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This research aims to enhance drug discovery through an innovative molecular generation framework.

December 22, 2025Open Access

Toward Closed-loop Molecular Discovery via Language Model, Property Alignment and Strategic Search

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Experimental findings demonstrate generative modelling enhances drug discovery efficiency, suggesting promising advancements in molecular design.

This research aims to enhance drug discovery through an innovative molecular generation framework.
Implemented Trio, a closed-loop molecular generation framework integrating language modeling and reinforcement learning.
Utilized Monte Carlo tree search for targeted molecular design.
Evaluated the model's performance in generating viable drug candidates based on binding affinity and pharmacological properties.
Trio improved binding affinity by 7.85%, drug-likeness by 11.10%, and synthetic accessibility by 12.05%.
Achieved a more than fourfold increase in molecular diversity over traditional methods.

Ji et al. (2025) studied this question.

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Experimental findings demonstrate generative modelling enhances drug discovery efficiency, suggesting promising advancements in molecular design.