# Generalizable, Fast, and Accurate DeepQSPR with fastprop

arXiv: https://arxiv.org/abs/2404.02058 · HTML: https://mlsanity.com/paper/2404.02058/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 2 Apr 2024.

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

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Analyzing Learned Molecular Representations for Property Prediction](https://mlsanity.com/paper/1904.01561/) (2019)
- [A Network Perspective on Stratification of Multi-Label Data](https://mlsanity.com/paper/1704.08756/) (2017)
- [Molecular Property Prediction Based on Graph Structure Learning](https://mlsanity.com/paper/2312.16855/) (2023)
- [ADMET property prediction through combinations of molecular fingerprints](https://mlsanity.com/paper/2310.00174/) (2023)
- [Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry](https://mlsanity.com/paper/2401.03369/) (2024)
- [Molecular Hypergraph Neural Networks](https://mlsanity.com/paper/2312.13136/) (2023)
- [Transformer-CNN: Fast and Reliable tool for QSAR](https://mlsanity.com/paper/1911.06603/) (2019)

## Built on by (most cited first, 4 of 4)

- [Deep Learning Foundation Models from Classical Molecular Descriptors](https://mlsanity.com/paper/2506.15792/) (2025)
- [SC3: The Multi-Solvent Solubility Challenge and Benchmark](https://mlsanity.com/paper/2606.07656/) (2026)
- [CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics Applications](https://mlsanity.com/paper/2608.04942/) (2026)
- [DISSOLVR: An Interpretable and Fast Framework for Aqueous and Organic Solubility Prediction](https://mlsanity.com/paper/2610.02574/) (2026)

## Abstract (arXiv)

Quantitative Structure Property Relationship studies aim to define a mapping between molecular structure and arbitrary quantities of interest. This was historically accomplished via the development of descriptors which requires significant domain expertise and struggles to generalize. Thus the field has morphed into Molecular Property Prediction and been given over to learned representations which are highly generalizable. The paper introduces fastprop, a DeepQSPR framework which uses a cogent set of molecular level descriptors to meet and exceed the performance of learned representations on diverse datasets in dramatically less time. fastprop is freely available on github at github.com/JacksonBurns/fastprop.