Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction. [PDF]
ACS Omega, 2022 Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications. Despite efforts made over decades, there are still challenges associated with developing a solubility prediction model with satisfactory accuracy for many of these applications.
Panapitiya G +6 more
europepmc +4 more sources
ADME prediction with KNIME: In silico aqueous solubility consensus model based on supervised recursive random forest approaches [PDF]
ADMET and DMPK, 2020 In-silico prediction of aqueous solubility plays an important role during the drug discovery and development processes. For many years, the limited performance of in-silico solubility models has been attributed to the lack of high-quality solubility data
Gabriela Falcón-Cano +2 more
doaj +3 more sources
SolTranNet-A Machine Learning Tool for Fast Aqueous Solubility Prediction. [PDF]
J Chem Inf Model, 2021 While accurate prediction of aqueous solubility remains a challenge in drug discovery, machine learning (ML) approaches have become increasingly popular for this task.
Francoeur PG, Koes DR.
europepmc +2 more sources
Intrinsic Aqueous Solubility: Mechanistically Transparent Data-Driven Modeling of Drug Substances [PDF]
Pharmaceutics, 2022 Intrinsic aqueous solubility is a foundational property for understanding the chemical, technological, pharmaceutical, and environmental behavior of drug substances.
Mare Oja +3 more
doaj +2 more sources
TopP-S: Persistent homology based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility [PDF]
arXiv, 2017 Aqueous solubility and partition coefficient are important physical properties of small molecules. Accurate theoretical prediction of aqueous solubility and partition coefficient plays an important role in drug design and discovery. The prediction accuracy depends crucially on molecular descriptors which are typically derived from theoretical ...
Kedi Wu +3 more
arxiv +3 more sources
Precise heteroatom doping determines aqueous solubility and self-assembly behaviors for polycyclic aromatic skeletons [PDF]
Communications Chemistry, 2022 Enhancing the hydrophilicity of hydrophobic molecular scaffolds allows to increase their aqueous solubility and therefore their usability for a range of applications.
Kang Li +4 more
doaj +2 more sources
Pruned Machine Learning Models to Predict Aqueous Solubility. [PDF]
ACS Omega, 2020 Solubility is a key metric for therapeutic compounds. Conversely, insoluble compounds cloud the accuracy of assays at all stages of chemical biology and drug discovery.
Perryman AL +4 more
europepmc +2 more sources
Improvement in aqueous solubility of achiral symmetric cyclofenil by modification to a chiral asymmetric analog [PDF]
Scientific Reports, 2021 Decreasing the partition coefficient (LogP) by the introduction of a hydrophilic group is the conventional approach for improving the aqueous solubility of drug candidates, but is not always effective. Since melting point is related to aqueous solubility,
Junki Morimoto +6 more
doaj +2 more sources
Improved Prediction of Aqueous Solubility of Novel Compounds by Going Deeper With Deep Learning. [PDF]
Front Oncol, 2020 Aqueous solubility is an important physicochemical property of compounds in anti-cancer drug discovery. Artificial intelligence solubility prediction tools have scored impressive performances by employing regression, machine learning, and deep learning ...
Cui Q +6 more
europepmc +2 more sources
Molecules, 2014 Thymoquinone (TQ) is a potent anticancer phytochemical with confirmed in vitro efficacy. Its clinical use has not yet established, and very few reports have documented its formulation.
Jumah Masoud M. Salmani +3 more
doaj +2 more sources