Cheminformatics & Machine Learning

PREDACTORS

Predicting Activity in Organic Samples — a SMILES-based platform for screening the biological activity of organic molecules, powered by Machine Learning models trained from molecular docking simulations.

See Features

Try PREDACTORS Now

Access the platform directly in your browser — no installation required. Draw or paste a molecule's SMILES notation and get an instant activity prediction.

Platform not loading right away? The backend runs on a free server tier — the first request can take 50 seconds to 1 minute while it wakes up.
Built with: React, FastAPI, RDKit, the Ketcher molecular editor, and a Machine Learning model trained on molecular docking simulation data. Free for educational and research use.
Features

What PREDACTORS does

Everything you need for fast, reproducible molecular activity screening.

Interactive Molecule Editor

Draw structures or paste SMILES directly using the built-in Ketcher chemical editor — no external software needed.

Instant Activity Prediction

Get a clear ATIVO (active) or INATIVO (inactive) classification against the target in seconds.

Docking-Trained Model

The underlying ML model was trained on descriptors derived from molecular docking simulations, not experimental data alone.

3D Molecule Visualization

Inspect the predicted molecular structure in an interactive 3D viewer right in the browser.

No Docking Software Required

Skip hours of docking setup — PREDACTORS delivers a fast, indicative prediction using pre-trained models.

Free & Open for Research

Built for researchers, students, and chemists — free to use for educational and research purposes.

How It Works

From SMILES to prediction in four steps

No installation and no docking software — everything runs in your browser.

1

Access the platform

Open PREDACTORS at predactors.vercel.app — no installation needed.

2

Enter the SMILES code

Draw the molecule or paste its SMILES notation into the input field.

3

Wait for processing

The system loads the pre-trained ML model and computes molecular descriptors — this can take 50 seconds to 1 minute.

4

View the prediction

The molecule is classified as ATIVO or INATIVO based on its predicted interaction with the biological target.

About the Project

Scientific Purpose

PREDACTORS was developed as part of research exploring the molecular potential of natural products against COVID-19 through molecular modeling and Machine Learning. The platform makes the resulting predictive model publicly accessible to other researchers, chemists, and students.

By combining molecular descriptors computed via cheminformatics tools with a Machine Learning model trained on molecular docking simulation data, PREDACTORS estimates whether a given organic molecule is likely to be active against a biological target — without requiring users to run their own docking software.

As with any computational screening tool, predictions from PREDACTORS are indicative, not diagnostic. They are meant to help prioritize candidate molecules for further experimental or computational investigation, not to replace laboratory validation.

Related publication: “Exploring the molecular potential of natural products against COVID-19 through Molecular Modeling and Machine Learning.” Full profile on ORCID ↗.
Team

Who built PREDACTORS

Prof. Dr. Rafael Vieira
Prof. Dr. Rafael Vieira
Professor of Chemistry — Federal Institute of Rondônia (IFRO), Ji-Paraná
VB
Vitor Hugo Batista
Undergraduate student in Chemistry — Federal Institute of Rondônia (IFRO), Ji-Paraná

Ready to screen your first molecule?

Access PREDACTORS and get an activity prediction for your compound in less than a minute.