# Multi-Objective Latent Space Optimization of Generative Molecular Design Models

arXiv: https://arxiv.org/abs/2203.00526 · HTML: https://mlsanity.com/paper/2203.00526/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 57 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://mlsanity.com/paper/1802.04364/) (2018)
- [GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation](https://mlsanity.com/paper/2001.09382/) (2020)
- [Grammar Variational Autoencoder](https://mlsanity.com/paper/1703.01925/) (2017)
- [Molecular De Novo Design through Deep Reinforcement Learning](https://mlsanity.com/paper/1704.07555/) (2017)
- [Optimization of Molecules via Deep Reinforcement Learning](https://mlsanity.com/paper/1810.08678/) (2018)
- [MARS: Markov Molecular Sampling for Multi-objective Drug Discovery](https://mlsanity.com/paper/2103.10432/) (2021)
- [Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining](https://mlsanity.com/paper/2006.09191/) (2020)
- [Improving black-box optimization in VAE latent space using decoder uncertainty](https://mlsanity.com/paper/2107.00096/) (2021)
- [Conditional molecular design with deep generative models](https://mlsanity.com/paper/1805.00108/) (2018)
- [Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference](https://mlsanity.com/paper/2005.07704/) (2020)
- [Improving Molecular Design by Stochastic Iterative Target Augmentation](https://mlsanity.com/paper/2002.04720/) (2020)
- [Quantifying the multi-objective cost of uncertainty](https://mlsanity.com/paper/2010.04653/) (2020)
- [Optimal Experimental Design for Uncertain Systems Based on Coupled Differential Equations](https://mlsanity.com/paper/2007.06117/) (2020)
- [Accelerating Optimal Experimental Design for Robust Synchronization of Uncertain Kuramoto Oscillator Model Using Machine Learning](https://mlsanity.com/paper/2106.00332/) (2021)

## Built on by (most cited first, 13 of 13)

- [Computer-Aided Multi-Objective Optimization in Small Molecule Discovery](https://mlsanity.com/paper/2210.07209/) (2022)
- [Advancing Bayesian Optimization via Learning Correlated Latent Space](https://mlsanity.com/paper/2310.20258/) (2023)
- [ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation](https://mlsanity.com/paper/2309.05853/) (2023)
- [ExLLM: Experience-Enhanced LLM Optimization for Molecular Design and Beyond](https://mlsanity.com/paper/2502.12845/) (2025)
- [Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design](https://mlsanity.com/paper/2405.00202/) (2024)
- [Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders](https://mlsanity.com/paper/2405.20573/) (2024)
- [FragmentGPT: A Unified GPT Model for Fragment Growing, Linking, and Merging in Molecular Design](https://mlsanity.com/paper/2509.11044/) (2025)
- [MEIDNet: Multimodal generative AI framework for inverse materials design](https://mlsanity.com/paper/2601.22009/) (2026)
- [Don't Retrain, Just Reuse: Recovering Dual-Target Molecules from Single-Target Diffusion Models](https://mlsanity.com/paper/2605.25681/) (2026)
- [mRNAutilus: Multi-Objective-Guided Discrete Generation of mRNA with Optimized Therapeutic Properties](https://mlsanity.com/paper/2605.31296/) (2026)
- [Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy](https://mlsanity.com/paper/2411.03460/) (2024)
- [Q-BIOLAT: Binary Latent Protein Fitness Landscapes for QUBO-Based Optimization](https://mlsanity.com/paper/2603.27526/) (2026)
- [Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences](https://mlsanity.com/paper/2605.07724/) (2026)

## Abstract (arXiv)

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.