What is the Alchemy Dataset?

The Tencent Quantum Lab has recently introduced a new molecular dataset, called Alchemy, to facilitate the development of new machine learning models useful for chemistry and materials science.

The dataset lists 12 quantum mechanical properties of 130,000+ organic molecules comprising up to 12 heavy atoms (C, N, O, S, F and Cl), sampled from the GDBMedChem database. These properties have been calculated using the open-source computational chemistry program Python-based Simulation of Chemistry Framework (PySCF).

The Alchemy dataset expands on the volume and diversity of existing molecular datasets such as QM9.

For more details of Alchemy, please refer to the paper Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models. If you use the dataset in your research, please cite the paper below:

@article{chen2019alchemy,
  title={Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models},
  author={Chen, Guangyong and Chen, Pengfei and Hsieh, Chang-Yu and Lee, Chee-Kong and Liao, Benben and Liao, Renjie and Liu, Weiwen and Qiu, Jiezhong and Sun, Qiming and Tang, Jie and Zemel, Richard and Zhang, Shengyu},
  journal={arXiv preprint arXiv:1906.09427},
  year={2019}
}

Join the Alchemy Contest

Take part and help developing machine learning models to accurately predict organic molecular properties!

In this multi-feature learning contest, you are free to use whatever method you like to predict a set of 12 properties for organic molecules. To help train your model, a training set with the same set of molecular properties is provided below.

The competition will be conducted in two phases.

Phase 1 (Development)

5/22/2019 - 8/7/2019

A period for contest participants to get familiar with the Codalab and develop their models. Participants are asked to predict properties for molecules given in the valid.zip file. Five submissions per day are allowed.

Phase 2 (Evaluation)

8/8/2019 - 10/7/2019

The final stage for the competition. Participants are asked to predict properties for molecules given in the test.zip file. One submission per day is allowed, and twenty submissions in total are allowed in Phase 2.

The contest evaluation is based on the mean absolute error averaged over 12 regression tasks.

Leaderboard   Join     via

Contest Rules

Please refer to Contest Rules for full details. Every contest participant must acknowledge the reading of the contest rules before getting the datasets.

Contest Data

All the molecular properties are retrieved from Tencent Quantum Lab's Alchemy dataset. In this contest, all reported molecular properties are normalized by the substraction of population mean and divided by the standard deviations.

Optional Tools

RDKit

For contest participants without prior experiences in handling molecular data, we strongly recommend you learn to work with RDKit, a cheminformatics software that allows one to easily build molecular graphs based on the SDF files we provide.

RDKit - Getting Started in Python

Tencent Alchemy Tools

If you do not want to dive into RDKit, we also provide a ready-to-use pytorch dataloader which can help you easily deal with those molecules.

You may also find a collection of baselines, including MPNN, from which you can start your journey with Alchemy!

Tencent Alchemy Tools

Submission

answer.csv

The following description applies to both Phase 1 and Phase 2.

Once you have built a model that works to your satisfaction, you should run the model against molecules provided in either the valid.zip or test.zip file, and save the predicted properties in a file named answer.csv according to the format of train.csv file in dev.zip.

In short, answer.csv should store an N by 13 matrix where N is the number of molecules in valid.zip file during Phase 1 or the number of molecules in test.zip during Phase 2. The first column should be the GDB ID followed by 12 columns of molecular properties. The data entries should be sorted in an ascending order of GDB ID.

The answer.csv file should then be zipped and named submission.zip before uploading for evaluation.

Join the Alchemy Contest  

* A Codalab account is required for your submission.

Available on

5/22/2019 - 8/7/2019 Phase 1 (Development),
8/8/2019 - 10/7/2019 Phase 2 (Evaluation)

Have Questions?

For general questions, please ask at the Codalab forum of Alchemy Contest

Problem with datasets and/or dataloaders? Submit an issue on Alchemy on Github

Note. Tencent has the right to adjust the competition rules, prize information, time of competition and other aspects of the contest, relevant requirements and specifications according to the operation situation of the competition, and all other content involved in the contest shall be subject to final confirmation by Tencent.

Alchemy Codalab Leaderboard

Alchemy Codalab Submission Panel

Organizers and Sponsors

Organizer

Professor Shengyu Zhang (Director of Tencent Quantum Lab)

Sponsor

Professor Richard Zemel (Research Director of Vector Institute)

Sponsor

Professor Jie Tang (Tsinghua University, Beijing)