  The AIDS epidemic has killed 40 million people and caused serious global
problems. The identification of new HIV-inhibiting molecules is of great
importance for combating the AIDS epidemic. Here, the Classifier Guidance
Diffusion model and ligand-based virtual screening strategy are combined to
discover potential HIV-inhibiting molecules for the first time. We call it
Diff4VS. An extra classifier is trained using the HIV molecule dataset, and the
gradient of the classifier is used to guide the Diffusion to generate
HIV-inhibiting molecules. Experiments show that Diff4VS can generate more
candidate HIV-inhibiting molecules than other methods. Inspired by ligand-based
virtual screening, a new metric DrugIndex is proposed. The DrugIndex is the
ratio of the proportion of candidate drug molecules in the generated molecule
to the proportion of candidate drug molecules in the training set. DrugIndex
provides a new evaluation method for evolving molecular generative models from
a pharmaceutical perspective. Besides, we report a new phenomenon observed when
using molecule generation models for virtual screening. Compared to real
molecules, the generated molecules have a lower proportion that is highly
similar to known drug molecules. We call it Degradation in molecule generation.
Based on the data analysis, the Degradation may result from the difficulty of
generating molecules with a specific structure in the generative model. Our
research contributes to the application of generative models in drug design
from method, metric, and phenomenon analysis.
