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The aim is to explore how AI impacts fragment-based drug design (FBDD) and the associated future challenges.

The expectations of in silico fragment-based drug design and future challenges

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The aim is to explore how AI impacts fragment-based drug design (FBDD) and the associated future challenges.
Review of current AI applications in fragment-based drug design
Analysis of deep learning and generative models
Assessment of virtual screening techniques
Discussion of multi-objective optimisation processes
AI accelerates hit-to-lead processes in drug design
Improved accuracy and drug properties due to AI interventions
Larger fragment libraries boost model training efficiency
AI expands the scope of FBDD to include challenging targets like PROTACs

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AI automates compound design and enhances drug discovery in fragment-based drug design, suggesting significant advancements.

Rupesh V. Chikhale (2026) studied this question.

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