# Optimizing Molecules using Efficient Queries from Property Evaluations

arXiv: https://arxiv.org/abs/2011.01921 · HTML: https://mlsanity.com/paper/2011.01921/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 30 of 50)

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- [Deep Extrapolation for Attribute-Enhanced Generation](https://mlsanity.com/paper/2107.02968/) (2021)
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- [Importance Weighted Expectation-Maximization for Protein Sequence Design](https://mlsanity.com/paper/2305.00386/) (2023)
- [Holistic Adversarial Robustness of Deep Learning Models](https://mlsanity.com/paper/2202.07201/) (2022)
- [Text-guided multi-property molecular optimization with a diffusion language model](https://mlsanity.com/paper/2410.13597/) (2024)
- [Collaborative Expert LLMs Guided Multi-Objective Molecular Optimization](https://mlsanity.com/paper/2503.03503/) (2025)
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- [Improving Small Molecule Generation using Mutual Information Machine](https://mlsanity.com/paper/2208.09016/) (2022)
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- [Diffusing Gaussian Mixtures for Generating Categorical Data](https://mlsanity.com/paper/2303.04635/) (2023)
- [MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization](https://mlsanity.com/paper/2604.12237/) (2026)
- [Artificial intelligence-driven antimicrobial peptide discovery](https://mlsanity.com/paper/2308.10921/) (2023)
- [Joint Design of Protein Sequence and Structure based on Motifs](https://mlsanity.com/paper/2310.02546/) (2023)
- [Functional Geometry Guided Protein Sequence and Backbone Structure Co-Design](https://mlsanity.com/paper/2310.04343/) (2023)
- [Kernel-Elastic Autoencoder for Molecular Design](https://mlsanity.com/paper/2310.08685/) (2023)
- [Diffusion Models with Double Guidance: Generate with aggregated datasets](https://mlsanity.com/paper/2505.13213/) (2025)
- [GP-MoLFormer-Sim: Test Time Molecular Optimization through Contextual Similarity Guidance](https://mlsanity.com/paper/2506.05628/) (2025)
- [Private Zeroth-Order Optimization with Public Data](https://mlsanity.com/paper/2511.10859/) (2025)
- [An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design](https://mlsanity.com/paper/2110.05478/) (2021)

## Abstract (arXiv)

Machine learning based methods have shown potential for optimizing existing molecules with more desirable properties, a critical step towards accelerating new chemical discovery. Here we propose QMO, a generic query-based molecule optimization framework that exploits latent embeddings from a molecule autoencoder. QMO improves the desired properties of an input molecule based on efficient queries, guided by a set of molecular property predictions and evaluation metrics. We show that QMO outperforms existing methods in the benchmark tasks of optimizing small organic molecules for drug-likeness and solubility under similarity constraints. We also demonstrate significant property improvement using QMO on two new and challenging tasks that are also important in real-world discovery problems: (i) optimizing existing potential SARS-CoV-2 Main Protease inhibitors toward higher binding affinity; and (ii) improving known antimicrobial peptides towards lower toxicity. Results from QMO show high consistency with external validations, suggesting effective means to facilitate material optimization problems with design constraints.