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

Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu

Research output: Contribution to journalConference articlepeer-review

Abstract

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. The code and checkpoints are available at https://github.com/deqiankong/SGDS.

Original languageEnglish
Pages (from-to)1109-1120
Number of pages12
JournalProceedings of Machine Learning Research
Volume216
StatePublished - 2023
Event39th Conference on Uncertainty in Artificial Intelligence, UAI 2023 - Pittsburgh, United States
Duration: 31 Jul 20234 Aug 2023

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