TY - JOUR
T1 - Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting
AU - Kong, Deqian
AU - Pang, Bo
AU - Han, Tian
AU - Wu, Ying Nian
N1 - Publisher Copyright:
© UAI 2023. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85170037822&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85170037822&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85170037822
VL - 216
SP - 1109
EP - 1120
JO - Proceedings of Machine Learning Research
JF - Proceedings of Machine Learning Research
T2 - 39th Conference on Uncertainty in Artificial Intelligence, UAI 2023
Y2 - 31 July 2023 through 4 August 2023
ER -