Open Graph Benchmark: Datasets for Machine Learning on Graphs

Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec. arXiv:2005:00687v1
Contributed by Jesper Madsen

A diverse collection of datasets for use in ML applications to graphs has been collected by Hu et al. The Benchmark is intuitively structured and includes evaluation protocols and metrics. Furthermore, the authors have reported the measured performance of a few popular approaches within each application (e.g., ROC-AUC,PRC-AUC, hits, or accuracy). There are several datasets in all three classes of tasks: Node property prediction (ogbn-), link property (ogbl-) prediction, and graph property prediction (ogbg-). 

Of particular interest to those of us who work in biochemistry broadly defined are the SMILES molecular graphs adapted from MoleculeNet [2] such as ogbg-molhiv (HIV) and ogbg-pcba (PubChem Bio Assay); however, also ogbl-ppa (Protein-Protein Association) and ogbn-proteins (Protein-Protein Association) are of interest. Note that MoleculeNet is not included in its entirety - far from it. So, that resource is definitely also interesting to have a close look at if you have not already explored it.

If you are the competitive type, your efforts can be submitted to scoreboards at the hosting website: https://ogb.stanford.edu

[1] Hu, Fey, Zitnik, Dong, Ren, Liu, Catasta, Leskovec. (2020) arXiv:2005:00687v1
[2] MoleculeNet: a benchmark for molecular machine learning. Wu, Ramsundar, Feinberg, Gomes, Geniesse, Pappu, Leswing, Pande. Chem Sci (2018) 9: 513
[3] https://ogb.stanford.edu (2020)

Discovering chemistry with an ab initio nanoreactor.

Lee-Ping Wang, Alexey Titov, Robert McGibbon, Fang Liu, Vijay S. Pande and Todd J. Martínez

Nature Chemistry

6, 1044–1048 (2014)

Contributed by Alán Aspuru-Guzik

When is quantum chemistry going to take the primary discovery role, rather than an explanatory one? This one of the questions asked in the recent Nature Chemistry paper by the groups of Vijay Pande and Todd Martínez at Stanford.

With the advent of fast GPGPU quantum chemistry in codes such as Terachem, it is practical to carry out medium-scale quantum dynamics simulations for a considerable amount of time. In this paper, the authors employ a spherical "piston" to compress and decompress high-temperature reaction mixtures, which in turn result in an accelerated process for triggering chemical reactions. Automated algorithms are employed to recognize the nature of the chemical transformations involved. The authors use this approach to identify novel mechanisms for the Urey-Miller origins of life experiment.

In this modern world of Facebook, Twitter, Youtube, Facebook and blogs, a video is worth more than a thousand words. The video below summarizes the paper very well.

This paper is indeed, one of my top 5 favorite papers in the field of computational chemistry of 2014. 

Todd Martínez explaining the future of the nanoreactor in the QUITEL 2014 conference held in the Galápagos Islands in Ecuador. He suggested, in a joking fashion, that the nanoreactor could eventually evolve a flask with molecules into life ab initio. The virtual end product of the evolution seems to be the contributor of this post.