Year: 2024
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Priyadarsini, Indra, Takeda, Seiji, Hamada, Lisa, Brazil, Emilio Vital, Soares, Eduardo, Shinohara, Hajime
Institutions: IBM Research - Brazil, IBM Research - Tokyo, IBM (Brazil)
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403578000