Percepta offers calculation of a variety of physicochemical (PhysChem) properties, ADME properties, and toxicity endpoints. See the full list of molecular property calculators, with details, below:
Calculate logD (lipophilicity based on pH for ionizable compounds) from structure.
Molecular property calculators are also available for the following:
The Absolv prediction module calculates Abraham solvation parameters from structure.
The blood-brain barrier (BBB) permeation model in ACD/Labs software provides a comprehensive evaluation of the permeation potential of candidate compounds. While prediction cannot replace experimentation, this module allows compounds to be ranked according to their passive transport across the BBB, based on the following information:
The oral bioavailability model uses a combination of probabilistic and mechanistic modeling techniques to predict oral bioavailability from structure, and relies on a number of other ACD/Labs prediction algorithms and experimental data sets. Results are provided as a quantitative prediction of bioavailability after oral administration (%F) of a dose defined by the user.
Human Intestinal Absorption (HIA) and solubility are two key factors that affect oral bioavailability. The Passive Absorption model predicts the human intestinal permeability of drugs, taking into account trans-cellular and para-cellular routes, and ionization-specific differences in permeation rates. Predictions are based on mechanistic models that use a number of physicochemical parameters, including lipophilicity and ionization, as inputs. The model outputs the following calculated parameters:
P-glycoprotein (P-gp) is a clinically relevant efflux transporter that extrudes compounds from a large variety of cells. Its function has been associated with the drugs' absorption, distribution, excretion, CNS effects, multidrug resistance (MDR). P-gp transports a variety of natural compounds and drugs of different therapeutic areas.
Rapid identification of drug candidates that are P-gp substrates and/or inhibitors is possible using P-gp specificity model. Filtering and exclusion of P-gp substrates/inhibitors from huge 'in-house' libraries of synthesized compounds or virtual libraries is possible, followed by exclusion of such compounds from further development. P-gp specificity model may serve as an initial screen that could replace screening test based on P-gp ATPase activity measurements and partially replace expensive experiments with P-gp expressing cell monolayers and P-gp knock-out animals.
The CYP Regioselectivity model is able to provide valuable insights into a compound's metabolic profile early in the drug discovery process when little or no experimental information is available, and labor intensive investigation of each compound in the screening process is prohibited by the large number of compounds involved.
Resulting from a collaboration with the FDA, this module offers insights into 21 toxic endpoints reflecting various mechanisms of hazardous activity including:
* Available as a discrete bundle
† Trainable (machine learning) modules
‡ Purchased as an individual module
Predict molecular properties from structure, name, or SMILES string.
Easily evaluate results from physicochemical, ADME, and toxicity calculators—each module offers prediction—specific information and tools such as structure highlighting and calculation protocols. Powerful graphing, sorting, and filtering tools further aid evaluation of predicted results.
Use reliability index, probability and/or similar structures from the training set to assess confidence in the predicted result and relevance to your current project.
Apply calculated molecular property data to investigate structural modification/lead optimization to reach the target product profile (absorption, distribution, metabolism, excretion).
Train with experimental data—better reflect proprietary chemical space and improve prediction accuracy using inbuilt machine learning capabilities.
Add custom models and in-house prediction algorithms to core Percepta modules by connecting to an existing web service using an XML protocol, or in the form of a DLL.
ACD/Labs offers a number of deployment options for our physicochemical, ADME, and toxicity predictors:
Desktop/Thick client
Software installations for individual computers with a graphical user interface. Full physicochemical, ADME and toxicity calculator modules are available (with training capabilities) including the PhysChem Profiler bundle.
Batch
Screen tens of thousands of compounds with minimal user intervention—compatible with Microsoft Windows and Linux operating systems (OS). Plug-in to corporate intranets or workflow tools such as Pipeline Pilot.
Percepta Portal/Thin client
Web-based application for prediction of molecular properties (PhysChem, ADME, and toxicity) and data analysis. KNIME integration components available.
Host on your corporate intranet or the cloud. Available for Linux and Windows OS.
This application note discusses the importance of using the machine learning (model training) capabilities of predictive models to improve accuracy.
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Procter & Gamble employs the Percepta platform to replace physicochemical property values derived from physical laboratory experiments, with in silico predictions. Read how Percepta helps chemists and formulators work more efficiently, and assists in decision-making.
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Comparison of experimental and published pKa values for 88 cephalosporin antibiotics with ACD/pKa and other prediction software.
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Percepta Portal is a scalable web application that may be easily integrated into your in-house environment. It offers the power of parallel computing, and effortless maintenance and deployment.
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This guide to informatics and data integrity published by the European Pharmaceutical Review highlights ACD/Labs software solutions including our Spectrus and Percepta platforms, and new solutions Luminata and Katalysts D2D.
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Thousands of scientists are already benefiting from the machine learning capabilities of ACD/Labs' molecular property calculators and predictors.
Use curated experimental data to expand the training database and the applicability domain of the models. Trainable modules enable the relevance of ACD/Labs’ algorithms to be expanded to novel chemical space not represented in any commercially available products.
Model training and application of training data does not require an understanding of programming and is easily accessible and applicable.
Broad and/or relevant chemical space coverage—when working with known chemicals choose predictors with broad chemical space coverage. If you are working with novel compounds they will not be represented in datasets of commercially available calculators. Some models may cover more relevant chemical space, however. Evaluate software with compounds for which you have reliable experimental data.
Trainability—if you are working in a niche chemical space or with patent protected compounds look for prediction algorithms that you can train with reliable experimental data—those with machine learning built in.
Capability to assess prediction reliability and accuracy—no calculator will give perfect results for every compound. Choose software that gives you information about the structures/chemical features on which the result is based, or provides an indication of the accuracy of the result (or calculation error).
The behavior of a molecule in the human body, or any environment, is governed by the molecular properties of that structure. PhysChem and ADME predictors and calculators enable scientists to anticipate how chemical structure affects the performance of a molecule as a drug, pesticide, herbicide, or pollutant etc. Toxicity prediction helps scientists assess potential risk.
In R&D, calculators and predictors of molecular properties help:
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