# We Should at Least Be Able to Design Molecules That Dock Well

arXiv: https://arxiv.org/abs/2006.16955 · HTML: https://mlsanity.com/paper/2006.16955/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2020.

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

- [SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints](https://mlsanity.com/paper/2405.01155/) (2024)
- [Graph Diffusion Policy Optimization](https://mlsanity.com/paper/2402.16302/) (2024)
- [Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks](https://mlsanity.com/paper/2410.22296/) (2024)
- [Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms](https://mlsanity.com/paper/2407.00236/) (2024)
- [What Ails Generative Structure-based Drug Design: Expressivity is Too Little or Too Much?](https://mlsanity.com/paper/2408.06050/) (2024)
- [ReACT-Drug: Reaction-Template Guided Reinforcement Learning for de novo Drug Design](https://mlsanity.com/paper/2512.20958/) (2025)
- [Position: Ideas Should be the Center of Machine Learning Research](https://mlsanity.com/paper/2605.15253/) (2026)
- [Fairness Definitions and Metrics in Deep Reinforcement Learning for Drug Discovery in Healthcare: A Rapid Evidence Review](https://mlsanity.com/paper/2606.02902/) (2026)
- [PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization](https://mlsanity.com/paper/2610.19011/) (2026)

## Abstract (arXiv)

Designing compounds with desired properties is a key element of the drug discovery process. However, measuring progress in the field has been challenging due to the lack of realistic retrospective benchmarks, and the large cost of prospective validation. To close this gap, we propose a benchmark based on docking, a popular computational method for assessing molecule binding to a protein. Concretely, the goal is to generate drug-like molecules that are scored highly by SMINA, a popular docking software. We observe that popular graph-based generative models fail to generate molecules with a high docking score when trained using a realistically sized training set. This suggests a limitation of the current incarnation of models for de novo drug design. Finally, we propose a simplified version of the benchmark based on a simpler scoring function, and show that the tested models are able to partially solve it. We release the benchmark as an easy to use package available at https://github.com/cieplinski-tobiasz/smina-docking-benchmark. We hope that our benchmark will serve as a stepping stone towards the goal of automatically generating promising drug candidates.