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Early-Stage Discovery in the Era of Hard-To-Drug Targets and Giga-Scale Chemical Spaces

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Author
Dmitri Kireev
Published in
Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special Sessions
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the complexities of early-stage drug discovery, particularly focusing on hard-to-drug targets and the vastness of giga-scale chemical spaces. It highlights the need for a hybrid approach that combines AI and traditional methodologies, exemplified by the FRASE-bot platform. The text discusses the platform's methodology, which includes data mining, conventional docking, machine learning-based triage, and physics-based calculations. It also presents compelling case studies, such as the identification of potential binders for targets related to triple-negative breast cancer, Parkinson's disease, and broad-spectrum antivirals. The chapter concludes with a discussion on the future of AI in drug discovery, emphasizing the importance of physically interpretable, modular frameworks. Readers will gain insights into the current state and future directions of AI-driven drug discovery, the challenges faced, and the potential of hybrid approaches in overcoming these hurdles.
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next chapter Comparative Analysis of Chemical Structure String Representations for Neural Machine Translation
Title
Early-Stage Discovery in the Era of Hard-To-Drug Targets and Giga-Scale Chemical Spaces
Author
Dmitri Kireev
Copyright Year
2026
Book
Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special Sessions

Print ISBN: 978-3-032-04551-5

Electronic ISBN: 978-3-032-04552-2

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-04552-2_1
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