Percepta Predictors

A Collection of Expert Modules Built for Comprehensive Evaluation of Structure-Property Relationships

Get an in-depth understanding of structure-property relationships with ACD/Labs' fully powered predictive models for physiochemical and ADME properties, and toxicity endpoints.

Available Modules



PhysChem

• Absolv

  Absolv Module

  The Absolv prediction module calculates Abraham solvation parameters and is the result of collaboration between ACD/Labs and Prof. M. H. Abraham—the original author of this six parameter system.

  • A—Hydrogen bonding acidity
  • B and B0—Hydrogen bonding basicity parameters
  • S—Polarity/polarizability
  • L—Partitioning coefficient between gas phase and hexadecane
  • V—McGowan volume
  • E—Excessive molar refraction

  Read complete information about ACD/Absolv

• Aqueous Solubility^*†

  Aqueous (Water) Solubility Module

  • Calculates pH dependent aqueous solubility, intrinsic solubility, and solubility of the chemical dissolved in pure (unbuffered) water at 25°C and zero ionic strength; along with the equilibrium pH of the solution
  • The model is trainable with experimental values to improve predictions for proprietary chemical space
• Boiling Point

  Boiling Point/Vapor Pressure Module

  • Estimates the boiling point of organic compounds at specified pressure
  • Predicts the vapor pressure of organic compounds as a function of temperature
• LogD^*†

  LogD Module

  The distribution coefficient, D, is a pH dependant measure of the propensity of a molecule to differentially dissolve in two immiscible phases, taking into account all ionized and unionized forms (microspecies). It serves as a quantitative descriptor of lipophilicity. The ACD/LogD prediction module provides the following possibilities:

  • Estimate the value of the octanol-water distribution constant, as the logarithmic ratio (logD), from structure at any pH (0–14)
  • Adapt the model to proprietary chemical spaces by training the underlying logP or pK_a prediction algorithms

  Read complete information about ACD/LogD

• LogP^*†

  LogP Module

  The partition constant, P, is a measure of the propensity of a neutral molecule to differentially dissolve in two immiscible phases, and serves as a quantitative descriptor of lipophilicity. The logP prediction model provides an estimate of the value of the octanol-water partitioning coefficient (also referred to as KOW) as the logarithmic ratio (logP), from structure.

  LogP predictions are exploited as input variable in many of our PhysChem and ADME prediction algorithms including logD, Oral Bioavailability, Blood-Brain Barrier Permeation, Passive Absorption, and several Toxicity models, such as hERG inhibition and Aquatic toxicity.

  Read complete information about ACD/LogP

• pK_a^*†

  pK_a Module

  The acid dissociation constant, K_a, is a measure of the tendency of a molecule or ion to keep a proton (H+) at its ionization center(s). It is related to the ionization ability of a chemical species and is a core property that defines chemical and biological behaviour. The pK_a prediction model from ACD/Labs offers the following functionality:

  • Calculation of accurate acid and base pK_a constants (pK_a = -logK_a) under standard conditions (25°C and zero ionic strength) in aqueous solutions for every ionizable group within organic structures
  • Confidence intervals for all predicted pK_a values, indicating their accuracy

  Read complete information about ACD/pK_a

• Sigma

  Sigma Module

  • A model for calculation of substituent-specific parameters for selected fragments of the molecule:
  • Eectronic constants (Hammett Sigmas)
  • Steric constants (molar volume, molar refractivity)
  • Hydrophobic constant (Hansch Pi)
• Structure Designer

  Structure Designer

  The Structure Designer module is a structure generation and lead optimization tools that combines a structure modification interface with an interactive property prediction toolset so that structures may be optimized for:

  • Physicochemical properties—lipophilicity (logP), ionization (pK_a), number of hydrogen bond donors/acceptors, molecular weight, aqueous solubility)
  • ADME parameters—central nervous system penetration and absorption, plasma protein binding, and P-gp substrate specificity
  • Drug safety-related properties—CYP3A4 and hERG inhibition

  The software automatically generates structural analogs that can be reviewed, analyzed and filtered using a variety of tools in a spreadsheet interface enabling the user to reduce thousands/hundreds of analogs to a manageable number for synthesis. A full set of properties may be calculated to enable thorough analysis of analogs.

