Drug response prediction (DRP) is a crucial phase in drug discovery, and the
most important metric for its evaluation is the IC50 score. DRP results are
heavily dependent on the quality of the generated molecules. Existing molecule
generation methods typically employ classifier-based guidance, enabling
sampling within the IC50 classification range. However, these methods fail to
ensure the sampling space range's effectiveness, generating numerous
ineffective molecules. Through experimental and theoretical study, we
hypothesize that conditional generation based on the target IC50 score can
obtain a more effective sampling space. As a result, we introduce
regressor-free guidance molecule generation to ensure sampling within a more
effective space and support DRP. Regressor-free guidance combines a diffusion
model's score estimation with a regression controller model's gradient based on
number labels. To effectively map regression labels between drugs and cell
lines, we design a common-sense numerical knowledge graph that constrains the
order of text representations. Experimental results on the real-world dataset
for the DRP task demonstrate our method's effectiveness in drug discovery. The
code is available at:this https URL

我们引入了无回归器指导分子生成的方法，以确保在更有效的空间内进行采样并支持药物反应预测。通过实验和理论研究，我们假设基于目标 IC50 得分的条件生成可以获得更有效的采样空间。我们设计了一个常识数值知识图谱来有效地映射药物和细胞系之间的回归标签。实际应用于药物反应预测任务的真实数据集的实验结果证明了我们方法在药物发现方面的有效性。