Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Systematic Study of Effects of Structural Modifications on the Aqueous Solubility of Drug-like Molecules.

ABSTRACT: Aqueous solubilities and activities have been measured for 17 members of the quinolinyltriazole series of inhibitors of human macrophage migration inhibitory factor (MIF). Systematic variation of a solvent-exposed substituent provided increases in solubility from 2 ?g/mL for the parent compound 3a up to 867 ?g/mL. The low solubility of 3a results from its near-planar structure and an intermolecular hydrogen bond, as revealed in a small-molecule X-ray structure. Removal of the hydrogen bond yields a 3-fold increase in solubility, but a 7-fold drop in activity. 5b emerges as the most potent MIF inhibitor with a Ki of 14 nM and good solubility, 47 ?g/mL, while 4e has both high potency and solubility.

SUBMITTER: Cisneros JA

PROVIDER: S-EPMC5238483 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Systematic Study of Effects of Structural Modifications on the Aqueous Solubility of Drug-like Molecules.

Cisneros José A JA Robertson Michael J MJ Mercado Brandon Q BQ Jorgensen William L WL

ACS medicinal chemistry letters 20161201 1

Aqueous solubilities and activities have been measured for 17 members of the quinolinyltriazole series of inhibitors of human macrophage migration inhibitory factor (MIF). Systematic variation of a solvent-exposed substituent provided increases in solubility from 2 μg/mL for the parent compound <b>3a</b> up to 867 μg/mL. The low solubility of <b>3a</b> results from its near-planar structure and an intermolecular hydrogen bond, as revealed in a small-molecule X-ray structure. Removal of the hydro ...[more]

PMID: 28105287

Similar Datasets

Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules.

Project description:Shallow machine learning methods have been applied to chemoinformatics problems with some success. As more data becomes available and more complex problems are tackled, deep machine learning methods may also become useful. Here, we present a brief overview of deep learning methods and show in particular how recursive neural network approaches can be applied to the problem of predicting molecular properties. However, molecules are typically described by undirected cyclic graphs, while recursive approaches typically use directed acyclic graphs. Thus, we develop methods to address this discrepancy, essentially by considering an ensemble of recursive neural networks associated with all possible vertex-centered acyclic orientations of the molecular graph. One advantage of this approach is that it relies only minimally on the identification of suitable molecular descriptors because suitable representations are learned automatically from the data. Several variants of this approach are applied to the problem of predicting aqueous solubility and tested on four benchmark data sets. Experimental results show that the performance of the deep learning methods matches or exceeds the performance of other state-of-the-art methods according to several evaluation metrics and expose the fundamental limitations arising from training sets that are too small or too noisy. A Web-based predictor, AquaSol, is available online through the ChemDB portal ( cdb.ics.uci.edu ) together with additional material.

| S-EPMC3739985 | biostudies-literature

Intrinsic Aqueous Solubility: Mechanistically Transparent Data-Driven Modeling of Drug Substances.

Project description:Intrinsic aqueous solubility is a foundational property for understanding the chemical, technological, pharmaceutical, and environmental behavior of drug substances. Despite years of solubility research, molecular structure-based prediction of the intrinsic aqueous solubility of drug substances is still under active investigation. This paper describes the authors' systematic data-driven modelling in which two fit-for-purpose training data sets for intrinsic aqueous solubility were collected and curated, and three quantitative structure-property relationships were derived to make predictions for the most recent solubility challenge. All three models perform well individually, while being mechanistically transparent and easy to understand. Molecular descriptors involved in the models are related to the following key steps in the solubility process: dissociation of the molecule from the crystal, formation of a cavity in the solvent, and insertion of the molecule into the solvent. A consensus modeling approach with these models remarkably improved prediction capability and reduced the number of strong outliers by more than two times. The performance and outliers of the second solubility challenge predictions were analyzed retrospectively. All developed models have been published in the QsarDB.org repository according to FAIR principles and can be used without restrictions for exploring, downloading, and making predictions.

| S-EPMC9611068 | biostudies-literature

Uniting cheminformatics and chemical theory to predict the intrinsic aqueous solubility of crystalline druglike molecules.

Project description:We present four models of solution free-energy prediction for druglike molecules utilizing cheminformatics descriptors and theoretically calculated thermodynamic values. We make predictions of solution free energy using physics-based theory alone and using machine learning/quantitative structure-property relationship (QSPR) models. We also develop machine learning models where the theoretical energies and cheminformatics descriptors are used as combined input. These models are used to predict solvation free energy. While direct theoretical calculation does not give accurate results in this approach, machine learning is able to give predictions with a root mean squared error (RMSE) of ~1.1 log S units in a 10-fold cross-validation for our Drug-Like-Solubility-100 (DLS-100) dataset of 100 druglike molecules. We find that a model built using energy terms from our theoretical methodology as descriptors is marginally less predictive than one built on Chemistry Development Kit (CDK) descriptors. Combining both sets of descriptors allows a further but very modest improvement in the predictions. However, in some cases, this is a statistically significant enhancement. These results suggest that there is little complementarity between the chemical information provided by these two sets of descriptors, despite their different sources and methods of calculation. Our machine learning models are also able to predict the well-known Solubility Challenge dataset with an RMSE value of 0.9-1.0 log S units.

| S-EPMC3965570 | biostudies-literature

Using cocrystals to systematically modulate aqueous solubility and melting behavior of an anticancer drug.