  Read more about Structure Designer

• Other PhysChem Descriptors

  Other PhysChem Descriptors

  The models are available for the estimation for the following basic physicochemical properties:

  • Density
  • Freely Rotatable Bonds
  • H-Bond Donors and Acceptors
  • Index of Refraction
  • Molar Refractivity
  • Molar Volume
  • Molecular Weight
  • Parachor
  • Polar Surface Area
  • Polarizability
  • Surface Tension

ADME

• Blood Brain Barrier Permeation

  Blood Brain Barrier Permeation Module

  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:

  • Predictions of:
  • Rate of passive diffusion/permeability (logPS)
  • Extent of BBB permeation (logBB)—steady-state distribution ratio of a compound between brain tissue and plasma
  • Brain/plasma equilibration rate (PS * f_{u, brain})
  • Alerts for compounds likely to undergo transport across the BBB barrier by carrier mediated mechanisms

  Read complete information about ACD/Blood Brain Barrier Permeation

• Cytochrome P450 Inhibitors

  Cytochrome P450 Inhibitors Module

  • Calculates the probability that your compound will be an inhibitor of one of the five major drug metabolizing enzymes—CYP3A4, CYP2D6, CYP2C9, CYP2C19, and CYP1A2—at two different IC₅₀ thresholds
  • IC₅₀ < 50 µM (general inhibition);
  • IC₅₀ < 10 µM (efficient inhibition)
  • The model can be trained with experimental data for new compounds in order to expand its applicability domain
• Cytochrome P450 Substrates

  Cytochrome P450 Substrates Module

  • Calculates the probability that your compound will be a substrate of one of the five major drug metabolizing enzymes—CYP3A4, CYP2D6, CYP2C9, CYP2C19, and CYP1A2
  • The model can be trained with experimental data for new compounds in order to expand its applicability domain
• Distribution^*

  Distribution Module

  • Estimates the strength of drug binding to human plasma proteins as either the overall percentage bound in plasma or as an affinity constant to human serum albumin
  • Both the %PPB and logK_a(HSA) models are trainable with user data
  • Predict their apparent volume of distribution (V_d)
• Maximum Recommended Daily Dose

  Maximum Recommended Daily Dose Module

  • Approximate an estimation of the maximum oral dose of drug that can be used in the clinic
• Oral Bioavailability

  Oral Bioavailability Module

  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.

  • Predict a number of endpoints that affect oral bioavailability:
  • Solubility (dose/solubility ratio)
  • Stability in acidic media
  • Intestinal membrane permeability by passive or active transport (with a summary of transporters where relevant)
  • P-gp efflux
  • First pass metabolism in the liver
  • View up to 5 of the most similar structures from the internal training set, with experimental results and literature references

  Read complete information about ACD/Oral Bioavailability

• Passive Absorption

  Passive Absorption Module

  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:

  • The extent of Human Intestinal Absorption (HIA) in terms of passive transport across the intestine(not affected by any side processes such as limited solubility/dissolution, variable oral dose, chemical stability, active transport, and first pass metabolism in gut or liver), indicating percentage contribution from transcellular and paracellular route.
  • Passive permeability across jejunal epithelium, also indicating absorption rate.
  • Passive permeability across Caco-2 cell monolayers, indicating percentage contribution from transcellular and paracellular route.

  Read complete information about ACD/Passive Absorption

• P-gp Specificity^*†

  P-gp Specificity Module

  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.

  Read complete information about ACD/P-gp Specificity

• PK Explorer

  PK Explorer Module

  • Estimates a number of parameters determining the pharmacokinetic profile of your compounds by using a set of differential equations from a multi-compartment model describing the organism of an average statistical human:
    • C_p(T)
    • T_max and C_p(max)
    • AUC after oral and intravenous administrations
    • Oral Bioavailability
• Regioselectivity of Metabolism

  Regioselectivity of Metabolism Module

  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.

