# Re-evaluating sample efficiency in de novo molecule generation

arXiv: https://arxiv.org/abs/2212.01385 · HTML: https://mlsanity.com/paper/2212.01385/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 1 Dec 2022.

## Builds on (most cited first, 9 of 9)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [GuacaMol: Benchmarking Models for De Novo Molecular Design](https://mlsanity.com/paper/1811.09621/) (2018)
- [Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models](https://mlsanity.com/paper/1811.12823/) (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)
- [DOCKSTRING: easy molecular docking yields better benchmarks for ligand design](https://mlsanity.com/paper/2110.15486/) (2021)

## Built on by (most cited first, 8 of 8)

- [Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design](https://mlsanity.com/paper/2305.16160/) (2023)
- [Utilizing Reinforcement Learning for de novo Drug Design](https://mlsanity.com/paper/2303.17615/) (2023)
- [GraphXForm: Graph transformer for computer-aided molecular design](https://mlsanity.com/paper/2411.01667/) (2024)
- [Diversity-Aware Reinforcement Learning for de novo Drug Design](https://mlsanity.com/paper/2410.10431/) (2024)
- [REINFORCE-ING Chemical Language Models for Drug Discovery](https://mlsanity.com/paper/2501.15971/) (2025)
- [Test-Time Training Scaling Laws for Chemical Exploration in Drug Design](https://mlsanity.com/paper/2501.19153/) (2025)
- [Diverse Mini-Batch Selection in Reinforcement Learning for Efficient Chemical Exploration in de novo Drug Design](https://mlsanity.com/paper/2506.21158/) (2025)
- [Multi-objective fluorescent molecule design with a data-physics dual-driven generative framework](https://mlsanity.com/paper/2601.13564/) (2026)

## Abstract (arXiv)

De novo molecule generation can suffer from data inefficiency; requiring large amounts of training data or many sampled data points to conduct objective optimization. The latter is a particular disadvantage when combining deep generative models with computationally expensive molecule scoring functions (a.k.a. oracles) commonly used in computer-aided drug design. Recent works have therefore focused on methods to improve sample efficiency in the context of de novo molecule drug design, or to benchmark it. In this work, we discuss and adapt a recent sample efficiency benchmark to better reflect realistic goals also with respect to the quality of chemistry generated, which must always be considered in the context of small-molecule drug design; we then re-evaluate all benchmarked generative models. We find that accounting for molecular weight and LogP with respect to the training data, and the diversity of chemistry proposed, re-orders the ranking of generative models. In addition, we benchmark a recently proposed method to improve sample efficiency (Augmented Hill-Climb) and found it ranked top when considering both the sample efficiency and chemistry of molecules generated. Continual improvements in sample efficiency and chemical desirability enable more routine integration of computationally expensive scoring functions on a more realistic timescale.