We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science. As of June 20th 2019, the dataset comprises of 12 quantum mechanical properties of 119,487 organic molecules with up to 14 heavy atoms, sampled from the GDB MedChem database. The Alchemy dataset expands the volume and diversity of existing molecular datasets. Our extensive benchmarks of the state-of-the-art graph neural network models on Alchemy clearly manifest the usefulness of new data in validating and developing machine learning models for chemistry and material science. We further launch a contest to attract attentions from researchers in the related fields. More details can be found on the contest website \footnote{https://alchemy.tencent.com}. At the time of benchamrking experiment, we have generated 119,487 molecules in our Alchemy dataset. More molecular samples are generated since then. Hence, we provide a list of molecules used in the reported benchmarks.
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Insilico Medicine Hong Kong Ltd；Chemistry and Chemical Biology Department, Harvard University（哈佛大学化学与化学生物学系）；Neuromation OU；Hit discovery, Discovery Sciences, Biopharmaceutics R&D, AstraZeneca Gothenburg（阿斯利康哥德堡公司发现科学部药物研发中心）；National Research University Higher School of Economics（俄罗斯国家研究型高等经济大学）；Department of Chemistry and Department of Computer Science, University of Toronto（多伦多大学化学系与计算机科学系）；National Research University Higher School of Economics, St. Petersburg（俄罗斯国家研究型高等经济大学圣彼得堡分校）
Nanyang Technological University（南洋理工大学）；University of Cambridge（剑桥大学）；Loyola Marymount University（洛约拉马利蒙特大学）；Mila（米拉人工智能研究所）；University of Montréal（蒙特利尔大学）；National University of Singapore（新加坡国立大学）
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Department of Computing, Imperial College London（计算系，帝国理工学院伦敦分校）；Department of Chemistry, Imperial College London（化学系，帝国理工学院伦敦分校）；SOLVE Chemistry（SOLVE化学）
Henan Key Laboratory of Quantum Information and Cryptography（河南省量子信息与密码学重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Academy of Quantum Information Sciences（北京量子信息科学研究院）；Tsinghua University（清华大学）
SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology of China（中国科学技术大学）；Hefei University of Technology（合肥工业大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））
Centre for Quantum Technologies, National University of Singapore（新加坡国立大学量子技术中心）；University of Toronto（多伦多大学）；Imperial College London（伦敦帝国理工学院）；Boehringer（勃林格）
URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment
Insilico Medicine AI Limited（Insilico Medicine AI有限公司）；Independent researcher（独立研究者）；Insilico Medicine Canada Inc.（Insilico Medicine加拿大公司）；Insilico Medicine Hong Kong Ltd.（Insilico Medicine香港有限公司）
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Mila - Québec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；University of Chinese Academy of Sciences（中国科学院大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Toronto（多伦多大学）；University of California, Berkeley（加州大学伯克利分校）；Massachusetts Institute of Technology（麻省理工学院）；California Institute of Technology（加州理工学院）；National Research Council Canada（加拿大国家研究委员会）；University of Ottawa（渥太华大学）；HEC Montréal（蒙特利尔高等商学院）
