DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets.
Arwa Bin RaiesEwa TulodzieckaJames StainerLawrence MiddletonRyan S DhindsaPamela HillOla EngkvistAndrew R HarperSlavé PetrovskiDimitrios Michael VitsiosPublished in: Communications biology (2022)
The druggability of targets is a crucial consideration in drug target selection. Here, we adopt a stochastic semi-supervised ML framework to develop DrugnomeAI, which estimates the druggability likelihood for every protein-coding gene in the human exome. DrugnomeAI integrates gene-level properties from 15 sources resulting in 324 features. The tool generates exome-wide predictions based on labelled sets of known drug targets (median AUC: 0.97), highlighting features from protein-protein interaction networks as top predictors. DrugnomeAI provides generic as well as specialised models stratified by disease type or drug therapeutic modality. The top-ranking DrugnomeAI genes were significantly enriched for genes previously selected for clinical development programs (p value < 1 × 10 -308 ) and for genes achieving genome-wide significance in phenome-wide association studies of 450 K UK Biobank exomes for binary (p value = 1.7 × 10 -5 ) and quantitative traits (p value = 1.6 × 10 -7 ). We accompany our method with a web application ( http://drugnomeai.public.cgr.astrazeneca.com ) to visualise the druggability predictions and the key features that define gene druggability, per disease type and modality.
Keyphrases
- genome wide
- copy number
- dna methylation
- protein protein
- genome wide identification
- machine learning
- small molecule
- healthcare
- adverse drug
- endothelial cells
- mental health
- public health
- drug induced
- gene expression
- artificial intelligence
- bioinformatics analysis
- high resolution
- cross sectional
- drinking water
- big data
- induced pluripotent stem cells
- pluripotent stem cells
- electronic health record