# Computer-Aided Multi-Objective Optimization in Small Molecule Discovery

arXiv: https://arxiv.org/abs/2210.07209 · HTML: https://mlsanity.com/paper/2210.07209/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 163 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 30 of 33)

- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [MolGAN: An implicit generative model for small molecular graphs](https://mlsanity.com/paper/1805.11973/) (2018)
- [GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders](https://mlsanity.com/paper/1802.03480/) (2018)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation](https://mlsanity.com/paper/1806.02473/) (2018)
- [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)
- [Deep Reinforcement Learning for De-Novo Drug Design](https://mlsanity.com/paper/1711.10907/) (2017)
- [Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization](https://mlsanity.com/paper/2206.12411/) (2022)
- [Multi-Objective De Novo Drug Design with Conditional Graph Generative Model](https://mlsanity.com/paper/1801.07299/) (2018)
- [Learning Multimodal Graph-to-Graph Translation for Molecular Optimization](https://mlsanity.com/paper/1812.01070/) (2018)
- [Less is more: sampling chemical space with active learning](https://mlsanity.com/paper/1801.09319/) (2018)
- [Molecular generative model based on conditional variational autoencoder for de novo molecular design](https://mlsanity.com/paper/1806.05805/) (2018)
- [The Synthesizability of Molecules Proposed by Generative Models](https://mlsanity.com/paper/2002.07007/) (2020)
- [Keeping it Simple: Language Models can learn Complex Molecular Distributions](https://mlsanity.com/paper/2112.03041/) (2021)
- [Optimizing Molecules using Efficient Queries from Property Evaluations](https://mlsanity.com/paper/2011.01921/) (2020)
- [MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization](https://mlsanity.com/paper/2010.02318/) (2020)
- [Conditional molecular design with deep generative models](https://mlsanity.com/paper/1805.00108/) (2018)
- [Retrieval-based Controllable Molecule Generation](https://mlsanity.com/paper/2208.11126/) (2022)
- [Application of generative autoencoder in de novo molecular design](https://mlsanity.com/paper/1711.07839/) (2017)
- [Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning](https://mlsanity.com/paper/2004.14308/) (2020)
- [JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design](https://mlsanity.com/paper/2106.04011/) (2021)
- [MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional Variational Autoencoder](https://mlsanity.com/paper/2202.07476/) (2022)
- [High-Dimensional Materials and Process Optimization using Data-driven Experimental Design with Well-Calibrated Uncertainty Estimates](https://mlsanity.com/paper/1704.07423/) (2017)
- [Multi-Objective Latent Space Optimization of Generative Molecular Design Models](https://mlsanity.com/paper/2203.00526/) (2022)
- [FastFlows: Flow-Based Models for Molecular Graph Generation](https://mlsanity.com/paper/2201.12419/) (2022)
- [New Paradigms for Exploiting Parallel Experiments in Bayesian Optimization](https://mlsanity.com/paper/2210.01071/) (2022)
- [Deep Learning and Knowledge-Based Methods for Computer Aided Molecular Design -- Toward a Unified Approach: State-of-the-Art and Future Directions](https://mlsanity.com/paper/2005.08968/) (2020)
- [Fragment-based Sequential Translation for Molecular Optimization](https://mlsanity.com/paper/2111.01009/) (2021)

