Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting
This addresses the need for efficient molecule design in drug discovery, but it appears incremental as it builds on existing generative modeling approaches.
The paper tackles the problem of generating molecules with desired chemical and biological properties for drug discovery by proposing a probabilistic generative model that captures the joint distribution of molecules and their properties, achieving very strong performances on various tasks.
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.