Solubility and Permeability Prediction Service
CD ComputaBio helps you evaluate aqueous solubility and membrane permeability together to identify absorption bottlenecks, formulation risk, and the analogs most likely to improve oral exposure.
Solubility and Permeability Prediction Services
Each service can be used independently or combined into a staged workflow that moves from broad candidate screening to detailed absorption and formulation analysis.
The workflow begins with the absorption or formulation decision, not with a predefined simulation package.
Solubility Risk Prediction
Rank compounds by aqueous solubility risk, considering intrinsic versus apparent behavior, pH sensitivity, solid-state factors, and formulation constraints.
Permeability Prediction
Estimate passive or Caco-2-oriented permeability with endpoint-specific models, applicability-domain review, and transport-mechanism interpretation.
Solubility–Permeability Balance
Compare analogs across ionization, lipophilicity, polarity, and size to avoid optimizing one property at the expense of the other.
pH-Dependent Behavior Modeling
Analyze how ionization state, pKa, and logD influence solubility and permeability across the physiological pH range.
Analog-Series Design
Use matched molecular pairs and structure–property relationships to guide analog design that improves the overall absorption profile.
Formulation and Assay Prioritization
Determine which experiments—kinetic or thermodynamic solubility, PAMPA, Caco-2, or solid-form analysis—will best resolve the dominant bottleneck.
BCS-Oriented Risk Interpretation
Provide Biopharmaceutics Classification System (BCS) –oriented guidance without claiming formal classification from prediction alone.
Every gain has a counterweight
More polarity may improve dissolution while weakening membrane passage; more lipophilicity can reverse that trade-off. We model these shifts together instead of optimizing isolated endpoints.
The result is a property-balance brief that identifies the limiting step, the safest design margin, and the measurement most likely to resolve uncertainty.
How much aqueous availability remains across the relevant pH range?
Can the neutral fraction cross efficiently without excessive efflux exposure?
Where does ionization change the dominant absorption mechanism?
Which risks belong to molecular design, and which can formulation realistically absorb?
Translate absorption failure into a molecular design question
Compound optimization becomes more efficient when the observed performance loss is connected to a specific solubility, permeability, or formulation mechanism.
Common absorption observations
May indicate poor solubility, inadequate permeability, or a combination of both.
May reflect ionization-related solubility changes or formulation sensitivity.
May arise from poor dissolution, efflux, or metabolic instability.
May involve assay conditions, transporter interplay, or compound-specific artifacts.
May reflect low solubility, high lipophilicity, or solid-form instability.
Possible computational responses
The modeling strategy is selected according to the suspected failure mechanism and the evidence needed to choose a new compound or formulation.
Read absorption behavior through charge state
Each ionization class creates a different balance of dissolution, passive transport, assay sensitivity, and formulation opportunity.
Neutral lane
Benchmark non-ionizable compounds and establish baseline absorption expectations.
Watch: lipophilicity and solid stateAcidic lane
Evaluate pH-dependent solubility and permeability for carboxylic acids, sulfonamides, and phenols.
Watch: intestinal pH transitionBasic lane
Assess ionization behavior, solubility at gastric pH, and permeability in the intestine.
Watch: gastric-to-intestinal shiftZwitterionic lane
Analyze complex charge-state behavior and its impact on absorption and formulation.
Watch: low passive diffusionMulti-pKa lane
Optimize solubility, permeability, and developability within a single chemical series.
Watch: shifting microstatesConnect molecular properties to absorption-level decisions
Solubility and permeability behavior spans several length and time scales. QSPR models can provide rapid estimates, machine learning can capture nonlinear relationships, and pH-dependent analysis can clarify ionization effects.
Higher-resolution approaches can then examine transport mechanisms, formulation interactions, and developability for selected candidates.
Broad AI and QSPR screening can narrow the candidate space before more detailed experimental or simulation work is performed.
Interpretable structure–property relationships for solubility, permeability, and ionization.
Nonlinear models for complex property relationships, with applicability-domain assessment.
pKa, logD, charge-state profiling, and solubility–pH curve generation.
Endpoint-specific permeability predictions with validation and interpretation.
Integration of property predictions with formulation risk and experimental follow-up planning.
Different absorption problems require different compound strategies
These examples illustrate how a project can be structured around a specific experimental bottleneck.
Which compound has the best balance of solubility and permeability?
Compare solubility, permeability, ionization, and lipophilicity to identify the lead with the lowest absorption risk.
Output: prioritized compounds with trade-off maps and experimental follow-up recommendations.How can polarity be increased without losing permeability?
Use matched molecular pairs to identify structural changes that improve solubility while maintaining or enhancing permeability.
Output: design hypotheses with predicted property profiles and risk assessments.Which formulation approach is most likely to succeed?
Evaluate solubility, solid-state risk, and developability to prioritize formulation strategies and experiments.
Output: formulation recommendations and a prioritized assay plan.Is the compound likely to be BCS Class 2 or Class 4?
Provide a BCS-oriented risk interpretation based on predicted solubility and permeability, with clear uncertainty and follow-up advice.
Output: BCS risk assessment and recommended confirmatory experiments.From absorption failure to a compound ready for experimental testing
The workflow is customized around the compound series, available data, and the specific decision the client needs to make.
Define the Absorption Problem
Identify solubility limitation, permeability barrier, pH sensitivity, or formulation risk.
Build the Compound Space
Curate structures, properties, measured data, and experimental context.
Model Critical Properties
Apply QSPR, machine learning, or pH-dependent analysis according to the key uncertainty.
Compare Trade-Offs
Rank solubility, permeability, ionization, and developability together.
Plan Validation
Deliver prioritized compounds or formulations with recommended controls, measurements, and next-step criteria.
Results designed for compound selection and formulation decisions
The final package can combine candidate rankings, molecular interpretation, calculated datasets, property profiles, and a targeted validation plan.
Prioritized compounds with solubility, permeability, and ionization scores.
pH-solubility curves, permeability estimates, pKa, logD, and charge-state analysis.
Visual comparisons of solubility vs. permeability for analog series.
Structures, descriptors, model outputs, plots, and documentation.
Explanation of the molecular features driving favorable or unfavorable absorption behavior.
Suggested assays, controls, and experimental follow-up to confirm predictions.
Planning a solubility and permeability prediction project
Can intrinsic and apparent solubility be modeled together?
They must be distinguished. Apparent solubility can reflect ionization, salts, supersaturation, aggregation, or protocol effects, whereas intrinsic solubility refers to the neutral form under defined conditions.
Does high Caco-2 permeability guarantee good oral absorption?
No. Dissolution, solubility, efflux, metabolism, transporters, dose, formulation, and intestinal conditions also influence exposure.
Can a predicted BCS class be provided?
We can provide BCS-oriented risk interpretation, but formal classification depends on defined experimental solubility, permeability, dose, and regulatory criteria.
Can pH-dependent behavior be considered?
Yes, when pKa, charge state, logD, and relevant pH conditions are available or can be estimated, with uncertainty clearly noted.
How do you choose between PAMPA and Caco-2 follow-up?
PAMPA emphasizes passive diffusion, while Caco-2 can reflect cellular barriers and transporter effects. The choice depends on the mechanism and decision the experiment must resolve.
What information is needed to start a solubility and permeability project?
Helpful inputs include compound structures, desired endpoint definitions, pH range, assay context, measured data when available, dose and route context, and the decision the computational study should support.
Which compound can deliver the right balance of solubility and permeability for your target?
Share your compound series, available data, observed absorption issues, and performance targets. CD ComputaBio can develop a customized solubility and permeability prediction workflow.
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