Machine learning-driven generation and screening of potential ionic liquids for cellulose dissolution.

Journal: Journal of cheminformatics
Published Date: May 21, 2025

Abstract

Cellulose, a highly versatile material, faces challenges in processing due to its limited solubility in common solvents. Ionic liquids have been found to possess high solvating capacities for cellulose. However, the experimental development of ionic liquids with optimal cellulose solubilities remains a time-consuming trial-and-error process. In this work, a virtual molecular library containing billions of potentially de novo ionic liquid candidates has been generated utilizing Monte Carlo tree search and recurrent neural network techniques. The library is subsequently screened through two predictive machine learning models, which have been pre-trained for predicting cellulose solubility and melting point of ionic liquids. The promising candidates were further validated and screened using the Conductor-like Screening Model for Real Solvents (COSMO-RS) model. Our work offers an efficient workflow and virtual molecular library, which should facilitate theoretical and experimental development of novel ionic liquids.

Authors

  • Mengyang Qu
    Faculty of Biological Science and Technology, Institute of Science and Engineering, Kanazawa University, Kakuma-Machi, Kanazawa, 920-1192, Japan.
  • Gyanendra Sharma
    Faculty of Biological Science and Technology, Institute of Science and Engineering, Kanazawa University, Kakuma-Machi, Kanazawa, 920-1192, Japan. sharmag-19@se.kanazawa-u.ac.jp.
  • Naoki Wada
    Department of Renal and Urologic Surgery, Asahikawa Medical University, Asahikawa, Japan. nwada@asahikawa-med.ac.jp.
  • Hisaki Ikebata
    The Graduate University for Advanced Studies (SOKENDAI), Tachikawa, Japan.
  • Shigeyuki Matsunami
    Research Network and Facility Services Division, NIMS, 1-2-1 Sengen, Tsukuba, Ibaraki, 305-0047, Japan.
  • Kenji Takahashi
    Department of Orthopaedics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.

Keywords

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