Work

Translation between Molecules and Natural Language

Year: 2022

Type: conference-paper

Abstract: We present MolT5 -a self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings.MolT5 allows for new, useful, and challenging analogs of traditional vision-language tasks, such as molecule captioning and text-based de novo molecule generation (altogether: translation between molecules and language), which we explore for the first time.Since MolT5 pretrains models on single-modal data, it helps overcome the chemistry domain shortcoming of data scarcity.Furthermore, we consider several metrics, including a new cross-modal embedding-based metric, to evaluate the tasks of molecule captioning and text-based molecule generation.Our results show that MolT5-based models are able to generate outputs, both molecules and captions, which in many cases are high quality 1 .

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/2022.emnlp-main.26

Authors: Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, Heng Ji

Institutions: University of Illinois Urbana-Champaign, Genentech, New York University

Language: English

Topic: Multimodal Machine Learning Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Awards: Molecule Maker Lab Institute (MMLI): An AI Institute for Molecular Discovery, Synthetic Strategy, and Manufacturing, III: Medium: Collaborative Research: StructNet: Constructing and Mining Structure-Rich Information Networks for Scientific Research