Deep learning large-scale drug discovery and repurposing

Authors

• Weiming Li

Overview

Introduction

It is rather challenging to do large-scale drug discovery and repurposing. Identifying mechanism of action (MOA) of active substances is crucial yet current approaches like omics-based methods are costly and low-throughput, while structure-based predictions often have unacceptably high false-positive rates. Here, we present a novel approach for MOA identification based on high-throughput longitudinal profiling of mitochondrial phenotypes to expedite large-scale drug repurposing, target identification, and the discovery of new therapeutic interventions. By investigating the effects of drugs on mitochondrial morphology and membrane potential, we established a pipeline for the acquisition of time-resolved mitochondrial images, resulting in a dataset that comprised a total of 570,096 single-cell images from exposures to 1,068 FDA-approved drugs. We then developed a deep learning model named MitoReID with re-identification (ReID) framework and an I3D ResNet backbone. The model achieved 76.32% Rank-1 and 65.92% mean Average Precision (mAP) on the testing set and successfully identified the MOAs for 6 novel drugs based on the temporal characteristics of mitochondrial phenotypes. Furthermore, MitoReID identified COX-2 inhibition as the MOA of the natural tea compound epicatechin, which was confirmed by in vitro validations. Our approach thus provides an automated and cost-effective alternative for lead identification that could accelerate large-scale drug development and target discovery.

Pipeline of MitoReID

Model performance

Below are the CMC and ROC curves for validation, along with the T-SNE plot illustrating the learned features extracted from the query set (denoted by points) and the gallery set (denoted by stars). These features were extracted from MitoReID, trained on 38 MOA classes.

Methods comparison in identifying 38 MOAs based on temporal mitochondrial phenotypes

Methods	Accuracy/%	AUC/%	Rank-1/%	Rank-5/%	Rank-10/%	mAP/%
IPP-based	15.72	75.32	-	-	-	-
IDE	32.46	82.96	23.68	36.84	49.12	9.06
MitoReID (Ours)	82.46	97.83	76.32	84.21	84.21	65.92

Ablation studies of MitoReID

Methods	small (ResNet-18)	small (ResNet-18)	base (ResNet-50)	base (ResNet-50)
	Rank-1/%	mAP/%	Rank-1/%	mAP/%
MitoReID	75.44	58.35	76.32	65.92
-- pretrain	37.72 (-37.72)	16.13 (-42.22)	39.47 (-36.85)	22.29 (-43.63)
-- retain temporal dimension	31.58 (-6.14)	12.77 (-3.36)	32.46 (-7.01)	14.95 (-7.34)
-- temporal dimension	3.51 (-28.07)	2.73 (-10.04)	4.39 (-28.07)	2.77 (-12.18)

System Requirements

Hardware requirements

This project requires a computer with at least 16GB GPU memory for training and 3GB GPU memory for inference. All the models present in the paper were trained with Nvidia V100 (32 GB version).

Software requirements

OS Requirements

This project is supported for all OS. The package has been tested on the following systems:

• Windows: Windows 10
• Linux: Ubuntu 18.04

Python Dependencies

Install pytorch GPU version, you can use other versions besides the following example:

pip install torch==1.10.1+cu102 torchvision==0.11.2+cu102 -f https://download.pytorch.org/whl/torch_stable.html

Install other packages listed in requirements.txt. Python version has been tested is 3.8.

pip install -r requirements.txt

The overall installation will take about half hour.

Instructions

Jupyter notebook

We provide step-by-step jupyter notebook ./run.ipynb for beginners. If you are more familiar with command line, you can move forward.

Command line instructions

1. cd to folder where you want to download this repo.

2. Run git clone https://github.com/liweim/MitoReID.git

3. Download the Mito dataset in zip format from Google drive and unzip it.

   python utils/download.py --file_name Mito.zip --save_folder ./data
   

   The dataset's folder structure is as follows. Notably, l1, l2, and l3 represent three replicated experiments, each sharing an identical folder structure.

   - l1: experiment 1
   -- train: data for training.
   -- query: data for evaluation, query and gallery are used together to evaluate.
   -- gallery: data for evaluation, query and gallery are used together to evaluate.
   -- nc: natural compounds.
   - l2: experiment 2
   -- (same as l1)
   - l3: experiment 3
   -- (same as l1)
   - novel: unseen novel FDA-approved drugs.
   - annotation.xlsx: annotations for the dataset. There are two sheets in the excel: 'FDA-list' for FDA approved drugs, 'Novel-list' for novel compounds.
   

4. (Optional) Prepare your own dataset.