Project description:Five cocrystals of an anticancer compound have been assembled using a well-defined hydrogen-bond-based supramolecular approach that produced the necessary structural consistency in the resulting solids. These cocrystals contain aliphatic even-numbered dicarboxylic acids of increasing chain length, and as a result, the physical properties of the cocrystals can be related to the molecular structure of the acid. The melting points of the five cocrystals show an excellent correlation with the melting points of the individual acids, and it has also been shown that aqueous solubility can be increased by a factor of 2.5 relative to that of the individual drug. Consequently, cocrystals can offer a range of solid forms from which can be chosen an active ingredient where a particular physical property can be dialed in, provided that the cocrystals show considerable structural consistency and that systematic changes are made to the participating cocrystallizing agents.

| S-EPMC3718473 | biostudies-literature

Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules.

Project description:Machine learning (ML) approaches are receiving increasing attention from pharmaceutical companies and regulatory agencies, given their ability to mine knowledge from available data. In drug discovery, for example, they are employed in quantitative structure-property relationship (QSPR) models to predict biological properties from the chemical structure of a drug molecule. In this paper, following the Second Solubility Challenge (SC-2), a QSPR model based on artificial neural networks (ANNs) was built to predict the intrinsic solubility (logS0) of the 100-compound low-variance tight set and the 32-compound high-variance loose set provided by SC-2 as test datasets. First, a training dataset of 270 drug-like molecules with logS0 value experimentally determined was gathered from the literature. Then, a standard three-layer feed-forward neural network was defined by using 10 ChemGPS physico-chemical descriptors as input features. The developed ANN showed adequate predictive performances on both of the SC-2 test datasets. Benefits and limitations of ML approaches have been highlighted and discussed, starting from this case-study. The main findings confirmed that ML approaches are an attractive and promising tool to predict logS0; however, many aspects, such as data quality, molecular descriptor computation and selection, and assessment of applicability domain, are crucial but often neglected, and should be carefully considered to improve predictions based on ML.

| S-EPMC8309152 | biostudies-literature

Thermodynamic Correlation between Liquid–Liquid Phase Separation and Crystalline Solubility of Drug-Like Molecules

Project description:The purpose of the present study was to experimentally confirm the thermodynamic correlation between the intrinsic liquid–liquid phase separation (LLPS) concentration (

| S-EPMC9782016 | biostudies-literature

Pruned Machine Learning Models to Predict Aqueous Solubility.

Project description: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. Herein, we disclose naïve Bayesian classifier models to predict aqueous solubility. Publicly accessible aqueous solubility data were used to create two full, or nonpruned, training sets. These two sets were also combined to create a full fused set, and a training set comprised of a literature collation of solubility data was also considered as a reference. We tested different extents of data pruning on the training sets and constructed machine learning models that were evaluated with two independent, external test sets that contained compounds that were different from the training sets. The best pruned and fused model was significantly more accurate, in comparison to either the full model or the full fused model, with the prediction of these external test sets. By carefully removing data from the training set, less information can be used to create more accurate machine learning models for aqueous solubility. This knowledge and the curated training sets should prove useful to future machine learning approaches.

| S-EPMC7364544 | biostudies-literature

Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction

Project description:Determining the aqueous

| S-EPMC9096921 | biostudies-literature

Improvement of Aqueous Solubility of Lapatinib-Derived Analogues: Identification of a Quinolinimine Lead for Human African Trypanosomiasis Drug Development.

Project description:Lapatinib, an approved epidermal growth factor receptor inhibitor, was explored as a starting point for the synthesis of new hits against Trypanosoma brucei, the causative agent of human African trypanosomiasis (HAT). Previous work culminated in 1 (NEU-1953), which was part of a series typically associated with poor aqueous solubility. In this report, we present various medicinal chemistry strategies that were used to increase the aqueous solubility and improve the physicochemical profile without sacrificing antitrypanosomal potency. To rank trypanocidal hits, a new assay (summarized in a cytocidal effective concentration (CEC50)) was established, as part of the lead selection process. Increasing the sp3 carbon content of 1 resulted in 10e (0.19 ?M EC50 against T. brucei and 990 ?M aqueous solubility). Further chemical exploration of 10e yielded 22a, a trypanocidal quinolinimine (EC50: 0.013 ?M; aqueous solubility: 880 ?M; and CEC50: 0.18 ?M). Compound 22a reduced parasitemia 109 fold in trypanosome-infected mice; it is an advanced lead for HAT drug development.

| S-EPMC6556231 | biostudies-literature

Modifications of DCDHF single molecule fluorophores to impart water solubility.

Project description:A series of dicyanomethylenedihydrofuran (DCDHF) fluorophores with different hydrophilic groups were synthesized and their photophysical properties and water solubilities were measured. Significant water solubility was achieved without compromising desirable photophysical properties, permitting applications of these fluorophores in biological systems.

| S-EPMC2744137 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data