## Built on by (most cited first, 30 of 31)

- [Bayesian optimization as a flexible and efficient design framework for sustainable process systems](https://mlsanity.com/paper/2401.16373/) (2024)
- [TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion](https://mlsanity.com/paper/2509.25171/) (2025)
- [Artificial Intelligence for Drug Discovery: Are We There Yet?](https://mlsanity.com/paper/2307.06521/) (2023)
- [Reinforcement Learning for Generative AI: A Survey](https://mlsanity.com/paper/2308.14328/) (2023)
- [Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-directed Molecular Generation](https://mlsanity.com/paper/2403.20109/) (2024)
- [Pareto Optimization to Accelerate Multi-Objective Virtual Screening](https://mlsanity.com/paper/2310.10598/) (2023)
- [Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation](https://mlsanity.com/paper/2411.03641/) (2024)
- [In-Context Multi-Objective Optimization](https://mlsanity.com/paper/2512.11114/) (2025)
- [M^4olGen: Multi-Agent, Multi-Stage Molecular Generation under Precise Multi-Property Constraints](https://mlsanity.com/paper/2601.10131/) (2026)
- [InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization](https://mlsanity.com/paper/2503.01488/) (2025)
- [Diffusion-based Evolutionary Optimization for 3D Multi-Objective Molecular Generation](https://mlsanity.com/paper/2505.11037/) (2025)
- [Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds](https://mlsanity.com/paper/2506.21887/) (2025)
- [Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design](https://mlsanity.com/paper/2512.17659/) (2025)
- [CombiMOTS: Combinatorial Multi-Objective Tree Search for Dual-Target Molecule Generation](https://mlsanity.com/paper/2604.23307/) (2026)
- [Molecule optimization via multi-objective evolutionary in implicit chemical space](https://mlsanity.com/paper/2212.08826/) (2022)
- [Towards Evolutionary-based Automated Machine Learning for Small Molecule Pharmacokinetic Prediction](https://mlsanity.com/paper/2408.00421/) (2024)
- [Bayesian Optimization with Preference Exploration using a Monotonic Neural Network Ensemble](https://mlsanity.com/paper/2501.18792/) (2025)
- [Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction](https://mlsanity.com/paper/2502.16378/) (2025)
- [Molecule Generation for Target Protein Binding with Hierarchical Consistency Diffusion Model](https://mlsanity.com/paper/2503.00975/) (2025)
- [Agents on a Tree: Pathwise Coordination for Multi-Objective Molecular Optimization](https://mlsanity.com/paper/2606.00008/) (2026)
- [Pepti-Agent: An AI Agent for Peptide Design and Optimization](https://mlsanity.com/paper/2606.15422/) (2026)
- [MOT-SR: Multi-Objective Tool-Augmented Scientific Equation Discovery with Large Language Models](https://mlsanity.com/paper/2607.29561/) (2026)
- [Generative Model for Small Molecules with Latent Space RL Fine-Tuning to Protein Targets](https://mlsanity.com/paper/2407.13780/) (2024)
- [TCR-GPT: Integrating Autoregressive Model and Reinforcement Learning for T-Cell Receptor Repertoires Generation](https://mlsanity.com/paper/2408.01156/) (2024)
- [A study of EHVI vs fixed scalarization for molecule design](https://mlsanity.com/paper/2507.13704/) (2025)
- [Multi-Objective Bayesian Optimization via Adaptive varepsilon-Constraints Decomposition](https://mlsanity.com/paper/2604.15959/) (2026)
- [From Dual Tracking to Clipping: Provably Faster Distributionally Robust Multi-Objective Optimization](https://mlsanity.com/paper/2605.05660/) (2026)
- [ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion](https://mlsanity.com/paper/2606.04468/) (2026)
- [Top-k Pareto Bandits: Hypervolume Regret for Multi-Objective Slate Selection](https://mlsanity.com/paper/2607.26273/) (2026)
- [MatCreatioNN: Machine learning-guided computational discovery of photocatalysts for environmental applications](https://mlsanity.com/paper/2607.27295/) (2026)

## Abstract (arXiv)

Molecular discovery is a multi-objective optimization problem that requires identifying a molecule or set of molecules that balance multiple, often competing, properties. Multi-objective molecular design is commonly addressed by combining properties of interest into a single objective function using scalarization, which imposes assumptions about relative importance and uncovers little about the trade-offs between objectives. In contrast to scalarization, Pareto optimization does not require knowledge of relative importance and reveals the trade-offs between objectives. However, it introduces additional considerations in algorithm design. In this review, we describe pool-based and de novo generative approaches to multi-objective molecular discovery with a focus on Pareto optimization algorithms. We show how pool-based molecular discovery is a relatively direct extension of multi-objective Bayesian optimization and how the plethora of different generative models extend from single-objective to multi-objective optimization in similar ways using non-dominated sorting in the reward function (reinforcement learning) or to select molecules for retraining (distribution learning) or propagation (genetic algorithms). Finally, we discuss some remaining challenges and opportunities in the field, emphasizing the opportunity to adopt Bayesian optimization techniques into multi-objective de novo design.