Publication ● 31 Aug 2020 ● ACS J. Chem. Inf. Model., 2020Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening In this work, we investigate the feasibility of learning a deep neural network to predict the docking output directly from a two-dimensional compound structure.
Publication ● 27 Jun 2020 ● ICML (workshop track) 2020Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits We propose Molecule Edit Graph Attention Network (MEGAN), a template-free neural model that encodes reaction as a sequence of graph edit.
Publication ● 20 Jun 2020 ● arXivWe Should at Least Be Able to Design Molecules That Dock Well We propose a benchmark based on docking, a popular computational method for assessing molecule binding to a protein.
Publication ● 19 Feb 2020 ● NeurIPS (workshop track) 2019Molecule Attention Transformer We propose Molecule Attention Transformer (MAT). Our key innovation is to augment the attention mechanism in Transformer using inter-atomic distances and the molecular graph structure.
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