Deep-learning-based inverse design model for intelligent discovery of organic molecules

Kyungdoc Kim,Sungwoo Hwang,Inkoo Kim,Sangmo Kim,Dongseon Lee,Youngmin Nam,Hyo Sug Lee,Sunghan Kim,Jaikwang Shin,Youngchun Kwon,Jiho Yoo,Youn-Suk Choi,Yongsik Jung,Seokho Kang,Won-Joon Son,Jhunmo Son

doi:10.1038/s41524-018-0128-1

Kyungdoc Kim, Sungwoo Hwang + Show 14 more

Open Access

https://doi.org/10.1038/s41524-018-0128-1

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Abstract

The discovery of high-performance functional materials is crucial for overcoming technical issues in modern industries. Extensive efforts have been devoted toward accelerating and facilitating this process, not only experimentally but also from the viewpoint of materials design. Recently, machine learning has attracted considerable attention, as it can provide rational guidelines for efficient material exploration without time-consuming iterations or prior human knowledge. In this regard, here we develop an inverse design model based on a deep encoder-decoder architecture for targeted molecular design. Inspired by neural machine language translation, the deep neural network encoder extracts hidden features between molecular structures and their material properties, while the recurrent neural network decoder reconstructs the extracted features into new molecular structures having the target properties. In material design tasks, the proposed fully data-driven methodology successfully learned design rules from the given databases and generated promising light-absorbing molecules and host materials for a phosphorescent organic light-emitting diode by creating new ligands and combinatorial rules.

Highlights

The discovery of new functional materials has led to major technological advancements, and it remains an important objective to meet the ever-growing demand for various applications, such as semiconductors, displays, and batteries
The stepwise procedure of molecule design, property prediction, chemical synthesis, and experimental evaluation is usually repeated until satisfactory performance is achieved
In this study, extended-connectivity fingerprint (ECFP)[28] was used as the molecular descriptor to express the structural feature of a molecule as an m-dimensional bit vector, which indicates the presence or absence of particular substructures

Summary

Introduction

The discovery of new functional materials has led to major technological advancements, and it remains an important objective to meet the ever-growing demand for various applications, such as semiconductors, displays, and batteries. New approaches were proposed to extract latent knowledge from molecular databases, such as PubChem[12] and ZINC,[13] and generate the target molecules.[14,15,16,17,18,19] they required additional incorporation of chemical knowledge in terms of design rules and predefined molecular fragments in order to construct molecular structures, and the deduced candidates were not free from heuristic instructions.

Results

Conclusion