DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets.

2022-11-24

Raies, Arwa

Tulodziecka, Ewa

Stainer, James

Middleton, Lawrence

Dhindsa, Ryan S

Hill, Pamela

Engkvist, Ola

Harper, Andrew R

Petrovski, Slavé

Vitsios, Dimitrios

Journal Article

0000-0003-3952-7363

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0000-0002-8965-0813

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0000-0003-4970-6461

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0000-0002-8939-5445

10.1038/s42003-022-04245-4

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.

link

Communications Biology 2022; 5(1)

Communications Biology

2399-3642