# Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design

arXiv: https://arxiv.org/abs/2209.12487 · HTML: https://mlsanity.com/paper/2209.12487/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2022.

## Built on by (most cited first, 17 of 17)

- [Efficient Evolutionary Search Over Chemical Space with Large Language Models](https://mlsanity.com/paper/2406.16976/) (2024)
- [El Agente: An Autonomous Agent for Quantum Chemistry](https://mlsanity.com/paper/2505.02484/) (2025)
- [GP-MoLFormer: A Foundation Model For Molecular Generation](https://mlsanity.com/paper/2405.04912/) (2024)
- [A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences](https://mlsanity.com/paper/2210.10838/) (2022)
- [GraphXForm: Graph transformer for computer-aided molecular design](https://mlsanity.com/paper/2411.01667/) (2024)
- [A Genetic Algorithm for Navigating Synthesizable Molecular Spaces](https://mlsanity.com/paper/2509.20719/) (2025)
- [FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning](https://mlsanity.com/paper/2410.15010/) (2024)
- [Active Learning Enables Generation of Molecules that Advance the Known Pareto Front](https://mlsanity.com/paper/2501.02059/) (2025)
- [FSscore: A Machine Learning-based Synthetic Feasibility Score Leveraging Human Expertise](https://mlsanity.com/paper/2312.12737/) (2023)
- [Quantum Computing-Enhanced Algorithm Unveils Novel Inhibitors for KRAS](https://mlsanity.com/paper/2402.08210/) (2024)
- [Using GNN property predictors as molecule generators](https://mlsanity.com/paper/2406.03278/) (2024)
- [MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules](https://mlsanity.com/paper/2607.00464/) (2026)
- [NovoMolGen: Rethinking Molecular Language Model Pretraining](https://mlsanity.com/paper/2508.13408/) (2025)
- [Inverse Design of Inorganic Compounds with Generative AI](https://mlsanity.com/paper/2604.11827/) (2026)
- [Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark](https://mlsanity.com/paper/2606.30170/) (2026)
- [HEDGEHOG: Hierarchical Evaluation of Drug Generators Through Rigorous Filtration](https://mlsanity.com/paper/2607.13155/) (2026)
- [PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization](https://mlsanity.com/paper/2610.19011/) (2026)

## Abstract (arXiv)

The efficient exploration of chemical space to design molecules with intended properties enables the accelerated discovery of drugs, materials, and catalysts, and is one of the most important outstanding challenges in chemistry. Encouraged by the recent surge in computer power and artificial intelligence development, many algorithms have been developed to tackle this problem. However, despite the emergence of many new approaches in recent years, comparatively little progress has been made in developing realistic benchmarks that reflect the complexity of molecular design for real-world applications. In this work, we develop a set of practical benchmark tasks relying on physical simulation of molecular systems mimicking real-life molecular design problems for materials, drugs, and chemical reactions. Additionally, we demonstrate the utility and ease of use of our new benchmark set by demonstrating how to compare the performance of several well-established families of algorithms. Surprisingly, we find that model performance can strongly depend on the benchmark domain. We believe that our benchmark suite will help move the field towards more realistic molecular design benchmarks, and move the development of inverse molecular design algorithms closer to designing molecules that solve existing problems in both academia and industry alike.