mlsanityData to 7 Oct 2026

Lo-Hi: Practical ML Drug Discovery Benchmark

arXiv 2310.06399 ↗First posted 10 Oct 2023cs.LG cs.AIcited by 10

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Abstract (arXiv)

Finding new drugs is getting harder and harder. One of the hopes of drug discovery is to use machine learning models to predict molecular properties. That is why models for molecular property prediction are being developed and tested on benchmarks such as MoleculeNet. However, existing benchmarks are unrealistic and are too different from applying the models in practice. We have created a new practical Lo-Hi benchmark consisting of two tasks: Lead Optimization (Lo) and Hit Identification (Hi), corresponding to the real drug discovery process. For the Hi task, we designed a novel molecular splitting algorithm that solves the Balanced Vertex Minimum k-Cut problem. We tested state-of-the-art and classic ML models, revealing which works better under practical settings. We analyzed modern benchmarks and showed that they are unrealistic and overoptimistic. Review: https://openreview.net/forum?id=H2Yb28qGLV Lo-Hi benchmark: https://github.com/SteshinSS/lohineurips2023 Lo-Hi splitter library: https://github.com/SteshinSS/lohisplitter

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 23 of its 88 references that are in the map. First posted on arXiv 10 Oct 2023.

Builds on

23 of 88 references.

References · 23

  1. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  2. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 647
  3. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 373
  4. 2020Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)cited by 318
  5. 2018GuacaMol: Benchmarking Models for De Novo Molecular Designcited by 294
  6. 2017Are GANs Created Equal? A Large-Scale Studycited by 267
  7. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 258
  8. 2022Sample Efficiency Matters: A Benchmark for Practical Molecular Optimizationcited by 151
  9. 2022GOOD: A Graph Out-of-Distribution Benchmarkcited by 140
  10. 2017On the State of the Art of Evaluation in Neural Language Modelscited by 103
  11. 2022Exploring Chemical Space with Score-based Out-of-distribution Generationcited by 95
  12. 2019SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discoverycited by 89
11 more
  1. 2022Benchmarking Graphormer on Large-Scale Molecular Modeling Datasetscited by 63
  2. 2022DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotationscited by 54
  3. 2020MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimizationcited by 48
  4. 2021How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Moleculescited by 27
  5. 2023Learning Subpocket Prototypes for Generalizable Structure-based Drug Designcited by 25
  6. 2019Path-Augmented Graph Transformer Networkcited by 22
  7. 2019Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learningcited by 21
  8. 2020Scaffold-constrained molecular generationcited by 18
  9. 2018Edge Attention-based Multi-Relational Graph Convolutional Networkscited by 11
  10. 2023Activity Cliff Prediction: Dataset and Benchmarkcited by 11
  11. 2018Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretationcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 6.

10 papers.

  1. 2026Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvementscited by 6
  2. 2025Synergistic Benefits of Joint Molecule Generation and Property Predictioncited by 5
  3. 2024Benchmarking drug-drug interaction prediction methods: a perspective of distribution changescited by 2
  4. 2024Evaluation Framework for AI-driven Molecular Design of Multi-target Drugs: Brain Diseases as a Case Studycited by 1
  5. 2026MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Moleculescited by 1
  6. 2024Bioptic B1: A Target-Agnostic Potency-Based Small Molecules Search Enginecited by 0
  7. 2026Decomposing the Generalization Gap in PROTAC Activity Prediction: Variance Attribution and the Inter-Laboratory Ceilingcited by 0
  8. 2026Refnd: Preventing Data Leakage in Relational Datasetscited by 0
  9. 2026Rigorous Evaluation of Large Language Models for Malaria Drug Discovery: Trade-offs in Performance, Scale, and Resource Utilitycited by 0
  10. 2026Molecular Property Prediction under Structural Shift with Tabular Foundation Modelscited by 0