ZairaChem

ZairaChem is Ersilia's AutoML tool for supervised learning of small molecule activity and property data. In the field of computer-aided drug discovery, this task is known as Quantitiative Structure Activity/Property Relationship Modeling (QSAR/QSPR).

ZairaChem offers a relatively complex ensemble modeling pipeline, showing robust performance over a wide set of tasks. Currently, ZairaChem is focused on binary classification tasks.

In brief, in ZairaChem molecules are represented numerically using a combination of distinct descriptors that can be easily selected from the Ersilia Model Hub. The rationale is that combining multiple descriptors will enhance applicability over a broad range of tasks, ranging from aqueous solubility predictions to phenotypic outcomes. Subsequently, the fast, lightweight LazyQSAR estimator pipeline is used to yield accurate models without the need for human intervention (i.e. algorithm choice, hyperparameter tuning, etc.).

Installation

ZairaChem can be installed as follows:

git clone https://github.com/ersilia-os/zairachem-docker.git
cd zairachem-docker
conda create -n zairachem python=3.12
conda activate zairachem
pip install -e .

zairachem --help

Input and Output

To run ZairaChem, a file with two columns is required:

• SMILES: a list of smiles for model training

• Label: a binary label (1 or 0) or a continuous experimental value. If the latter, the binarization cut-off and direction must also be specified by the user (see below)

The output of ZairaChem is a model folder that contains all the necessary files to run the model as well as automated reports on performance. Please note that ZairaChem will not automatically do train/test splits and those need to be prepared by the user and run in subsequent trials.

Fit

You can train a model as follows:

zairachem fit -i train.csv -m model

This command will run the full ZairaChem pipeline and produce a model folder with processed data, model checkpoints, and reports.

Predict

You can then run predictions on the test set:

zairachem predict -i test.csv -m model -o test

ZairaChem will run predictions using the checkpoints stored in model and store results in the test directory. Several performance plots will be generated alongside prediction outputs.

Pipeline steps

Internally, the ZairaChem pipeline consists of the following steps:

1. session: a session is initialized pointing to the necessary system paths.

2. setup: data is processed and stored in a cleaned form.

3. describe: molecular descriptors are calculated.

4. estimate: models are trained or predictions are done on trained models.

5. pool: results from multiple models from the ensemble are aggregated.

6. report: output data is assembled in a spreadsheet, and plots are created for easy inpection of results.

7. distill (mainly based on the Olinda package; integration in progress 👷): lightweight versions of the models are created for quick prediction.

8. finish: the session is closed and residual files are deleted.

Session

You can start a ZairaChem training session as follows:

zairachem session --fit -i train.csv -m model

Likewise, you can start a prediction session:

zairachem session --predict -i test.csv -m model -o test

The session command will simply create the necessary folders and a session log.

Setup

In this step, data preparation is done, including:

• Identification of relevant columns (compound identifier, SMILES, and value) in the input file.

• Chemical structure standardization.

• Deduplication.

• Data balancing and augmentation using a reference set of molecules (e.g. ChEMBL).

• Binarization when a cutoff is specified.

• Transformation (Guassianization) of continuous data.

• Folds and clusters assignments.

Give an initialized session (fit or predict), data preparation will be done accordingly. To perform this step, simply run:

zairachem setup

Most data generated in the setup step will be stored in model/data (fit) or test/data (predict). The most important file in this folder is data.csv, containg the result of the data preparation step. Other files are generated, like mapping.csv, which match data.csv to the row indices of the input file.

Describe

In the describe step, small molecule descriptors are calculated. ZairaChem provides a set of default descriptors, including the Chemical Checker signaturizer, Grover embeddings and Morgan fingerprints and Mordred descriptors.

Other descriptors can be easily incorporated thanks to the Ersilia Model Hub. They can be specified in a parameters.json file.

Several operations are performed for each of the descriptors, including:

• Calculation of descriptors for each molecule using the Ersilia Model Hub.

• Removal of constant-value columns and columns with a high degree of missing values.

• Imputation of the rest missing values.

• Robust scaling of contiuous descriptors.

In addition, a reference descriptor is calculated (Grover). To this reference descriptors, the following dimentionality reduction techniques are applied:

• UMAP

• PCA

Optionally, supervised versions of thesealgorithms are applied:

• Supervised UMAP

• LolP

All of the above can be performed by running the following command:

zairachem describe

Please note that calculating some descriptors (for example, GROVER) may be a slow procedure. However, the Ersilia backend is linked to an in-house caching library called Isaura that is able to access pre-calculated data. At the moment, Isaura works on local caching. However, we are currently setting up a cloud-based database in order to facilite access to pre-calculations stored online.

Estimate

This step is aimed at training AutoML models based on the descriptors calculated above.

The following supervised models are applied:

• Baseline LazyQSAR models (based on Morgan fingerprints and classic descriptors).

• FLAML models on each of the pre-calculated descriptors.

• AutoGluon model based on the manifolds of the reference embedding.

• Keras Tuner fully-connected network based on the reference embedding.

• MolMap convolutional neural network.

All of these steps can be performed with the following command.

zairachem estimate

Pool

In the pooling step, results from the estimators above are aggregated. A weighted average is applied, based on the expected performance of each of the individual estimators.

Pooling can be performed with the following command:

zairachem pool

Report

ZairaChem provides automated performance reports as well as a output table.

• Output table

• Performance table

• Plots

zairachem report

Distill

ZairaChem models are computationally demanding. At the end of the procedure, our goal is to provide a distilled model. This distilledm model is stored in an interoperable format (ONNX) and can be deployed as an AWS lambda. The Ersilia package for creating distilled models is called Olinda.

Finish

The finish command simply offers options for cleaning

ersilia finish

How to run a step of interest

It is possible to run a specific step from a previous session. In this case, simply initialize the session pointing to the relevant folders:

zairachem session --path model

ZairaChem will automatically identify the session as training (fit) task or as a prediction task.

Once the session has been set, you can run the command of choice. For example:

zairachem describe

The session file

In the session file, multiple steps are specified. Each step in ZairaChem has an associated name. You can restart the pipeline at any given step.