id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-239720-efbfqnem	Axelrod, Simon	GEOM: Energy-annotated molecular conformations for property prediction and molecular generation	2020-06-09		.txt	text/plain	6435	423	55	This dataset will assist benchmarking and transfer learning in two classes of tasks: inferring 3D properties from 2D molecular graphs, and developing generative models to sample 3D conformations. Machine learning outperforms traditional rulebased baselines in many molecule-related tasks, including property prediction and virtual screening [1] [2] [3] , inverse design using generative models [4] [5] [6] [7] [8] [9] [10] [11] , reinforcement learning [12] [13] [14] [15] , differentiable simulators [10, 16, 17] , and synthesis planning and retrosynthesis [18, 19] . Molecular representations in machine learning, and the existing reference datasets, typically use either graphs [28] , or a single point cloud per molecule [29] . Message-passing neural networks use these node and edge features to create a learned fingerprint (representation) for the molecule. A variety of graph convolutional models have been proposed for learning force fields, which map a set of 3D atomic positions of a molecular entity to an energy.	./cache/cord-239720-efbfqnem.txt	./txt/cord-239720-efbfqnem.txt