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High-Throughput, In Silico Prediction of Aqueous Solubility Based on One- and Two-Dimensional Descriptors
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Outgoing claims: the papers this work cites, builds on, or argues with.
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„Grundgerüstwechsel“ (Scaffold-Hopping) durch topologische Pharmakophorsuche: ein Beitrag zum virtuellen Screening
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Kombinatorische Methoden zur Synthese von Alumophosphatmolekularsieben
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Pharmacokinetics and Metabolism in Drug Design
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Artificial neural networks for computer-based molecular design
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Drug-like properties and the causes of poor solubility and poor permeability
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Comparison of Two Methods for Predicting Aqueous Solubility
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Search for Predictive Generic Model of Aqueous Solubility Using Bayesian Neural Nets
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Can We Learn To Distinguish between “Drug-like” and “Nondrug-like” Molecules?
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A Scoring Scheme for Discriminating between Drugs and Nondrugs
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Physicochemical Profiling (Solubility, Permeability and Charge State)
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Machine learning in prediction of intrinsic aqueous solubility of drug‐like compounds: Generalization, complexity, or predictive ability?
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Development and Validation of a Pharmacophore‐Based QSAR Model for the Prediction of CNS Activity
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Applications of machine learning and computational intelligence to drug discovery and development
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In Silico Predictions of Solubility
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Chemical substructures in drug discovery
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Predicting aqueous solubility from structure
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Machine Learning Prediction of Flavonoid Cocrystal Formation Combined with Experimental Validation
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ADME Evaluation in Drug Discovery. 4. Prediction of Aqueous Solubility Based on Atom Contribution Approach
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Generalized Fragment-Substructure Based Property Prediction Method
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Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach
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Prediction of Molecular Solvation Free Energy Based on the Optimization of Atomic Solvation Parameters with Genetic Algorithm
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A Computational Model for the Prediction of Aqueous Solubility That Includes Crystal Packing, Intrinsic Solubility, and Ionization Effects
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Aqueous Solubility Prediction: Do Crystal Lattice Interactions Help?
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Linear and nonlinear functions on modeling of aqueous solubility of organic compounds by two structure representation methods
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In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of compounds from calculated pKa and AlogP98 values
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Data-mining patent literature for novel chemical reagents for use in medicinal chemistry design
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In silicomodelling of ADMET—a minireview of work from 2000 to 2004
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QSPR analysis of the partitioning of vaporous chemicals in a water–gas phase system and the water solubility of liquid and solid chemicals on the basis of fragment and physicochemical similarity and HYBOT descriptors
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Quantitative structure-toxicity relationships (QSTRs): A comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics to Tetrahymena pyriformis
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Machine‐learning predictions of caffeine co‐crystal formation accompanying experimental and molecular validations
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Recent progress in the computational prediction of aqueous solubility and absorption
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Computational models to predict aqueous drug solubility, permeability and intestinal absorption
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Structure – ADME relationship: still a long way to go?
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In silicoprediction of aqueous solubility
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Predicting Aqueous Solubility of Chlorinated Hydrocarbons by the MCI Approach
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