Multi-Objective Latent Space Optimization of Generative Molecular Design Models

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. In this paper, we propose a multi-objective latent space optimization (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.

23 pages, 9 figures

References in corpus (11)

• Scikit-learn: Machine Learning in Python
• Automatic chemical design using a data-driven continuous representation of molecules
• Optimization of Molecules via Deep Reinforcement Learning
• Conditional molecular design with deep generative models
• GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
• Multi-Objective Molecule Generation using Interpretable Substructures
• Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
• Quantifying the multi-objective cost of uncertainty
• Optimal Experimental Design for Uncertain Systems Based on Coupled Differential Equations
• Accelerating Optimal Experimental Design for Robust Synchronization of Uncertain Kuramoto Oscillator Model Using Machine Learning
• Optimal Decision Making in High-Throughput Virtual Screening Pipelines