Descriptor values
Model-specific descriptors are extracted from each submitted molecule.
QSAR screening for malaria and leishmaniasis research
RondonQSAR
Submit molecular structures, follow QSAR processing, and review PlasmoQSAR and LeishQSAR predictions in a browser-based research workspace.
InputSDF molecular structures
ModelsPlasmoQSAR and LeishQSAR outputs
ReferenceACS Omega, DOI 10.1021/acsomega.4c05768
Research basis
From QSAR research to a reusable screening workflow
The first model highlighted in RondonQSAR comes from published work on triclosan analogs tested against Plasmodium falciparum 3D7. The application carries that QSAR workflow into a form researchers can run, track, and review from the browser.
Featured publicationDOI 10.1021/acsomega.4c05768
Application of Machine Learning in the Development of Fourth Degree Quantitative Structure-Activity Relationship Model for Triclosan Analogs Tested against Plasmodium falciparum 3D7
The study combines machine learning and QSAR to produce a supervised fourth-degree polynomial model. In RondonQSAR, that work is represented through descriptor-based prediction, pEC50 and EC50 estimates, and result tables for submitted molecules.
Target
Triclosan analogs tested against Plasmodium falciparum 3D7
Approach
Machine learning applied to fourth-degree QSAR modeling
Context
Antimalarial screening supported by Fiocruz Rondônia research groups
Workflow
A direct path from SDF file to QSAR results
RondonQSAR keeps the routine work simple: upload molecules, wait for the calculation, and open the resulting model tables when processing is complete.
Submit structures
Upload SDF files containing the molecules to be evaluated.
Follow processing
Track each submission through queued, processing, completed, and failed states.
Run both QSAR models
Use one submission to generate PlasmoQSAR and LeishQSAR outputs.
Review predictions
Inspect descriptor values, pEC50 and EC50 predictions, and formula views.
Screening studies
Organize malaria and leishmaniasis QSAR work in the same application.
Research records
Keep submitted files, job identifiers, statuses, and outputs connected.
Outputs
Results designed for review and follow-up
Completed submissions bring the calculation record and prediction output together: descriptors, pEC50 and EC50 values, model tabs, formulas, and processing status.
Prediction tables
PlasmoQSAR and LeishQSAR results are shown in dedicated tables.
Formula views
Completed submissions include the formula view used to organize model terms.
Submission history
Status, file name, job ID, and timestamps remain available after submission.
Institutional support
Developed with research collaborators
RondonQSAR is connected to Fiocruz Rondônia and collaborating groups in medicinal chemistry, bioinformatics, and biosystems modeling.
LABIOQUIM
FIOCRUZ
FIOCRUZ/RO
UFCSPANext step
Start a QSAR submission.
Sign in, upload an SDF file, and follow the calculation through to PlasmoQSAR and LeishQSAR results.