale molecular representation methods have revolutionized applications in material science, such as drug discovery, chemical modeling, and material design. With the rise of transformers, models now learn representations directly from molecular structures. In this study, we develop an encoder-decoder model based on BART that is capable of leaning molecular representations and generate new molecules. Trained on SELFIES, a robust molecular string representation, our model outperforms existing baselines in downstream tasks, demonstrating its potential in efficient and effective molecular data analysis and manipulation.