Multi-objective latent space optimization of generative molecular design models

Author:

Abeer, A N M Nafiz

Urban, Nathan M

Weil,Michael

Alexander, Francis J

Yoon, Byung-Jun
Author Address

Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA., Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA., Strategic and Data Science Initiatives, Frederick National Laboratory, Frederick, MD 21702, USA., Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA.,

Abstract:

Molecular design based on generative models, such as variational autoencoders (VAEs), has become increasingly popular in recent years due to its efficiency for exploring high-dimensional molecular space to identify molecules with desired properties. While the efficacy of the initial model strongly depends on the training data, the sampling efficiency of the model for suggesting novel molecules with enhanced properties can be further enhanced via latent space optimization (LSO). In this paper, we propose a multi-objective LSO method that can significantly enhance the performance of generative molecular design (GMD). The proposed method adopts an iterative weighted retraining approach, where the respective weights of the molecules in the training data are determined by their Pareto efficiency. We demonstrate that our multi-objective GMD LSO method can significantly improve the performance of GMD for jointly optimizing multiple molecular properties. © 2024 The Author(s).

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