Data Preparation

How to format and prepare your molecular data for KAST.

File Format: SMILES

SMILES (Simplified Molecular Input Line Entry System) is the standard text format for molecules.

Format:

SMILES [space/tab] optional_name

Example:

CC(C)Cc1ccc(cc1)C(C)C(O)=O  ibuprofen
CN1C=NC2=C1C(=O)N(C(=O)N2C)C  caffeine
CC(C)CC1=CC(=C(C=C1)C(C)C)O  another_compound

File Organization

Place SMILES files in the data/ folder:

KAST/
├── data/
│   ├── actives.smi        ← Active compounds (required)
│   ├── inactives.smi      ← Inactive compounds (required)
│   └── my_library.smi     ← (Optional) New molecules to predict
├── workspaces/            ← Outputs will go here
├── main.py
└── ...

Required Files

data/actives.smi

Active (bioactive) compounds that show the desired property.

Minimum: 50 molecules (use with caution; see Data Quality Guidelines) Recommended: 500+ molecules for Deep Learning accuracy

Format: SMILES string followed by a space or tab and the molecule name.

CC(C)Cc1ccc(cc1)C(C)C(O)=O  ibuprofen
CN1C=NC2=C1C(=O)N(C(=O)N2C)C  caffeine
CC(C)CC1=CC(=C(C=C1)C(C)C)O  compound3

data/inactives.smi

Inactive (non-bioactive) compounds that do not show the desired property.

Minimum: 50 molecules (use with caution; see Data Quality Guidelines) Recommended: 500+ molecules for Deep Learning accuracy

Format: SMILES string followed by a space or tab and the molecule name.

CCCCCCCCCCCCCCCC  hexadecane
CC(=O)OC1=CC=CC=C1C(=O)O  aspirin
CCC(C)C(O)=O  carboxylic_acid

Optional File

data/my_library.smi

New molecules to screen (predict activity).

Can have any name, used in Step 5-6.

Example:

CCc1ccccc1  ethylbenzene
Cc1ccccc1  toluene
CCC1=CC=CC=C1O  ethylphenol

Data Quality Guidelines

Good Practices

  • Use canonical SMILES (standardized form)

  • Remove duplicates before starting

  • Validate SMILES structures with RDKit

  • Balance active/inactive ratio (aim for 1:1 to 1:10)

  • Aim for 500+ molecules per class for reliable Deep Learning performance

  • Maximum 100,000 molecules per file

ML/DL Warning (Overfitting Risk)

  • Minimum Threshold (50 molecules): Technically accepted by the pipeline, but strongly discouraged for Deep Learning. With small datasets, models will likely overfit, memorizing the training data rather than learning chemical features.

  • Data Scaling: If you have < 200 molecules per class, consider using simpler machine learning models (e.g., Random Forest or SVM) instead of deep neural networks to maintain predictive stability.

Avoid

  • Invalid SMILES (won’t parse)

  • Salts/mixtures (unless intended)

  • Very small molecules (< 5 heavy atoms)

  • Very large molecules (> 200 heavy atoms)

  • Incomplete or corrupted files

Quality Check

Before running KAST, validate your SMILES:

# Option 1: Use KAST's built-in check
python main.py
[1] Data Preparation
# KAST automatically validates during preparation and generates an audit report:
# workspaces/<your_workspace>/prepared_data/invalid_smiles_report.txt
# This file will list every discarded SMILES and the reason (e.g., duplicate, invalid_rdkit).

# Option 2: Manual check with RDKit
python -c "
from rdkit import Chem
with open('data/actives.smi') as f:
    for line in f:
        smiles = line.split()[0]
        if Chem.MolFromSmiles(smiles) is None:
            print(f'Invalid SMILES: {smiles}')
"

Balance Active/Inactive

Recommended ratios:

  • 1:1 (50 active, 50 inactive) — Balanced (optimal for training)

  • 1:5 (100 active, 500 inactive) — Realistic for many screening libraries

  • 1:10 (100 active, 1000 inactive) — Highly imbalanced (requires more attention to metrics)

Handling Imbalance: KAST incorporates strategies to manage imbalanced datasets within the pipeline. While the system can process significantly imbalanced data, maintaining a ratio between 1:1 and 1:10 is recommended for better model generalization and to avoid bias toward the majority class (Banerjee et al., 2018; Jiang et al., 2025).

Large Datasets (100K+ molecules)

For very large libraries:

  1. Enable parallel processing (see Parallel Processing)

  2. Increase PARALLEL_BATCH_SIZE if you have 16GB+ RAM

  3. Use filtering to reduce library size first

  4. Monitor RAM during featurization

Canonicalization

Note: KAST automatically canonicalizes all your SMILES during the 1_preparation.py step. You do not need to canonicalize your files manually for the pipeline to work.

If you prefer to pre-process your files externally to ensure consistency before importing them into KAST, you can use the following RDKit snippet:

# Optional: Manual canonicalization using RDKit
python -c "
from rdkit import Chem
import sys

infile, outfile = sys.argv, sys.argv[1][2]
with open(infile) as f, open(outfile, 'w') as out:
    for line in f:
        parts = line.strip().split()
        smiles = parts
        name = parts if len(parts) > 1 else ''[1]
        mol = Chem.MolFromSmiles(smiles)
        if mol:
            canonical = Chem.MolToSmiles(mol)
            out.write(f'{canonical}  {name}\n' if name else f'{canonical}\n')
" data/actives.smi data/actives_canonical.smi

References

  1. Banerjee et al. (2018). Prediction Is a Balancing Act…. Frontiers in Chemistry.

  2. Jiang et al. (2025). A review of machine learning methods for imbalanced data challenges in chemistry. Chemical Science.