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Generation of molecules with desired chemical and biological properties suchas high drug-likeness, high binding affinity to target proteins, is criticalfor drug discovery. In this paper, we propose a probabilistic generative modelto capture the joint distribution of molecules and their properties. Our modelassumes an energy-based model (EBM) in the latent space. Conditional on thelatent vector, the molecule and its properties are modeled by a moleculegeneration model and a property regression model respectively. To search formolecules with desired properties, we propose a sampling with gradualdistribution shifting (SGDS) algorithm, so that after learning the modelinitially on the training data of existing molecules and their properties, theproposed algorithm gradually shifts the model distribution towards the regionsupported by molecules with desired values of properties. Our experiments showthat our method achieves very strong performances on various molecule designtasks.

Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting