# ADMET property prediction through combinations of molecular fingerprints

arXiv: https://arxiv.org/abs/2310.00174 · HTML: https://mlsanity.com/paper/2310.00174/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction](https://mlsanity.com/paper/2010.09885/) (2020)
- [ChemBERTa-2: Towards Chemical Foundation Models](https://mlsanity.com/paper/2209.01712/) (2022)
- [Enhancing Model Learning and Interpretation Using Multiple Molecular Graph Representations for Compound Property and Activity Prediction](https://mlsanity.com/paper/2304.06253/) (2023)

## Built on by (most cited first, 11 of 11)

- [On the Scalability of GNNs for Molecular Graphs](https://mlsanity.com/paper/2404.11568/) (2024)
- [Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements](https://mlsanity.com/paper/2606.22731/) (2026)
- [Generalizable, Fast, and Accurate DeepQSPR with fastprop](https://mlsanity.com/paper/2404.02058/) (2024)
- [Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation](https://mlsanity.com/paper/2503.06337/) (2025)
- [Superior Molecular Representations from Intermediate Encoder Layers](https://mlsanity.com/paper/2506.06443/) (2025)
- [Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?](https://mlsanity.com/paper/2506.10912/) (2025)
- [Circuits, Features, and Heuristics in Molecular Transformers](https://mlsanity.com/paper/2512.09757/) (2025)
- [Quantum-Enhanced Multi-Task Learning with Learnable Weighting for Pharmacokinetic and Toxicity Prediction](https://mlsanity.com/paper/2509.04601/) (2025)
- [Can Decision Trees Teach Large Language Models? Distilling Verbalized Knowledge for Molecular Property Prediction](https://mlsanity.com/paper/2603.12344/) (2026)
- [Spectral Analysis of Molecular Features: When Richer Features Do Not Guarantee Better Generalization](https://mlsanity.com/paper/2510.14217/) (2025)
- [WEECFP-SuRGE: A Position-Aware Substructure Encoding Method for Molecular Property Prediction](https://mlsanity.com/paper/2609.04672/) (2026)

## Abstract (arXiv)

While investigating methods to predict small molecule potencies, we found random forests or support vector machines paired with extended-connectivity fingerprints (ECFP) consistently outperformed recently developed methods. A detailed investigation into regression algorithms and molecular fingerprints revealed gradient-boosted decision trees, particularly CatBoost, in conjunction with a combination of ECFP, Avalon, and ErG fingerprints, as well as 200 molecular properties, to be most effective. Incorporating a graph neural network fingerprint further enhanced performance. We successfully validated our model across 22 Therapeutics Data Commons ADMET benchmarks. Our findings underscore the significance of richer molecular representations for accurate property prediction.