Perform Your Chemical Predictions with Advanced QSAR Modeling

Our platform leverages advanced QSAR modeling and cheminformatics to deliver fast and reliable molecular predictions. Designed for researchers and scientists, alvaQSAR provides a user-friendly yet powerful workflow that enables the use of models built in accordance with OECD principles and REACH guidelines.

Calculating the prediction...

Enter Molecular Structure

Draw a molecule
Login Required

Our Models

Biomagnification Factor (BMF)

Estimates the potential for chemical accumulation through the food chain via dietary exposure. Supports bioaccumulation risk assessment using a QSAR model.

Daphnia magna LC50

Predicts 48-hour EC50 values for Daphnia magna. Supports environmental hazard assessment and regulatory risk evaluation.

Bioconcentration Factor (BCF)

Estimates bioaccumulation potential in aquatic organisms. Supports evaluation of chemical persistence and regulatory compliance.

Ready Biodegradability

Predicts whether a chemical degrades rapidly under aerobic conditions. Helps identify environmentally persistent substances.

Fathead minnow LC50

Predicts 96-hour LC50 values for Pimephales promelas (Fathead Minnow). Supports aquatic toxicity screening and chemical risk assessment.

{{ endpointUIName }}

Molecular Properties
{{ propUINames[property.name] }} {{ property.value }}
Information
SMILES {{ finalSMILES }}
Formula {{ molFormula }}
InChI {{ molInchi }}
Endpoint {{ endpointUIName }}
Description {{ endpointDescription }}
Dependent Variable {{ endpointDependentVariable }}
{{ modelUINames[model.name] }}
Description {{ modelDescriptions[model.name] }}
Prediction {{ model.prediction }}
Applicability Domain {{ model.applicability_domain }}

Published results derived from alvaQSAR must include the following citation: Alvascience, alvaQSAR (Advanced QSAR Modeling Platform) v{{ apiResponse.application_info.version }}, 2025, https://www.alvascience.com/

Empowering chemical predictions with Advanced Technology

Our platform harnesses cutting-edge QSAR modeling and cheminformatics to deliver rapid, reliable, and cost-effective chemical predictions. By integrating advanced algorithms with molecular descriptors, we provide accurate insights into key properties, helping researchers and industries make informed decisions with confidence.

Your Pathway to Precise QSAR Predictions

Our QSAR prediction platform simplifies chemical property estimation, making it fast, intuitive, and reliable. In just four steps, gain scientifically validated insights into molecular properties with ease.

Define Your Molecular Structure

Draw your molecule using the editor or enter a SMILES string to set up your prediction

Select the QSAR Model

Choose from our advanced predictive models, each tailored to assess key chemical properties with high accuracy.

Analyze Your Results

Get instant predictions with applicability domain insights and key molecular properties.

Download the Prediction Report

Download a complete prediction PDF report for further analysis.

Why Use Our QSAR Models?

Our QSAR models provide an efficient and reliable approach to chemical property prediction, replacing costly and time-consuming experimental methods while maintaining scientific accuracy and regulatory relevance.

Cost-Effective Alternative to Experimental Testing

QSAR modeling significantly reduces the need for expensive laboratory experiments, minimizing material costs, time, and ethical concerns related to animal testing. With our computational approach, chemical predictions become faster and more affordable, making early-stage screening more accessible.

Regulatory Compliance

Our QSAR models align with OECD principles and REACH guidelines, ensuring regulatory acceptance. Each model includes an applicability domain (AD) assessment to verify prediction reliability, enhancing transparency and decision-making while minimizing experimental testing.

Speed, Efficiency & Continuous Innovation

High-speed computational models generate accurate chemical predictions in seconds. Continuous advancements incorporate new experimental data and machine learning innovations, ensuring ongoing improvements in scientific accuracy.