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April 10, 2026Chemical ScienceOpen Access

Synthesizability via reward engineering: expanding generative molecular design into synthetic space

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Authors

DDDominik DeklevaAVAlexey VoronovJJJon Paul Janet

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Overview

Generative molecular ai enhances drug-like molecule synthesis, indicating advancements in medicinal chemistry workflows.

Key Points

  • The research aims to integrate generative molecular AI with reward functions to create novel molecules that are feasible for synthesis.
  • Utilized generative molecular AI techniques to design new molecules.
  • Implemented retrosynthetic reward functions to evaluate synthetic feasibility.
  • Aligned molecular designs with modern medicinal chemistry practices.
  • Produced novel, drug-like molecules optimized for synthesis.
  • Ensured compatibility with contemporary parallel synthesis workflows.

Cite This Study

Dekleva et al. (2026) studied this question.

synapsesocial.com/papers/69d8940c6c1944d70ce050d9https://doi.org/10.1039/d5sc09263a
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