ACD/Percepta Batch Modules—Fast, Accurate PhysChem and ADME-Tox Predictions for Tens of Thousands of Compounds

Utilize for Library Profiling or Deploy Via Intranets Throughout Your Organization

Batch Modules for ACD/Percepta

The batch predictive modules offered on the Percepta platform use the Algorithm Launching Pack (ALP) batch server to provide predictions for tens of thousands of predictions with minimal operator intervention. Batch modules are compatible with Microsoft Windows and Linux operating systems, with no inherent limits to the number of predictions that can be carried out.

To increase the accuracy of predictions for any set of structures, many modules offer the capability to train predictive models with in-house experimental data, extending the model's domain of applicability (see individual desktop product information for trainable modules).

Our customers employ Batch modules to screen compounds; provide property calculations to colleagues in certain departments or organization-wide over corporate internet/portals; plug-in to workflow tools such as Pipeline Pilot; and build more complex prediction models.

Available Modules

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 (Water) Solubility Module

  • Calculates pH dependent aqueous solubility, intrinsic solubility, and solubility of the chemical dissolved in pure (unbuffered) water at 25C 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/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 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 pKa prediction algorithms

Read complete information about ACD/LogD

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

pKa Module

The acid dissociation constant, Ka, 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 pKa prediction model from ACD/Labs offers the following functionality:

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

Read complete information about ACD/pKa

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)

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

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 * fu, brain)
  • Alerts for compounds likely to undergo transport across the BBB barrier by carrier mediated mechanisms

Read complete information about ACD/Blood Brain Barrier

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 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 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 logKa(HSA) models are trainable with user data
  • Predict their apparent volume of distribution (Vd)

Maximum Recommended Daily Dose Module

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

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 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 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.

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:
    • Cp(T)
    • Tmax and Cp(max)
    • AUC after oral and intravenous administrations
    • Oral Bioavailability

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

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 Module

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

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 Module

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

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 Module

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

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)

Compatible File Formats for Import/Export

Module Format
PhysChem Batch Modules *.sdf, *.rdf, *.txt, *.csv
ADME Batch Modules *.sdf, *.rdf, *.txt
Tox Batch Modules *.sdf, *.rdf, *.txt