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Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology.
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Introduction to density functional theory: Calculations by hand on the helium atom
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Forcegen 3d structure and conformer generation: from small lead-like molecules to macrocyclic drugs
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Docking and Virtual Screening in Drug Discovery , pp. 255–266
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Attention is all you need
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Psi4 1.4: Open-source software for high-throughput quantum chemistry
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Large-scale graph representation learning with very deep gnns and self-supervision
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Relational inductive biases, deep learning, and graph networks
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A sobering assessment of small-molecule force field methods for low energy conformer predictions
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
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Highly accurate protein structure prediction with alphafold
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Learning gradient fields for molecular conformation generation
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Scalars are universal: Equivariant machine learning, structured like classical physics
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Auto-encoding molecular conformations
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SE(3) equivariant graph neural networks with complete local frames
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Molecular geometry prediction using a deep generative graph neural network
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