How accurate are QSAR model predictions?

Accuracy depends on the quality and size of the training dataset, the similarity of query compounds to the training domain, and the endpoint being modeled. Our validation includes cross‑validation and external test sets to report performance metrics (R², RMSE, sensitivity). Predictions outside the applicability domain are flagged so you can assess reliability.

What toxicological endpoints can you predict?

We cover a wide range of endpoints, including acute oral toxicity (LD50), mutagenicity (Ames test), hepatotoxicity, carcinogenicity, developmental toxicity, and skin sensitization. Additional endpoints can be scoped as needed if training data are available.

Do regulatory agencies accept QSAR predictions?

Yes. OECD has published principles for the validation of QSAR models, and authorities such as the European Chemicals Agency (ECHA) and the FDA accept well‑documented in silico predictions as part of integrated testing strategies. ICH M7 explicitly endorses (Q)SAR for assessing mutagenic impurities. Our reports are formatted to support such submissions.

What data do I need to provide?

For predictions, we only need the chemical structure (SMILES, SDF, or MOL file). If you have proprietary assay data that you would like to incorporate into a custom model, we can discuss data integration during scoping.

How do you handle compounds that fall outside the applicability domain?

We compute an applicability domain (AD) for every model. Predictions for compounds outside AD are flagged with low confidence. Options include expanding the training set with similar compounds, using a second model, or recommending experimental testing only for those outliers.

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QSAR Toxicity Prediction Service

In Silico Toxicology

Harness AI-driven quantitative structure–activity relationship models to predict toxicological endpoints early, enabling safer product.

Our models predict mutagenicity, carcinogenicity, hepatotoxicity, and acute toxicity directly from the chemical structure of your compounds.
We employ validated machine learning models (MLR, neural networks, random forest) that are tailored to your specific chemical space and endpoint requirements.
You receive actionable reports that include applicability domain assessment, designed to support regulatory submissions such as REACH and ICH M7.
Scalable from single compounds to large libraries, scoped per project

Start Your Assessment

Why Choose Computational Toxicity Screening?

Reduce late-stage failures by identifying hazardous compounds before synthesis or animal studies.

Cost-effective screening of thousands of candidates
Supports 3Rs principles (Replace, Reduce, Refine animal testing)
Aligned with OECD and EU regulatory frameworks

AI-Powered

Regulatory-Ready

Early Detection

Methodology

Our QSAR Prediction Framework

We combine curated toxicological data, advanced descriptor computation, and rigorous model validation to deliver reliable toxicity predictions for your compounds.

Core QSAR Pipeline

Statistical models built from molecular descriptors to correlate structure with toxicity endpoints, using validated algorithms like GA-MLR, BPANN, and random forest.

Compound data curation from public and proprietary sources
Computation of 2D/3D molecular descriptors (topological, electronic, physicochemical)
Feature selection and model training (multiple algorithms compared)

Multi-Endpoint & AI Integration

Extend single-endpoint predictions to a battery of toxicity outcomes using ensemble learning and neural networks, enhancing coverage for regulatory decision-making.

The service enables simultaneous prediction of acute oral toxicity (LD50), hepatotoxicity, mutagenicity, and additional endpoints as scoped.
Applicability domain analysis to flag low-confidence predictions
Customizable to your chemical class or formulation

Data Quality

We curate training sets from peer-reviewed databases and in-house assays, ensuring consistent endpoint annotation and molecular structure standardization.

Statistical Rigor

Internal cross-validation, external test set validation, and Y-scrambling tests are applied to every model to guard against overfitting and confirm predictive power.

Interpretable Results

Descriptors contributing to toxicity are identified, giving mechanistic insight alongside predicted values—supporting transparent regulatory submissions.

What You Receive

Deliverables & Reporting

Clear, science-backed outputs designed to integrate into your safety assessment workflow.

Data Deliverables

Predicted toxicity values (e.g., LD50, probability of mutagenicity) per compound
Molecular descriptor set used for each model
Applicability domain index for every prediction
Model performance statistics (R², RMSE, sensitivity, specificity)

Reporting & Support

Comprehensive report with methods, validation summary, and endpoint-specific conclusions
Interpretation of key descriptors driving predictions
Optional regulatory‑ready documentation for REACH, CLP, or ICH M7 submissions
Consultation on integrating predictions into your risk assessment strategy

Customization

Service Scope — Tailored per Project

Predictions are scoped to your chemical inventory, endpoint needs, and regulatory context. The table below outlines typical parameters configured during consultation.

