# Test-Time Training Scaling Laws for Chemical Exploration in Drug Design

arXiv: https://arxiv.org/abs/2501.19153 · HTML: https://mlsanity.com/paper/2501.19153/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 58 references that are in the map.
- First posted on arXiv 31 Jan 2025.

## Builds on (most cited first, 18 of 18)

- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [Large Language Monkeys: Scaling Inference Compute with Repeated Sampling](https://mlsanity.com/paper/2407.21787/) (2024)
- [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)
- [Fr'echet ChemNet Distance: A metric for generative models for molecules in drug discovery](https://mlsanity.com/paper/1803.09518/) (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)
- [A Review of Cooperative Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1908.03963/) (2019)
- [Effective Diversity in Population Based Reinforcement Learning](https://mlsanity.com/paper/2002.00632/) (2020)
- [How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules](https://mlsanity.com/paper/2112.12542/) (2021)
- [De novo Drug Design using Reinforcement Learning with Multiple GPT Agents](https://mlsanity.com/paper/2401.06155/) (2023)
- [Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning](https://mlsanity.com/paper/2012.11293/) (2020)
- [ACEGEN: Reinforcement learning of generative chemical agents for drug discovery](https://mlsanity.com/paper/2405.04657/) (2024)
- [Re-evaluating sample efficiency in de novo molecule generation](https://mlsanity.com/paper/2212.01385/) (2022)
- [Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES](https://mlsanity.com/paper/2210.12458/) (2022)
- [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)

## Built on by (most cited first, 1 of 1)

- [Epistemic Uncertainty for Test-Time Discovery](https://mlsanity.com/paper/2605.11328/) (2026)

## Abstract (arXiv)

Chemical Language Models (CLMs) leveraging reinforcement learning (RL) have shown promise in de novo molecular design, yet often suffer from mode collapse, limiting their exploration capabilities. Inspired by Test-Time Training (TTT) in large language models, we propose scaling TTT for CLMs to enhance chemical space exploration. We introduce MolExp, a novel benchmark emphasizing the discovery of structurally diverse molecules with similar bioactivity, simulating real-world drug design challenges. Our results demonstrate that scaling TTT by increasing the number of independent RL agents follows a log-linear scaling law, significantly improving exploration efficiency as measured by MolExp. In contrast, increasing TTT training time yields diminishing returns, even with exploration bonuses. We further evaluate cooperative RL strategies to enhance exploration efficiency. These findings provide a scalable framework for generative molecular design, offering insights into optimizing AI-driven drug discovery.