Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel
  Approach to Generating Molecules with Desirable Properties
Siyuan Guo Jihong Guan Shuigeng Zhou
In the past decade, Artificial Intelligence driven drug design and discovery
has been a hot research topic, where an important branch is molecule generation
by generative models, from GAN-based models and VAE-based models to the latest
diffusion-based models. However, most existing models pursue only the basic
properties like validity and uniqueness of the generated molecules, a few go
further to explicitly optimize one single important molecular property (e.g.
QED or PlogP), which makes most generated molecules little usefulness in
practice. In this paper, we present a novel approach to generating molecules
with desirable properties, which expands the diffusion model framework with
multiple innovative designs. The novelty is two-fold. On the one hand,
considering that the structures of molecules are complex and diverse, and
molecular properties are usually determined by some substructures (e.g.
pharmacophores), we propose to perform diffusion on two structural levels:
molecules and molecular fragments respectively, with which a mixed Gaussian
distribution is obtained for the reverse diffusion process. To get desirable
molecular fragments, we develop a novel electronic effect based fragmentation
method. On the other hand, we introduce two ways to explicitly optimize
multiple molecular properties under the diffusion model framework. First, as
potential drug molecules must be chemically valid, we optimize molecular
validity by an energy-guidance function. Second, since potential drug molecules
should be desirable in various properties, we employ a multi-objective
mechanism to optimize multiple molecular properties simultaneously. Extensive
experiments with two benchmark datasets QM9 and ZINC250k show that the
molecules generated by our proposed method have better validity, uniqueness,
novelty, Fr\'echet ChemNet Distance (FCD), QED, and PlogP than those generated
by current SOTA models.