Cyto-Safe
Advancing Safety Assessment with AI-QSAR Models for Predicting Cytotoxicity of Novel Drug Candidates
A Comprehensive Guide to Using the Cyto-SafeApp's Advanced Machine Learning Model for Reliable Results
About
We are thrilled to introduce Cyto-Safe, the latest breakthrough in predicting the cytotoxicity of drug candidates. Developed with state-of-the-art machine learning algorithms, Cyto-Safe is a web tool that will revolutionize how you assess the safety of your pharmaceutical compounds. Here are the key highlights of Cyto-Safe:
With Cyto-Safe, you are one step ahead in designing safer and more effective drugs. Don't miss the opportunity to experience this innovation in cytotoxicity prediction.
Cutting-edge ML
Cyto-Safe harnesses the power of cutting-edge machine learning algorithms to refine drug development processes. These advanced algorithms are the cornerstone of the application's predictive capabilities, allowing for accurate evaluations of cytotoxicity risks across a spectrum of drug candidates.
Robust Dataset
Cyto-Safe's models are built on a solid foundation, trained with a substantial and consistent dataset. The experimental dataset tested for cytotoxicity in 3T3 and HEK 293 cells, followed by meticulous data treatment and curation ensures that Cyto-Safe's predictive models deliver accuracy and reliability.
Meticulous Validation
Cyto-Safe's models have been rigorously tested and validated in accordance with the latest industry standards, specifically following the principles for the validation, for regulatory purposes, of (quantitative) structure-activity relationship models.
Explainable AI
Take a closer look at how Cyto-Safe's predictions work with the help of Explainable AI (XAI). Discover the clear insights provided by Shapley value analysis, helping you understand the factors influencing cytotoxicity predictions.
Advanced Accuracy
Cyto-Safe uses QSAR models, providing unparalleled accuracy in predicting the cytotoxicity of drug candidates. With this application, you can make more informed decisions and reduce the risk of unwanted side effects.
User-Friendly Design
We designed Cyto-Safe with simplicity in mind. Its intuitive interface makes cytotoxicity prediction accessible to researchers and scientists, regardless of their machine learning expertise. Simply upload your data and let Cyto-Safe do the heavy lifting.
CytoSafe Predictor
INSTRUCTIONS
Draw a molecule or paste a SMILES string
Use the molecular editor to draw the chemical structure of the compound you want to evaluate. Alternatively, paste a SMILES string into the text input below the editor.
Hit the predict button and wait for results
Click 'PREDICT' to submit your molecule. Requests are processed via a fair queue. Please wait and do not close the page while your prediction is being processed.
Review cytotoxicity results for both cell lines
After prediction completes, you will see results for BALB/c 3T3 and HEK-293 cell lines, including the classification (Toxic/Non-toxic), confidence score, and applicability domain status.
Generate SHAP explanations for each cell line
Click 'Explain Prediction' on each result card to generate a SHAP-based visualization showing which molecular substructures contribute to the cytotoxicity prediction.
Before you submit
Data handling, curation & privacy
A short, honest note on what CytoSafe does and does not do with the chemistry you submit. Please read before running predictions on novel structures.
CytoSafe does not perform full chemical standardization on submitted structures. Before predicting, please curate your inputs: remove counter-ions and salts, neutralize charges where appropriate, and select a canonical tautomer. The server applies only basic RDKit canonicalization on the SMILES it receives. Downstream predictions and applicability-domain checks are sensitive to the exact form you submit.
If RDKit cannot parse your structure (malformed SMILES, broken valences, exotic atoms outside the model's training distribution), the request fails with an error message. Always sanity-check inputs in a chemistry editor before submitting. For parseable but chemically unusual molecules, predictions are still returned. Use the Applicability Domain panel in the results to judge whether the prediction is trustworthy.
We do not persist your submitted SMILES, SDF, or MOL data. To accelerate repeated queries we cache results in Redis keyed only by a SHA-256 hash of the canonical SMILES, with a 7-day expiry. The original structure is never written to disk on our side. You can submit novel chemistry with confidence.
The free CytoSafe web server predicts a single molecule per submission. SDF and MOL files are read in your browser by the chemistry editor, which loads one structure at a time onto the canvas, so any uploaded file is treated as a single-molecule request. Multi-molecule batch prediction will be available exclusively through our upcoming commercial offering, Insight AI Pro. For early access and pricing, please contact us at carolina@ufg.br.