Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors

This repository is the official implementation of paper Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors .

This paper introduces a simple yet effective strategy to boost the predictive power of QSAR deep learning models. The strategy proposes to train GNNs together with traditional descriptors, combining the strengths of both methods.

Acquire the Datasets

This repository does NOT include the datasets used in the experiment. Please download the datasets from this link

These are well-curated realistic datasets that removes false positves for a diverse important drug targets. The datasets also feature in its high imbalance nature (much more inactive molecules than active ones). Original papers of the datasets: see references [1,2].

Introduction of the Datasets

High-throughput screening (HTS) is the use of automated equipment to rapidly screen thousands to millions of molecules for the biological activity of interest in the early drug discovery process. However, this brute-force approach has low hit rates, typically around 0.05%-0.5%. Meanwhile, PubChem is a database supported by the National Institute of Health (NIH) that contains biological activities for millions of drug-like molecules, often from HTS experiments. However, the raw primary screening data from the PubChem have a high false positive rate. A series of secondary experimental screens on putative actives is used to remove these. While all relevant screens are linked, the datasets of molecules are often not curated to list all inactive molecules from the primary HTS and only confirmed actives after secondary screening. Thus, we identified nine high-quality HTS experiments in PubChem covering all important target protein classes for drug discovery. We carefully curated these datasets to have lists of inactive and confirmed active molecules.

Statistics of the Datasets, specified by PubChem Assay ID (AID)

Process the Datasets

Uncompress the downloaded file and you will see several .sdf files.

Use the scripts in BCL folder to process them into BCL features. The script will create a new folder named BCL-feats and store BCL features in ({dataset}-BCL-feat.csv) in that folder. See README.md in the BCL folder for the detailed instruction.

Run the Codes

First, set the corresponding configuration file (.e., .cfg) in the config folder. For example, if you want to run GCN, then you should change GCN.cfg

The main.py is the script that you want to run. There is one required flag --config that specifies the configuration file. Two optional flags are --no_train_eval and --test.

--no_train_eval means no metric evaluation for each training epoch. This greatly speeds up the training. --test will skip the training and directly load a trained model saved in the saved_model folder.

Example command:

python main --config config/GCN.cfg --no_train_eval --test

Q&A

Feel free to drop questions in the Issues tab, or contact me at yunchao.liu@vanderbilt.edu

References

[1] Butkiewicz, Mariusz, et al. "Benchmarking ligand-based virtual High-Throughput Screening with the PubChem database." Molecules 18.1 (2013): 735-756.)

[2] Butkiewicz, Mariusz, et al. "High-throughput screening assay datasets from the pubchem database." Chemical informatics (Wilmington, Del.) 3.1 (2017).