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Deep generative models have achieved tremendous success in designing novel drug molecules in recent years.
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
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A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. a qualitative and quantitative characterization of known drug databases
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Fast, accurate, and reliable molecular docking with QuickVina 2
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Why is tanimoto index an appropriate choice for fingerprint-based similarity calculations?
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Group equivariant convolutional networks
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Attention is all you need
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Automatic chemical design using a data-driven continuous representation of molecules
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
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Energy-inspired molecular conformation optimization
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Equivariant graph neural networks for 3d macromolecular structure, 2021
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From target to drug: Generative modeling for the multimodal structure-based ligand design
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Deep learning enables rapid identification of potent ddr1 kinase inhibitors
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Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design
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Se (3)-transformers: 3d roto-translation equivariant attention networks
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Learning from protein structure with geometric vector perceptrons
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Directional message passing for molecular graphs
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Highly accurate protein structure prediction with alphafold
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Structure-based de novo drug design using 3d deep generative models
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Pre-training molecular graph representation with 3d geometry
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A 3d generative model for structure-based drug design
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Structure-based classification predicts drug response in egfr-mutant nsclc
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E (n) equivariant graph neural networks
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
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De novo molecule design through the molecular generative model conditioned by 3d information of protein binding sites
Xu, M., Ran, T., and Chen, H · 2021
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