   Create a directory to store the raw images. The images of one drug should be stored in one folder and follow this naming conventions:

   {drug_id}/{drug_id}_{time}_{w} (For example: 5/5_16_w2)
   

   Run the following script to split cells from the raw images.

   python utils/generate_dataset.py --folder /path/to/your/dataset/folder --save_folder /path/to/your/save/folder
   

   Change the path settings in configs/mitoreid.yml. Compute the average mean and std of the dataset by running the following script and change the mean and std settings in configs/mitoreid.yml.

   python utils/get_mean_std.py --folder /path/to/your/save/folder
   

5. Use the trained model to test on the query set. Configurations can be refered to configs/test.yml. Parameters explanation of the configurations can be found in main.py. Evaluation metrics are accuracy, rank-k and mAP. The model weight mitoreid-resnet-50-pretrain will be downloaded automatically for testing and predicting. All the model weight you can download are listed below. They all trained with I3D ResNet network.

   Note: The standard I3D ResNet network downscales the temporal dimension throughout all stages while the modified I3D ResNet network only downscales the temporal dimension in the first two stages.

• resnet-18-kinetics: weight trained with standard I3D ResNet-18 network on Kinetics-400 dataset.

• resnet-50-kinetics: weight trained with standard I3D ResNet-50 network on Kinetics-400 dataset.

• resnet-18-mito: weight trained with modified I3D ResNet-18 network on Mito dataset using self-supervised pretraining technique.

• resnet-50-mito: weight trained with modified I3D ResNet-50 network on Mito dataset using self-supervised pretraining technique.

• ide: weight trained with IDE framework with modified I3D ResNet-18 network on Mito dataset, pretrained from resnet-18-kinetics.

• mitoreid-resnet-18: weight trained with MitoReID framework with modified I3D ResNet-18 network on Mito dataset, pretrained from resnet-18-kinetics.

• mitoreid-resnet-50: weight trained with MitoReID framework with modified I3D ResNet-50 network on Mito dataset, pretrained from resnet-50-kinetics.

• mitoreid-resnet-18-standard: weight trained with MitoReID framework with standard I3D ResNet-18 network on Mito dataset, pretrained from resnet-18-kinetics.

• mitoreid-resnet-50-standard: weight trained with MitoReID framework with standard I3D ResNet-50 network on Mito dataset, pretrained from resnet-50-kinetics.

• mitoreid-resnet-18-image: weight trained with MitoReID framework with standard I3D ResNet-18 network on Mito dataset (image only), pretrained from resnet-18-kinetics.

• mitoreid-resnet-50-image: weight trained with MitoReID framework with standard I3D ResNet-50 network on Mito dataset (image only), pretrained from resnet-50-kinetics.

• mitoreid-resnet-18-pretrain: weight trained with MitoReID framework with modified I3D ResNet-18 network on Mito dataset, pretrained from resnet-18-mito.

• mitoreid-resnet-50-pretrain: weight trained with MitoReID framework with modified I3D ResNet-50 network on Mito dataset, pretrained from resnet-50-mito.

  Note: the model weights provided are trained with multiple GPUs. I have tried my laptop with single GPU and not work, in this case, you can set the device_ids to 'cpu' in configs/test.yml.

  python main.py --config configs/test.yml --task test
  

6. Use the trained model to predict the natural compounds' MOAs. Configurations can be refered to configs/predict.yml. You are expected to get the predicted MOA id (pred), score (rank1 and cosine_distance) and the name of the MOA ( pred_name).

   python main.py --config configs/predict.yml --task predict
   

7. Fine-tune your model based on the self-supervised pretrained weight. Configurations can be refered to configs/fine-tune.yml. The pretrained weight resnet-50-mito will be downloaded automatically. Train on V100 will take about 1 hour. Wandb is used to visualize the training process, register the wandb account in https://wandb.ai/site if you don't have. If you get GPU out-of-memory error, set the bs to a lower value. If you get CPU out-of-memory error, set the num_workers to a lower value.

   python main.py --config configs/fine-tune.yml --task train
   

8. Pretrain your model using self-supervised technique. Configurations can be refered to configs/pretrain.yml. The pretraining is based on the weight trained on Kinetics-400 dataset, which is benchmark for video classification. The pretrained weight resnet-50-kinetics will be downloaded automatically. Train on V100 will take about 4 hours.

   Note: Train from scratch is not recommended considering the difficulty of image sequences classification. Too much training may lead to overfitting, don't set too large epoch in the config.

   python main.py --config configs/pretrain.yml --task train
   

9. Train your model from scratch base on the weight trained on Kinetics-400 dataset. Configurations can be refered to configs/train.yml. The pretrained weight resnet-50-kinetics will be downloaded automatically. Train on V100 will take about 5 hours.

   python main.py --config configs/train.yml --task train