Generative Adversarial Networks for De Novo Molecular Design

Young Jae Lee, Hyungu Kahng, Seoung Bum Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

In the chemical industry, the generation of novel molecular structures with beneficial pharmacological and physicochemical properties in de novo molecular design is a critical problem. The advent of deep learning and neural generative models has recently enabled significant achievements in constructing molecular design models in de novo design. Consequently, studies on new generative models continue to generate molecules that exhibit more useful chemical properties. In this study, we propose a method for de novo design that utilizes generative adversarial networks based on reinforcement learning for realistic molecule generation. This method learns to reproduce the training data distribution of simplified molecular-input line-entry system strings. The proposed method is demonstrated to effectively generate novel molecular structures from five benchmark results using a real-world public dataset, ChEMBL. The code is available at https://github.com/dudwojae/SMILES-MaskGAN.

Original languageEnglish
Article number2100045
JournalMolecular Informatics
Volume40
Issue number10
DOIs
Publication statusPublished - 2021 Oct

Keywords

  • de novo molecular design
  • deep learning
  • generative adversarial networks
  • reinforcement learning
  • simplified molecular-input line-entry system (SMILES) strings

ASJC Scopus subject areas

  • Structural Biology
  • Molecular Medicine
  • Drug Discovery
  • Computer Science Applications
  • Organic Chemistry

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