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Deep Lead Optimization: Leveraging Generative AI for Structural Modification
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DOI：10.1021/jacs.4c11686.png)
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Abstract
En 中文
The integration of deep learning-based molecular generation models into drug discovery has garnered significant attention for its potential to expedite the development process. Central to this is lead optimization, a critical phase where existing molecules are refined into viable drug candidates. As various methods for deep lead optimization continue to emerge, it is essential to classify these approaches more clearly. We categorize lead optimization methods into two main types: goal-directed and structure-directed. Our focus is on structure-directed optimization, which, while highly relevant to practical applications, is less explored compared to goal-directed methods. Through a systematic review of conventional computational approaches, we identify four tasks specific to structure-directed optimization: fragment replacement, linker design, scaffold hopping, and side-chain decoration. We discuss the motivations, training data construction, and current developments for each of these tasks. Additionally, we use classical optimization taxonomy to classify both goal-directed and structure-directed methods, highlighting their challenges and future development prospects. Finally, we propose a reference protocol for experimental chemists to effectively utilize Generative AI (GenAI)-based tools in structural modification tasks, bridging the gap between methodological advancements and practical applications.
Keywords:
FRAGMENT LINKING
CONDITIONAL TRANSFORMER
PHARMACOPHORE SEARCH
MOLECULAR DESIGN
NATURAL-PRODUCTS
CHEMICAL SPACE
DRUG DESIGN
DISCOVERY
ALGORITHM
SCAFFOLDS
Journal
IF:
15.6
Papers:
20.0W
Citations:
60.2W