Quick Create REINVENT4 Job

Fast job creation with predefined presets

REINVENT4 Not Installed

REINVENT4 is not installed on this server. Jobs will fail with an installation error. Please contact your system administrator to install REINVENT4 before creating jobs.

Installation guide available in: REINVENT4_INSTALLATION_REQUIRED.md

Quick Create: This form uses preset configurations optimized for common use cases. For advanced customization, use the full creation form.

Job Name *

Give your molecular generation job a descriptive name

QSAR Model Selection *

Note: Only publicly available QSAR models can be used for predictions. If you want to add your QSAR model to the publicly available list, please contact Dr Salah Alshehade.

Selected Model Details

Model Name:: -
Algorithm:: -
Created By:: -
Created Date:: -
Status:: -

Validation R²:: -
Training R²:: -
RMSE:: -
Description:: -

Tip: Higher R² values indicate better model performance. Choose a model trained on data similar to your target molecules.

Target Number of Molecules *

Number of molecules to generate (default: 100)

Generation Preset *

Preset Descriptions:

Default: Balanced settings suitable for most applications (1000 molecules, 500 steps)
Fast/Exploratory: Quick generation with high diversity for initial exploration (500 molecules, 250 steps)
Thorough/Exploitative: Deep optimization focused on quality over quantity (2000 molecules, 1000 steps)
Balanced: Medium-sized run balancing diversity and optimization (1500 molecules, 750 steps)

Description (Optional)

Optional notes about this job's objectives

What will be configured:

Generation Mode: Reinvent (de novo design)
Learning Algorithm: Transfer Learning
QSAR Weight: 0.6, QED Weight: 0.4
Diversity Threshold: 0.4
Batch Size: 128

Cancel

Back to REINVENT4 Dashboard

Need more control? Use the Advanced Creation Form

How It Works

Generation

REINVENT4 uses AI to generate novel molecule structures (SMILES) based on your configuration.

QSAR Evaluation

Each generated molecule is evaluated by your selected QSAR model to predict biological activity.

Scoring

Scores are combined (QSAR + QED) to create a composite fitness score for each molecule.

Learning

REINVENT4's neural network learns from high-scoring molecules and generates improved candidates.

Optimization

Over multiple iterations, the system converges toward molecules with optimal properties.