Percepta® Suite
Physicochemical, ADME and, Toxicity Predictions
Accelerate drug discovery and lead optimization with structure-based predictions that provide insights into molecular behavior, pharmacokinetics, and safety.
Trusted By Companies Worldwide
Explore
Property Predictions to Support Drug Design and Discovery
Percepta Suite enables data-driven decision-making across physicochemical, pharmacokinetic, and toxicity domains to accelerate drug discovery. Its collection of modules provides high-quality, structure-based predictive insights for molecular property analysis.
Use Percepta Suite to:
Predict physicochemical, ADME, and toxicity properties from chemical structure.
Evaluate and visualize predicted results using sorting and plotting tools.
Assess the reliability of predicted property values to support confident decision-making.
Train prediction models with proprietary experimental data to improve predictions for novel chemical space.
Predict with Confidence Across Key Property Domains
Explore Structure-Property Relationships
- Predict physicochemical properties from chemical structure to support compound design and optimization.
- Evaluate and interpret structure–property relationships influencing solubility, lipophilicity, ionization, and developability.
- Investigate molecular property profiles to guide lead optimization and compound selection.
Evaluate Pharmacokinetic Behavior
- Predict pharmacokinetic properties from chemical structure to evaluate absorption, distribution, metabolism, and excretion behavior.
- Assess exposure, bioavailability, metabolic stability, and more to support candidate selection and optimization.
- Leverage PK predictions to identify compounds with favorable in vivo performance earlier in discovery.
Assess Toxicity Risks Earlier
- Predict toxicity endpoints and safety liabilities from chemical structure to support early risk assessment.
- Evaluate potential adverse effects to prioritize safer compounds and reduce late-stage attrition.
- Leverage predictive safety data to guide compound selection and optimization decisions.
Predictive Modules in Percepta Suite
PhysChem
Physicochemical properties:
- Aqueous Solubility*
- Boiling Point/Vapor Pressure
- LogD
- LogP*
- pKa
- Sigma
Molecular Descriptors:
- Density
- Freely Rotatable Bonds
- H-Bond Donors and Acceptors
- Index of Refraction
- Molar Refractivity
- Molar Volume
- Molecular Weight
- Parachor
- Polar Surface Area
- Polarizability
- Rule-of-5
- Surface Tension
ADME
- Blood-Brain Barrier Permeability
- Cytochrome P450 Inhibitors*
- Cytochrome P450 Substrates*
- Distribution*
- Maximum Recommended Daily Dose
- Oral Bioavailability
- Passive Absorption
- P-gp Specificity*
- PK Explorer
- Regioselectivity of Metabolism
Toxicity
- Acute Toxicity*
- Aquatic Toxicity*
- Endocrine System Disruption
- Mutagenicity*
- Health Effects
- hERG Inhibition*
- Irritation
*Trainable with your own experimental data
Benefits
Accelerate Decision-Making with Predictive Insight
Fast, Accurate, Reliable Results
Calculate properties for single compounds or large compound libraries using curated experimental data.
Evaluate prediction confidence using a reliability index, similar structures, and literature references.
Convenient Visualization
Visualize substructure and atomic contributions to predicted property values with color mapping (select models).
Quickly identify favorable and unfavorable compounds in libraries using user-defined color coding.
Improve Predictions with Machine Learning
Train prediction models using your own experimental data to extend prediction accuracy to proprietary chemical space and build project-specific training sets for fine-tuned prediction accuracy.
Easy to Use
Draw or import a chemical structure to generate molecular property predictions and train models with your own experimental data through an intuitive interface designed for chemists, not programmers.
Gain Deeper Insights
Identify trends, prioritize compounds, and evaluate complete molecular property profiles using interactive scatter plots, filtering, sorting, and ranking tools to support confident decision-making.
Augment Structured Data for AI/ML
Annotate large structured datasets with physicochemical, ADME and toxicity property predictions to provide additional context before feeding them into machine learning (ML) and artificial intelligence (AI) models.
Insights for Modern Drug Discovery
Percepta Suite FAQs
PhysChem Suite is focused specifically on physicochemical property prediction, including logP, logD, pKa, aqueous solubility, and related molecular descriptors.
Percepta Suite combines PhysChem Suite with ADME and toxicity prediction capabilities in a single platform, bringing together physicochemical, pharmacokinetic, and toxicological property prediction to support broader drug discovery, molecular design, and lead optimization workflows.
Yes, Percepta Suite is the collection of prediction modules available on ACD/Labs Percepta platform, bringing together physicochemical, ADME, and toxicity prediction capabilities into a single integrated solution.
Percepta Suite predicts a broad range of physicochemical properties, ADME properties, and toxicity endpoints from chemical structure. These include properties such as pKa, logP, logD, and aqueous solubility; ADME endpoints such as absorption, distribution, cytochrome P450 activity, and P-glycoprotein specificity; and toxicity endpoints including mutagenicity, hERG inhibition, acute and aquatic toxicity, and adverse effects on organs.
Physicochemical properties influence how a compound behaves in biological systems, including absorption, distribution, membrane permeability, and interactions with biological targets. Because ADME and toxicity endpoints are closely linked to molecular features such as ionization state and lipophilicity, accurate physicochemical data helps improve the reliability of ADME and toxicity predictions and supports better decision-making earlier in drug discovery.
Yes. Select models within Percepta Suite can be trained using proprietary experimental data to improve prediction accuracy and better reflect an organization’s specific chemical space.
Directly trainable endpoints include:
- LogP
- pKa
- Distribution
- P-glycoprotein specificity
- Cytochrome P450 substrate and inhibition models
- Aqueous solubility
- Acute toxicity
- Aquatic toxicity
- Mutagenicity
- hERG inhibition
Training prediction models with reliable in-house experimental data can improve prediction accuracy for proprietary and novel chemical space. Commercial prediction models are typically built using publicly available datasets and may not fully represent the compounds being investigated in a specific research program. Training helps expand the model applicability domain, reduce prediction error for relevant chemistries, and can save time compared to building and validating entirely new models from scratch.
Percepta Suite is available as a desktop (thick client), batch processing for high-throughput workflows on Windows and Linux, and via Percepta Portal (thin client) for browser-based access or a number of APIs for programmatic access to a corporate calculation server (Percepta Kernel).
Yes. Percepta Suite supports integration of third-party and in-house models within computational workflows via a number of programmatic APIs such as RESTful web service, including integration through platforms such as KNIME. This enables predictive molecular property data to be incorporated into existing modeling, automation, and AI/ML pipelines.
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