Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.

Journal: Journal of chemical information and modeling
Published Date: Jul 9, 2025

Abstract

In recent years, generative deep learning has emerged as a transformative approach in drug design, promising to explore the vast chemical space and generate novel molecules with desired biological properties. This perspective examines the challenges and opportunities of applying generative models to drug discovery, focusing on the intricate tasks related to small molecule generation, evaluation, and prioritization. Central to this process is navigating conflicting information from diverse sources─balancing chemical diversity, synthesizability, and bioactivity. We discuss the current state of generative methods, their optimization, and the critical need for robust evaluation protocols. By mapping this evolving landscape, we outline key building blocks, inherent dilemmas, and future directions in the journey to fully harness generative deep learning in the "chemical odyssey" of drug design.

Authors

  • Rıza Özçelik
    Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey.
  • Helena Brinkmann
    Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, the Netherlands. Electronic address: https://twitter.com/hlnbrkmnn.
  • Emanuele Criscuolo
    Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, the Netherlands. Electronic address: https://twitter.com/emanuelecriscu9.
  • Francesca Grisoni
    Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, CH-, 8093, Zurich, Switzerland.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals