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Automatic Perception of Organic Molecules Based on Essential Structural Information
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Correct Bond Order Assignment in a Molecular Framework Using Integer Linear Programming with Application to Molecules Where Only Non-Hydrogen Atom Coordinates Are Available
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On the Perception of Molecules from 3D Atomic Coordinates
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BALI: Automatic Assignment of Bond and Atom Types for Protein Ligands in the Brookhaven Protein Databank
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Automatic Perception of Organic Molecules Based on Essential Structural Information.
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Automatic molecular structure perception for the universal force field
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Molecular Perception for Visualization and Computation: The Proxima Library
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Knodle: A Support Vector Machines-Based Automatic Perception of Organic Molecules from 3D Coordinates
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UNICON: A Powerful and Easy-to-Use Compound Library Converter
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Comparative Assessment of Scoring Functions: The CASF-2016 Update
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Reading PDB: Perception of Molecules from 3D Atomic Coordinates
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Comparative Assessment of Scoring Functions on an Updated Benchmark: 1. Compilation of the Test Set
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Influence of Protonation, Tautomeric, and Stereoisomeric States on Protein−Ligand Docking Results
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Halogen-Ionic Bridges: Do They Exist in the Biomolecular World?
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Geometrical Preferences of the Hydrogen Bonds on Protein−Ligand Binding Interface Derived from Statistical Surveys and Quantum Mechanics Calculations
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Optimizing the atom types of proteins through iterative knowledge-based potentials
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Automated bond order assignment as an optimization problem
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Deducing chemical structure from crystallographically determined atomic coordinates
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Computational Analysis and Molecular Design of Aromatic Stacking System Between Human ARTD2 Catalytic Domain and its Small-Molecule Inhibitors and Peptidic Ligands
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Automated simultaneous assignment of bond orders and formal charges
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A workflow for deriving chemical entities from crystallographic data and its application to the Crystallography Open Database
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Perception of Chemical Bonds via Machine Learning
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Additional file 1 of A workflow for deriving chemical entities from crystallographic data and its application to the Crystallography Open Database
inferred← citeshttps://doi.org/10.6084/m9.figshare.26656357openalex-snapshot
Additional file 2 of A workflow for deriving chemical entities from crystallographic data and its application to the Crystallography Open Database
inferred← citeshttps://doi.org/10.6084/m9.figshare.26656360openalex-snapshot
Additional file 3 of A workflow for deriving chemical entities from crystallographic data and its application to the Crystallography Open Database
inferred← citeshttps://doi.org/10.6084/m9.figshare.26656363openalex-snapshot
Additional file 4 of A workflow for deriving chemical entities from crystallographic data and its application to the Crystallography Open Database
inferred← citeshttps://doi.org/10.6084/m9.figshare.7813130openalex-snapshot
Additional file 1 of Automated simultaneous assignment of bond orders and formal charges
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