Parameter	Typical Project Scope
Toxicological Endpoints	Endpoints include acute oral toxicity (LD50), mutagenicity (Ames), hepatotoxicity, carcinogenicity, developmental toxicity, and skin sensitization, selectable per project.
Molecular Descriptors	2D/3D: ALOGP, RDF, GATS, topological, electronic, and quantum-chemical descriptors (e.g., from Dragon, RDKit, or custom)
Modeling Algorithms	Algorithms include multiple linear regression (MLR), back‑propagation artificial neural network (BPANN), random forest, support vector machine, and gradient boosting.
Validation Approach	Validation methods include leave‑one‑out, k‑fold cross‑validation, external test set (proportion scoped per project), Y‑scrambling, and applicability domain assessment via leverage or distance‑based methods.
Applicability Domain	Assessed for each query compound — predictions flagged if outside AD; optional expansion with additional training data
Output Format	Excel/CSV with predictions and AD flags; PDF report with full methodology; optional SDF of descriptors

Process

How Predictions Are Generated

A structured, five‑stage pipeline from data to decision‑ready report.

Data Collection & Curation

We gather toxicological data from peer‑reviewed databases (e.g., EPA ToxCast, PubChem) and client‑provided assays. Structures are standardized, duplicates removed, and endpoints harmonized.

Molecular Descriptor Computation

A comprehensive set of molecular descriptors (topological, electronic, physicochemical) is calculated for each training compound using validated cheminformatics toolkits.

Model Training & Selection

Multiple machine‑learning algorithms (MLR, BPANN, random forest) are trained on the curated dataset. Feature selection reduces dimensionality, and models are compared by cross‑validated performance.

Model Validation

The best‑performing model(s) undergo rigorous internal and external validation. Applicability domain is defined to ensure predictions are reliable for query compounds within the training space.

Prediction & Reporting

Query compounds are submitted to the validated model. Results include predicted toxicity values, AD flags, and an interpretable report highlighting key molecular features driving the prediction.

Use Cases

Where QSAR Toxicity Prediction Adds Value

From early drug discovery to regulatory hazard assessment, our models support diverse industries.

Early Safety Screening of Drug Candidates

Predict hepatotoxicity, mutagenicity, and off‑target toxicity before synthesis, enabling prioritization of safer lead compounds and reducing later‑stage attrition.

Screen virtual libraries of millions of compounds rapidly
Identify structural alerts and toxicophores early in the pipeline

Animal‑Free Safety Assessment

Meet EU cosmetics regulation and REACH requirements by replacing in vivo tests with in silico predictions for endpoints like skin sensitization and acute oral toxicity.

Support for CLP classification and hazard labeling
Cost‑effective screening of formulation ingredients

Strengthen Submissions with Computational Evidence

Leverage OECD‑validated QSAR models to justify waiving or reducing animal tests in regulatory dossiers (REACH, ICH M7, EPA).

Generate robust documentation for mutagenicity (Ames) predictions
Combine with read‑across or in vitro data for weight‑of‑evidence arguments

Common Questions

Frequently Asked Questions

Clarifying how our QSAR predictions support your safety decisions.

Accuracy depends on the quality and size of the training dataset, the similarity of query compounds to the training domain, and the endpoint being modeled. Our validation includes cross‑validation and external test sets to report performance metrics (R², RMSE, sensitivity). Predictions outside the applicability domain are flagged so you can assess reliability.
We cover a wide range of endpoints, including acute oral toxicity (LD50), mutagenicity (Ames test), hepatotoxicity, carcinogenicity, developmental toxicity, and skin sensitization. Additional endpoints can be scoped as needed if training data are available.
Yes. OECD has published principles for the validation of QSAR models, and authorities such as the European Chemicals Agency (ECHA) and the FDA accept well‑documented in silico predictions as part of integrated testing strategies. ICH M7 explicitly endorses (Q)SAR for assessing mutagenic impurities. Our reports are formatted to support such submissions.
For predictions, we only need the chemical structure (SMILES, SDF, or MOL file). If you have proprietary assay data that you would like to incorporate into a custom model, we can discuss data integration during scoping.
We compute an applicability domain (AD) for every model. Predictions for compounds outside AD are flagged with low confidence. Options include expanding the training set with similar compounds, using a second model, or recommending experimental testing only for those outliers.

Scientific Literature

References

Li J, Wang C, Yue L, et al. Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review. Ecotoxicology and environmental safety. 2022;243:113955. View on PubMed
Kianpour M, Mohammadinasab E, Isfahani TM. Prediction of Oral Acute Toxicity of Organophosphates Using QSAR Methods. Current computer-aided drug design. 2021;17(1):38-56. View on PubMed
Romano JD, Hao Y, Moore JH, et al. Automating Predictive Toxicology Using ComptoxAI. Chemical research in toxicology. 2022;35(8):1370-1382. View on PubMed
Tsakovska I, Diukendjieva A, Worth AP. In Silico Models for Predicting Acute Systemic Toxicity. Methods in molecular biology (Clifton, N.J.). 2022;2425:259-289. View on PubMed
Novič M. Quantitative Structure Activity/Toxicity Relationship through Neural Networks for Drug Discovery or Regulatory Use. Current topics in medicinal chemistry. 2023;23(29):2792-2804. View on PubMed

Please kindly note that our products and services are for research use only.