  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:https://anonymous.4open.science/r/RMCD-DBD1.