  • Predict metabolic soft spots for metabolism by:
  • Human liver microsomes (HLM)
  • Five major Cytochrome P450 enzymes (CYP3A4, CYP2D6, CYP2C9, CYP2C19, and CYP1A2)
  • Identify metabolic sites on new chemical entities
  • Guide synthesis of compounds with improved metabolic properties
  • Help identify and elucidate likely metabolite structures

  Read complete information about ACD/Regioselectivity of Metabolism

Toxicity

• Acute Toxicity^*†

  Acute Toxicity Module

  • Predicts quantitative LD₅₀ values for two rodent species after different administration routes:
  • LD₅₀ in mice after oral administration
  • LD₅₀ in mice after intravenous administration
  • LD₅₀ in mice after intraperitoneal administration
  • LD₅₀ in mice after subcutaneous administration
  • LD₅₀ in rats after oral administration
  • LD₅₀ in rats after intraperitoneal administration
  • Estimates qualitative OECD Hazard categories
  • The expert system identifies hazardous substructures potentially responsible for the toxic effect
• Aquatic Toxicity^*†

  Aquatic Toxicity Module

  • Predict LC₅₀ values of your compounds for two aquatic organisms—fathead minnows (P. promelas) and water fleas (D. magna)
• Endocrine System Disruption

  Endocrine System Disruption Module

  • Predicts the relative binding affinity of your compounds to the Estrogen Receptor which is associated with the possibility of reproductive toxicity and cancers
• Mutagenicity^*

  Mutagenicity Module

  • Provides predictions for the probability of your producing a positive Ames test outcome
• Health Effects

  Health Effects Module

  • Predicts the probable adverse effects of a compound on particular organs or organ systems based on long term organ specific toxicity studies encompassing various species and routes of administration
  • The following organs and organ systems are considered:
  • Blood
  • Cardiovascular system
  • Gastrointestinal system
  • Kidney
  • Liver
  • Lungs
• hERG Inhibition^*

  hERG Inhibition Module

  • Evaluates your compounds for cardiotoxicity related to drug interactions with the human ether-a-go-go (hERG) channel
• Irritation

  Irritation Module

  • Calculates the potential of a compound to cause moderate or stronger eye and skin irritation as per the Draize test

^* Trainable modules

^†Full prediction modules (all modules offer Spreadsheet, Profiling and Structure Design Workspaces)

Predict properties from simple structure input—name, 2D structure, or SMILES string.

Evaluate predictions in single structure or spreadsheet view—each module offers different prediction-specific tools/information including:

• Colour coded mapping on the structure to highlight atomic/substructure contributions
• Interactivity with structures to assess contributions from different structural elements
• Graphs showing the effect of pH
• Calculation protocols

Spreadsheet view offers the additional capability to view predictions from all licensed modules in one screen, and a number of graphing, sorting, and filtering tools to rank compounds and aid evaluation.

Assess confidence in the predicted result and relevance to your current project using reliability index, probability and/or displayed similar structures from within the training set.

Leverage scientific knowledge by training models with in-house experimental data—better reflect proprietary chemical space and improve prediction accuracy.

Evaluate and investigate predictions from different algorithms—several different models for prediction of logP and pK_a are provided. Choose from ACD/Labs classic, ACD/Labs GALAS, and a consensus model for logP prediction.

One platform for all your prediction results—including custom models and in-house prediction algorithms using our integration plug-in. Connect to an existing web service using an XML protocol, or include models in the form of a DLL.

ACD/Labs Product Suites

Bundles of Percepta prediction modules are available to offer cost savings for multiple modules that support particular workflows, and provide related modules as a package. These include:

• PhysChem Suite—all PhysChem modules
• Percepta Suite—a complete predictor portfolio including all PhysChem, ADME, and Tox modules
• Percepta Impurities Suite—a compilation of prediction modules to enable assessment of genotoxic and carcinogenic risk