# Deep Evolutionary Learning for Molecular Design

arXiv: https://arxiv.org/abs/2102.01011 · HTML: https://mlsanity.com/paper/2102.01011/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Learning to Extend Molecular Scaffolds with Structural Motifs](https://mlsanity.com/paper/2103.03864/) (2021)
- [Molecule optimization via multi-objective evolutionary in implicit chemical space](https://mlsanity.com/paper/2212.08826/) (2022)
- [Machine learning for the prediction of safe and biologically active organophosphorus molecules](https://mlsanity.com/paper/2302.10952/) (2023)
- [CHA2: CHemistry Aware Convex Hull Autoencoder Towards Inverse Molecular Design](https://mlsanity.com/paper/2302.11000/) (2023)

## Abstract (arXiv)

In this paper, we propose a deep evolutionary learning (DEL) process that integrates fragment-based deep generative model and multi-objective evolutionary computation for molecular design. Our approach enables (1) evolutionary operations in the latent space of the generative model, rather than the structural space, to generate novel promising molecular structures for the next evolutionary generation, and (2) generative model fine-tuning using newly generated high-quality samples. Thus, DEL implements a data-model co-evolution concept which improves both sample population and generative model learning. Experiments on two public datasets indicate that sample population obtained by DEL exhibits improved property distributions, and dominates samples generated by multi-objective Bayesian optimization algorithms.