Risk assessment of chemicals

Generate robust physicochemical properties to fill data gaps in your REACH evaluations. We provide the pure, high-fidelity calculation data you need to build your compliance reports.

Empowering Your Compliance Workflows

Our package calculates the critical physicochemical and effect-based endpoints required by regulatory bodies.

• Data You Can Trust: Get highly accurate predictions to support your chemical safety assessments.
• Intuitive & Flexible Input: Start calculations instantly using standard SMILES strings. Our intuitive interface means you do not need to struggle with rigid, pre-formatted CSV templates to initiate your work.
• Multiple Methodologies: Cross-validate results using different computational methods to ensure the highest reliability.
• Batch Processing & Seamless Export: Run single predictions or large-scale batch calculations effortlessly. Extract your results as clean CSV files to easily format the raw data for your specific regulatory submission templates.

Advanced Modeling for Risk Assessment

Choose from a comprehensive suite of models to predict a broad spectrum of properties - including the octanol-water partition coefficient (log P), water solubility (log Sw), and the bioconcentration factor (log BCF), among many others. We allow you to compare results across the following methodologies:

• Deep Neural Networks: Our highest-performing models, engineered to deliver exceptional accuracy across the entire chemical space - from basic everyday compounds to the most complex chemical structures.
• Group Contribution Methods: Transparent and established baseline models.
• k-Nearest Neighbors (k-NN): Fast and reliable estimations based on structural similarity.

Peer-Review Validated

We don’t just calculate; we validate. Our predictive models have been benchmarked against leading industry tools and published in peer-reviewed scientific journals.

Publications

• "Prediction of the water solubility by a graph convolutional-based neural network on a highly curated dataset.", J. Cheminformatics, 2025. https://doi.org/10.1186/s13321-025-01000-9
• "Prediction of melting points of chemicals with a data augmentation-based neural network approach.", ACS Omega, 2025. https://doi.org/10.1021/acsomega.5c00205
• "Prediction of solute descriptors for linear solvation energy relationships using k-nearest neighbours, group contributions, and graph-convolutional neural networks", ACS Environ. Au, 2026. https://doi.org/10.1021/acsenvironau.6c00063

Models in the Package

• Octanol-Water Partition Coefficient log Kow

  GNN

• Water solubility log Sw

  GNN

• Bioconcentration factor log BCF

  GNN

• Octanol-water partition coefficient log Kow

  GCA

• Water solubility log Sw

  GCA

• Bioconcentration factor log BCF

  GNN

• Octanol-Water Partition Coefficient log Kow

  kNN

• Acute Fish Toxicity log LC50

  GNN

• Bioconcentration factor log BCF

  GCA

• Acute toxicity Daphnia Magna log LC50

  GNN

• Air-Water Partition Coefficient log Kaw

  GNN

• Acute toxicity fish log LC50

  GCA

• Octanol-Air Partition Coefficient log Koa

  GNN

• OC-Water Partition Coefficient log Koc

  GNN

• Melting Point MP

  GNN

• Boiling point

  GNN

• Melting Point MP

  kNN

• OC-Water Partition Coefficient log Koc

  kNN

• Octanol-Air Partition Coefficient log Koa

  kNN

• Air-Water Partition Coefficient log Kaw

  kNN

• Bioconcentration Factor log BCF

  kNN

• Water Solubility log Sw

  kNN

• Boiling Point BP

  kNN

• Acute Toxicity Daphnia Magna log LC50

  kNN

• Acute Fish Toxicity log LC50

  kNN

• Algae Growth Inhibition log EC50

  kNN

• Algae Growth Inhibition log EC50

  GNN