Molecule Design by Latent Space Energy-Based Modeling and Gradual
  Distribution Shifting
Deqian Kong Bo Pang Tian Han Ying Nian Wu
Generation of molecules with desired chemical and biological properties such
as high drug-likeness, high binding affinity to target proteins, is critical
for drug discovery. In this paper, we propose a probabilistic generative model
to capture the joint distribution of molecules and their properties. Our model
assumes an energy-based model (EBM) in the latent space. Conditional on the
latent vector, the molecule and its properties are modeled by a molecule
generation model and a property regression model respectively. To search for
molecules with desired properties, we propose a sampling with gradual
distribution shifting (SGDS) algorithm, so that after learning the model
initially on the training data of existing molecules and their properties, the
proposed algorithm gradually shifts the model distribution towards the region
supported by molecules with desired values of properties. Our experiments show
that our method achieves very strong performances on various molecule design
tasks.