<ul><li>A new approach called Entropy-Reinforced Planning (ERP) is proposed for drug discovery using large language models (LLMs).</li><li>LLMs are effective for generating molecules but can result in invalid or suboptimal compounds due to their prior experience.</li><li>ERP enhances the Transformer decoding process by employing an entropy-reinforced planning algorithm, achieving a balance between exploitation and exploration.</li><li>Experimental results show that ERP outperforms the current state-of-the-art algorithm and baselines in multiple benchmarks, including SARS-CoV-2 and human cancer target proteins.</li></ul>

Entropy-Reinforced Planning with Large Language Models for Drug Discovery

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