lazyqsar 2.3.0

A library to quickly build QSAR models

Ersilia's LazyQSAR

A library to build supervised QSAR models for chemistry quickly.

Installation

Install LazyQSAR from source:

git clone https://github.com/ersilia-os/lazy-qsar.git
cd lazy-qsar
python -m pip install -e .

To use the built-in LazyQSAR descriptors, install the optional dependencies:

python -m pip install -e .[descriptors]

This will enable descriptor (featurizer) calculation. The first time you run LazyQSAR, it will download the Chemeleon and CDDD model checkpoints. To complete this setup in advance, run:

lazyqsar-setup

Use as a Python API

Binary Classification

LazyQSAR's binary classifier can run either with built-in descriptors (takes SMILES as input) or with custom pre-computed descriptors.

Built-in descriptors

Instantiate LazyBinaryQSAR with a mode of choice:

Mode	Descriptors used	Speed
fast	RDKit, Morgan fingerprints	Fastest, no deep-learning descriptors
default	Chemeleon, RDKit, CDDD	Balanced
slow	Chemeleon, Morgan, RDKit, CDDD	Most thorough

from lazyqsar.qsar import LazyBinaryQSAR

model = LazyBinaryQSAR(mode="default")
model.fit(smiles_list=smiles_train, y=y_train)
y_hat = model.predict_proba(smiles_list=smiles_test)[:, 1]

Custom descriptors

Pre-calculate your own descriptors and pass them directly. We recommend the Ersilia Model Hub for this — its .h5 output format is supported natively. Alternatively, pass descriptors as a NumPy array.

from lazyqsar.agnostic import LazyBinaryClassifier

# From a NumPy array
model = LazyBinaryClassifier(mode="default")
model.fit(X=X_train, y=y_train)
y_hat = model.predict_proba(X=X_test)[:, 1]

# From an Ersilia .h5 file
model.fit(h5_file="descriptors.h5", y=y_train)
y_hat = model.predict_proba(h5_file="descriptors.h5")[:, 1]

Saving and loading models

Models are saved as ONNX files by default, so inference only requires the ONNX runtime.

# Save after training
model.save(model_dir)

# Load for inference (auto-detects ONNX or raw format)
from lazyqsar.agnostic import LazyBinaryClassifier

model = LazyBinaryClassifier.load(model_dir)
y_hat = model.predict_proba(X=X)[:, 1]

You can also save and load as a .zip archive:

model.save("my_model.zip")
model = LazyBinaryClassifier.load("my_model.zip")

The same save/load interface applies to LazyBinaryQSAR:

from lazyqsar.qsar import LazyBinaryQSAR

model = LazyBinaryQSAR(mode="default")
model.fit(smiles_list=smiles_train, y=y_train)
model.save(model_dir)

model = LazyBinaryQSAR.load(model_dir)
y_hat = model.predict_proba(smiles_list=smiles_test)[:, 1]

Tests and benchmarks

Quick testing

The tests/ folder contains scripts for quickly verifying that the code works. The Bioavailability dataset is used as an example.

python tests/test_binary_classification.py
python tests/test_binary_classification.py --agnostic

Benchmarking

The benchmark repository contains performance results for the default estimators and descriptors on the TDCommons ADMET dataset.

Use as a CLI

The CLI expects a data_dir containing one CSV file per task. Each CSV must have SMILES in the first column and binary labels (0/1) in the second column, with a header row.

Fit:

lazyqsar-binary-fit --data_dir $DATA_DIR --model_dir $MODEL_DIR --mode default

Optionally, pass a --models_txt file listing which tasks (CSV filenames without extension) to train, one per line. Without it, all CSVs in the directory are used.

lazyqsar-binary-fit --data_dir $DATA_DIR --model_dir $MODEL_DIR --models_txt models.txt

Predict:

lazyqsar-binary-predict --input_csv $INPUT_CSV --model_dir $MODEL_DIR --output_csv $OUTPUT_CSV

Disclaimer

This library is intended for quick QSAR modeling. For a more complete automated QSAR pipeline, refer to Zaira Chem.

About us

Learn about the Ersilia Open Source Initiative!