The objective of drug discovery is to identify chemical compounds that
possess specific pharmaceutical properties toward a binding target. Existing
large language models (LLMS) can achieve high token matching scores in terms of
likelihood for molecule generation. However, relying solely on LLM decoding
often results in the generation of molecules that are either invalid due to a
single misused token, or suboptimal due to unbalanced exploration and
exploitation as a consequence of the LLMs prior experience. Here we propose
ERP, Entropy-Reinforced Planning for Transformer Decoding, which employs an
entropy-reinforced planning algorithm to enhance the Transformer decoding
process and strike a balance between exploitation and exploration. ERP aims to
achieve improvements in multiple properties compared to direct sampling from
the Transformer. We evaluated ERP on the SARS-CoV-2 virus (3CLPro) and human
cancer cell target protein (RTCB) benchmarks and demonstrated that, in both
benchmarks, ERP consistently outperforms the current state-of-the-art algorithm
by 1-5 percent, and baselines by 5-10 percent, respectively. Moreover, such
improvement is robust across Transformer models trained with different
objectives. Finally, to further illustrate the capabilities of ERP, we tested
our algorithm on three code generation benchmarks and outperformed the current
state-of-the-art approach as well. Our code is publicly available at:
this https URL

通过使用熵强化规划算法增强 Transformer 解码过程以在开发新药物和生成代码方面取得改进，该方法在多个属性上优于直接从 Transformer 中采样的现有算法。