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Is Experimental Data Quality the Limiting Factor in Predicting the Aqueous Solubility of Druglike Molecules?

https://doi.org/10.1021/mp500103r
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What this work says

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

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Structural and Thermodynamic Explanations of Ostwald's Rule
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Accurate calculations of the hydration free energies of druglike molecules using the reference interaction site model
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Drugs: synonyms and properties

What points at this work

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

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Improved estimation of intrinsic solubility of drug-like molecules through multi-task graph transformer
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Predicting amorphous drug solubility using molecular descriptors: an in-silico approach
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Simulation Models for Prediction of Bioavailability of Medicinal Drugs—the Interface Between Experiment and Computation
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Challenges in the use of atomistic simulations to predict solubilities of drug-like molecules
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Challenges in the use of atomistic simulations to predict solubilities of drug-like molecules
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3. In Silico methods to predict solubility
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Review of the Cosolvency Models for Predicting Drug Solubility in Solvent Mixtures: An Update
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Machine Learning for Solubility Prediction
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Aqueous Drug Solubility: What Do We Measure, Calculate and QSPR Predict?
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Designing solvent systems in chemical processes using self-evolving solubility databases and graph neural networks
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Designing solvent systems in chemical processes using self-evolving solubility databases and graph neural networks
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Building Attention and Edge Convolution Neural Networks for Bioactivity and Physical-Chemical Property Prediction
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Improved Prediction of Aqueous Solubility of Novel Compounds by Going Deeper With Deep Learning
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High-Throughput Virtual Screening of Quinones for Aqueous Redox Flow Batteries: Status and Perspectives
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Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks
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Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules
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Intrinsic Aqueous Solubility: Mechanistically Transparent Data-Driven Modeling of Drug Substances
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Creation and interpretation of machine learning models for aqueous solubility prediction
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The Current Limits in Virtual Screening and Property Prediction
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Mechanistically transparent models for predicting aqueous solu¬bility of rigid, slightly flexible, and very flexible drugs (MW<2000) Accuracy near that of random forest regression Alex Avdeef
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Perspectives in solubility measurement and interpretation
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Harmonizing solubility measurement to lower inter-laboratory variance – progress of consortium of biopharmaceutical tools (CoBiTo) in Japan
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Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database
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Can small drugs predict the intrinsic aqueous solubility of ‘beyond Rule of 5’ big drugs?
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Three machine learning models for the 2019 Solubility Challenge
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ADME Prediction with KNIME: In silico aqueous solubility models based on supervised recursive machine learning approaches
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ADME prediction with KNIME: A retrospective contribution to the second “Solubility Challenge”
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