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June 20, 2026Medicinal Research ReviewsOpen Access

AI‐Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery

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Authors

AGAmit GangwalALAntonio Lavecchia

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Overview

Review highlights AI-driven synthesis innovations in medicinal chemistry, emphasizing sustainability and integration.

Key Points

  • This review aims to explore the role of AI and robotics in enhancing drug discovery processes while focusing on sustainability metrics.
  • Evaluated AI-assisted planning and robotic execution techniques in synthetic chemistry.
  • Assessed recent advancements in large language models, retrosynthesis, and optimization methods.
  • Proposed a hierarchical framework for cognitive planning, physical execution, and translational evaluation.
  • AI-driven methods shortened cycle times and reduced step counts in drug synthesis.
  • Integration of sustainability metrics improved route sustainability.
  • Identified current limitations in dataset quality, reproducibility, and deployment costs.

Cite This Study

Gangwal et al. (2026) studied this question.

synapsesocial.com/papers/6a36306fdb0793dc1a537912https://doi.org/10.1002/med.70074
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