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Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules

https://doi.org/10.1021/ci4005805
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Global and Local Computational Models for Aqueous Solubility Prediction of Drug-Like Molecules
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Random Forest Models To Predict Aqueous Solubility
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Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules
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First-Principles Calculation of the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules
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Calculation of solvation free energies using a density functional/molecular dynamics coupled potential
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In Silico Prediction of Drug Solubility. 3. Free Energy of Solvation in Pure Amorphous Matter
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Universal Solvation Model Based on Solute Electron Density and on a Continuum Model of the Solvent Defined by the Bulk Dielectric Constant and Atomic Surface Tensions
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Predicting Intrinsic Aqueous Solubility by a Thermodynamic Cycle
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Calculating partition coefficient by atom-additive method
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Random Forests
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Towards a Universal SMILES representation - A standard method to generate canonical SMILES based on the InChI
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CRYSTAL: a computational tool for the ab initio study of the electronic properties of crystals
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Informatics, Machine Learning and Computational Medicinal Chemistry

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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New Insights into Strategies for Solubility Improvement
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Molecular Simulation Methods to Compute Intrinsic Aqueous Solubility of Crystalline Drug‐Like Molecules
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