# Diagnosing and fixing common problems in Bayesian optimization for molecule design

arXiv: https://arxiv.org/abs/2406.07709 · HTML: https://mlsanity.com/paper/2406.07709/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation](https://mlsanity.com/paper/2106.04399/) (2021)
- [GFlowNet Foundations](https://mlsanity.com/paper/2111.09266/) (2021)
- [Molecular De Novo Design through Deep Reinforcement Learning](https://mlsanity.com/paper/1704.07555/) (2017)
- [Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization](https://mlsanity.com/paper/2206.12411/) (2022)
- [Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space](https://mlsanity.com/paper/1909.11655/) (2019)
- [Genetic algorithms are strong baselines for molecule generation](https://mlsanity.com/paper/2310.09267/) (2023)

## Built on by (most cited first, 9 of 9)

- [Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs](https://mlsanity.com/paper/2509.17998/) (2025)
- [Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces](https://mlsanity.com/paper/2507.03910/) (2025)
- [Small Molecule Optimization with Large Language Models](https://mlsanity.com/paper/2407.18897/) (2024)
- [A Genetic Algorithm for Navigating Synthesizable Molecular Spaces](https://mlsanity.com/paper/2509.20719/) (2025)
- [Sample Efficient Generative Optimization for Molecular Design](https://mlsanity.com/paper/2607.12488/) (2026)
- [Why risk matters for protein binder design](https://mlsanity.com/paper/2504.00146/) (2025)
- [A study of EHVI vs fixed scalarization for molecule design](https://mlsanity.com/paper/2507.13704/) (2025)
- [MolLIBRA: Genetic Molecular Optimization with Multi-Fingerprint Surrogates and Text-Molecule Aligned Critic](https://mlsanity.com/paper/2602.07002/) (2026)
- [Scientific Discovery under Validation Congestion via Multi-Fidelity Pairwise Rankings](https://mlsanity.com/paper/2610.01827/) (2026)

## Abstract (arXiv)

Bayesian optimization (BO) is a principled approach to molecular design tasks. In this paper we explain three pitfalls of BO which can cause poor empirical performance: an incorrect prior width, over-smoothing, and inadequate acquisition function maximization. We show that with these issues addressed, even a basic BO setup is able to achieve the highest overall performance on the PMO benchmark for molecule design (Gao et al 2022). These results suggest that BO may benefit from more attention in the machine learning for molecules community.