---
license: apache-2.0
tags:
- biology
- chemistry
configs:
  - config_name: bace
    data_files:
      - split: train
        path: bace/train.csv
      - split: test
        path: bace/test.csv
      - split: val
        path: bace/valid.csv
  - config_name: bbbp
    data_files:
      - split: train
        path: bbbp/train.csv
      - split: test
        path: bbbp/test.csv
      - split: val
        path: bbbp/valid.csv
  - config_name: clintox
    data_files:
      - split: train
        path: clintox/train.csv
      - split: test
        path: clintox/test.csv
      - split: val
        path: clintox/valid.csv
  - config_name: esol
    data_files:
      - split: train
        path: esol/train.csv
      - split: test
        path: esol/test.csv
      - split: val
        path: esol/valid.csv
  - config_name: freesolv
    data_files:
      - split: train
        path: freesolv/train.csv
      - split: test
        path: freesolv/test.csv
      - split: val
        path: freesolv/valid.csv
  - config_name: hiv
    data_files:
      - split: train
        path: hiv/train.csv
      - split: test
        path: hiv/test.csv
      - split: val
        path: hiv/valid.csv
  - config_name: lipo
    data_files:
      - split: train
        path: lipo/train.csv
      - split: test
        path: lipo/test.csv
      - split: val
        path: lipo/valid.csv
  - config_name: qm9
    data_files:
      - split: train
        path: qm9/train.csv
      - split: test
        path: qm9/test.csv
      - split: val
        path: qm9/valid.csv
  - config_name: sider
    data_files:
      - split: train
        path: sider/train.csv
      - split: test
        path: sider/test.csv
      - split: val
        path: sider/valid.csv
  - config_name: tox21
    data_files:
      - split: train
        path: tox21/train.csv
      - split: test
        path: tox21/test.csv
      - split: val
        path: tox21/valid.csv
---

# MoleculeNet Benchmark ([website](https://moleculenet.org/))

MoleculeNet is a benchmark specially designed for testing machine learning methods of molecular properties. As we aim to facilitate the development of molecular machine learning method, this work curates a number of dataset collections, creates a suite of software that implements many known featurizations and previously proposed algorithms. All methods and datasets are integrated as parts of the open source DeepChem package(MIT license).

MoleculeNet is built upon multiple public databases. The full collection currently includes over 700,000 compounds tested on a range of different properties. We test the performances of various machine learning models with different featurizations on the datasets(detailed descriptions here), with all results reported in AUC-ROC, AUC-PRC, RMSE and MAE scores.

For users, please cite:
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, Vijay Pande, MoleculeNet: A Benchmark for Molecular Machine Learning, arXiv preprint, arXiv: 1703.00564, 2017.