| Title: | DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets |
| Journal: | Communications Biology |
| Published: | 24 Nov 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36434048/ |
| DOI: | https://doi.org/10.1038/s42003-022-04245-4 |
| URL: | https://www.nature.com/articles/s42003-022-04245-4.pdf |
| Citations: | 11 (11 in last 2 years) as of 8 Aug 2024 |
Publication 10720
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Abstract
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.</p>
10 Authors
- Arwa Raies
- Ewa Tulodziecka
- James Stainer
- Lawrence Middleton
- Ryan S. Dhindsa
- Pamela Hill
- Ola Engkvist
- Andrew R. Harper
- Slavé Petrovski
- Dimitrios Vitsios
Enabling scientific discoveries that improve human health