# HEDGEHOG: Hierarchical Evaluation of Drug Generators Through Rigorous Filtration

arXiv: https://arxiv.org/abs/2607.13155 · HTML: https://mlsanity.com/paper/2607.13155/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 102 references that are in the map.
- First posted on arXiv 14 Jul 2026.

## Builds on (most cited first, 20 of 20)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Equivariant Diffusion for Molecule Generation in 3D](https://mlsanity.com/paper/2203.17003/) (2022)
- [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://mlsanity.com/paper/1802.04364/) (2018)
- [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)
- [Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development](https://mlsanity.com/paper/2102.09548/) (2021)
- [Structure-based Drug Design with Equivariant Diffusion Models](https://mlsanity.com/paper/2210.13695/) (2022)
- [Hierarchical Generation of Molecular Graphs using Structural Motifs](https://mlsanity.com/paper/2002.03230/) (2020)
- [Fr'echet ChemNet Distance: A metric for generative models for molecules in drug discovery](https://mlsanity.com/paper/1803.09518/) (2018)
- [Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets](https://mlsanity.com/paper/2205.07249/) (2022)
- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)
- [3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction](https://mlsanity.com/paper/2303.03543/) (2023)
- [Exploring Chemical Space with Score-based Out-of-distribution Generation](https://mlsanity.com/paper/2206.07632/) (2022)
- [Learning to Extend Molecular Scaffolds with Structural Motifs](https://mlsanity.com/paper/2103.03864/) (2021)
- [DOCKSTRING: easy molecular docking yields better benchmarks for ligand design](https://mlsanity.com/paper/2110.15486/) (2021)
- [Text-Guided Molecule Generation with Diffusion Language Model](https://mlsanity.com/paper/2402.13040/) (2024)
- [Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design](https://mlsanity.com/paper/2209.12487/) (2022)
- [Multi-domain Distribution Learning for De Novo Drug Design](https://mlsanity.com/paper/2508.17815/) (2025)
- [ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design](https://mlsanity.com/paper/2411.04130/) (2024)
- [Matcha: Multi-Stage Riemannian Flow Matching for Accurate and Physically Valid Molecular Docking](https://mlsanity.com/paper/2510.14586/) (2025)

## Abstract (arXiv)

Generative molecular models can support early drug discovery by proposing new candidate compounds de novo. In practice, useful candidates must balance target-relevant activity, physicochemical properties, and other multiparameter design constraints. However, standard metrics commonly used to evaluate molecular generators only weakly reflect whether the generated compounds are medicinally plausible and suitable for downstream computation. This can produce an incomplete view of model performance and inefficient use of computational resources. We introduce HEDGEHOG, a unified six-stage filtration benchmark that is constructed as a hit identification workflow: (i) preprocessing; (ii) physicochemical descriptor screening; (iii) structural alerts and graph-sanity checks; (iv) synthesis feasibility; (v) docking; and (vi) three-dimensional pose and interaction checks. We evaluated 22 generative models in a KRAS G12D case study, using three runs of 1,000 requested generation attempts per model. The models showed different patterns of attrition, and final survival ranged from 0 to 127 molecules per run. None of the standard metrics showed a significant association with final survival after correction for multiple testing. HEDGEHOG provides a reproducible benchmark for evaluating molecular generative models by measuring molecule survival through chemical filters. The framework identifies stage-wise failure modes across generator classes and provides a practical basis for developing molecular generators better aligned with early drug discovery.