Molecular design based on generative models, such as variational autoencoders (VAEs), has become increasingly popular in recent years due to its efficiency in 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 our recent paper, entitled “Multi-objective latent space optimization of generative molecular design models”, 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.

The paper has been published in Patterns, a premium open access journal from Cell Press that publishes ground-breaking original research across the full breadth of data science. The paper can be accessed at the following URL:

https://www.cell.com/patterns/fulltext/S2666-3899(24)00184-3