ACD/DMSO Solubility Software for the prediction of solubility in DMSO solution. Now Shipping on the New ACD/Percepta Platform
With the release of our version 2012 software, our PhysChem, ADME, and Tox tools have been migrated to the new ACD/Percepta platform. Equivalent functionality to ACD/DMSO Solubility can now be found in ACD/Labs Percepta Predictors.
Existing software users, review our Release Version 2012 Information or contact ACD/Labs to discuss migration plans. This product was retired in June 2012; as per our End-of-Life Policy, technical support will be provided until June 2014.
ACD/DMSO Solubility predicts the probability that an organic compound will be soluble in DMSO solution.
Features: Predict solubility in DMSO from structure at greater or less than 20 mM, with reliability index.
View up to 5 of the most similar structures in the training set.
Batch module facilitates calculation of large compound collections.
Train the model with your own experimental data to extend chemical space coverage (the applicability domain) and improve prediction accuracy for proprietary compounds.
Benefits: Identify the compounds that may cause erratic results in compound screening and biological assays.
Eliminate compounds from your project that will not allow the use of standard testing protocols.
Add your data to the model to easily build a customized model based on your chemical space.
Additional Information: Import/Export.
Import SDfile or SMILES.
Export to SDfile, or copy and paste into Microsoft Excel.
Information about internal databases: The internal training set of ACD/DMSO Solubility contains data from >15,500 compounds.
The software also provides a reference database of >22,200 compounds.
Prediction of a compound's solubility in DMSO (SDMSO)
plays a vital role in sample management and global drug discovery. The distribution
of samples as DMSO solutions is preferable to distribution of solids/powders,
since these require an extra dissolution step when prepared for analytical
assays. DMSO solutions can be easily diluted with water, thus excluding the
need of sample weighing and dissolving before the analysis. Compounds that
are insoluble in DMSO cannot be handled using standard laboratory protocols,
so their development is costly. Furthermore, plating out these compounds
is severely hampered and may even lead to incorrect test results. AB/DMSO
is an ideal tool for calculating SDMSO values in batch mode. The AB/DMSO
predictor uses a data set of >20,000 compounds with SDMSO cut-off at 20 mM. This set represents a very diverse set of drug-like structures, which were taken from the Specs collection of compounds. This particular compound/data set has been obtained upon plating out more than 200,000 different compounds over a period of 10 years. All these compounds were checked for purity, and for the development of this particular algorithm only compounds with the highest (>90%) purity were selected. The data set that was eventually used (which is only a subset of the full data set) was divided into training (15,584) and validation (6,679) sets. Algorithms were developed entirely on the training set and their accuracy was tested on the validation set. AB/DMSO was developed using the Algorithm Builder (AB) development application. The combination of high quality data and the powerful AB toolset (including proprietary statistical methods) led to the development of an accurate and robust predictor. The AB/DMSO algorithm is based on three sub-algorithms. Each sub-algorithm has a unique generality and control system that directs which sub-algorithm should be applied in every single case. This mechanism achieves very good results for compounds that are similar to the training set and avoids steep drops in accuracy for compounds that are less similar to the training set. In order to check generality and applicability of the AB/DMSO algorithm, a calculation of solubility and its reliability was executed on the World Drug Index. Analysis of results showed that the algorithm of highest reliability was valid for ca. 30% of compounds, while low reliability only applied for less than 1% of the structures. This example shows that AB/DMSO can be used with confidence on any data set of drug/lead-like molecules. The Algorithm Builder development platform also presents an "open product" concept, e.g. it is not just a "black box" solution. Databases and project files are available as additional product options providing access to complete information about the AB/DMSO algorithm and the ability to customize and modify it utilizing in-house knowledge. Features Predictions of SDMSO above or below 20 mM. Overall accuracy of SDMSO predictions above 82%.
Estimation of reliability of calculations. Accuracy of SDMSO predictions approaches 95% (if reliability indication is "high"). SD file/ SMILES import. Export to SD file, copy/paste to Excel. Sub-algorithm mechanism responds to the similarity of the given molecule to the training set. "Open product" concept. Screening speed >20,000
compounds per hour. Additional Project Options Full database with experimental
solubility data. Project file, containing all workflow of the AB/DMSO algorithm.
Modify or even rebuild the existing algorithm using Algorithm Builder. Distribute
AB/DMSO algorithm from in-house applications, through the Pharma Algorithms -
Algorithm Launching Pack (ALP). Development partner
