Diff4VS: HIV-inhibiting Molecules Generation with Classifier Guidance Diffusion for Virtual Screening

TL;DR

Diff4VS introduces a novel diffusion-based generative model guided by a classifier to efficiently generate potential HIV-inhibiting molecules, enhancing virtual screening and providing new evaluation metrics and insights into molecule generation phenomena.

Contribution

It combines classifier guidance with diffusion models for drug molecule generation, proposes the DrugIndex metric, and analyzes the degradation phenomenon in molecule generation.

Findings

Diff4VS generates more candidate molecules than existing methods.

The DrugIndex metric offers a new pharmaceutical evaluation perspective.

Generated molecules show less similarity to known drugs, indicating a degradation phenomenon.

Abstract

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.