mlsanityData to 14 Oct 2026

ADMET property prediction through combinations of molecular fingerprints

arXiv 2310.00174 ↗First posted 29 Sep 2023q-bio.BM cs.LGcited by 11

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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.

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

5 of 19 references.

References · 5

  1. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  2. 2019Strategies for Pre-training Graph Neural Networkscited by 971
  3. 2020ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Predictioncited by 337
  4. 2022ChemBERTa-2: Towards Chemical Foundation Modelscited by 127
  5. 2023Enhancing Model Learning and Interpretation Using Multiple Molecular Graph Representations for Compound Property and Activity Predictioncited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 3.

11 papers.

  1. 2024On the Scalability of GNNs for Molecular Graphscited by 36
  2. 2026Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvementscited by 6
  3. 2024Generalizable, Fast, and Accurate DeepQSPR with fastpropcited by 4
  4. 2025Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generationcited by 4
  5. 2025Superior Molecular Representations from Intermediate Encoder Layerscited by 4
  6. 2025Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?cited by 2
  7. 2025Circuits, Features, and Heuristics in Molecular Transformerscited by 2
  8. 2025Quantum-Enhanced Multi-Task Learning with Learnable Weighting for Pharmacokinetic and Toxicity Predictioncited by 1
  9. 2026Can Decision Trees Teach Large Language Models? Distilling Verbalized Knowledge for Molecular Property Predictioncited by 1
  10. 2025Spectral Analysis of Molecular Features: When Richer Features Do Not Guarantee Better Generalizationcited by 0
  11. 2026WEECFP-SuRGE: A Position-Aware Substructure Encoding Method for Molecular Property Predictioncited by 0