Installation

This document outlines the essential requirements for successfully installing and running ADEL. ADEL (Active Deviation Ensemble Learning) integrates multiple deep learning and machine learning models, active deviation ensemble strategies, and large-scale virtual screening optimization methods, tailored for ultra-large-scale drug discovery applications.

System Requirements

To efficiently run ADEL, your system should meet the following specifications:

  • CPU: Multi-core processor recommended for parallel processing tasks

  • RAM: Minimum 16GB, 32GB or more recommended for handling large datasets

  • GPU: CUDA-compatible GPU (optional, but recommended for deep learning models)

  • CUDA: Version 11.3 or higher (required for GPU acceleration)

  • Disk Space: At least 10GB for software and its dependencies

Python Environment Setup

ADEL requires Python 3.8. It is strongly recommended to use Conda to manage your environment:

# Create a new conda environment
conda create -n adel_env python=3.8

# Activate the environment
conda activate adel_env

Core Dependency Installation

The following core dependencies must be installed in the specified order:

1. RDKit

RDKit is essential for molecular structure processing and generating molecular descriptors:

conda install rdkit -c conda-forge

2. PyTorch

PyaiVS uses PyTorch 1.12.1 for deep learning models:

pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --torchaudio==0.12.1

3. Deep Graph Library (DGL)

DGL is required for graph-based models:

pip install dgllife

4. Additional Required Packages

Install these extra packages required by different ADEL components:

conda install xgboost hyperopt pandas scikit-learn numpy
pip install requests

These dependencies support various machine learning algorithms used in the package:

Model Type

Required Packages

Machine Learning

scikit-learn, xgboost

Deep Learning

pytorch, dgl

Hyperparameter Opt.

hyperopt

Data Processing

pandas, numpy

Troubleshooting

CUDA Compatibility Issues

If you encounter CUDA-related errors:

  • Use nvidia-smi to verify your CUDA version

  • Ensure the correct CUDA version of PyTorch is installed

  • Set the appropriate environment variables:

    import os
os.environ['PYTHONHASHSEED'] = str(42)
os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"

Memory Issues

If you run into memory errors when handling large datasets or complex models:

  • Reduce batch size in model configuration

  • Use CPU mode if GPU memory is limited

  • Process datasets in chunks whenever possible

Example of specifying CPU device:

python /home/models/ml_screen.py --file /home/database/databae.csv --cpus 10 --out_dir /home/ --models /home/small/model_save/iteration_1/SVM/random_reg_ECFP4_1_SVM_bestModel.pkl

Package Dependency Conflicts

If you face dependency conflicts:

  • Create a new Conda environment

  • Install dependencies in the exact order listed above

  • Avoid mixing conda and pip installs for the same package

Next Steps

After installation, refer to the Tutorial for your first virtual screening task using ADEL.