Accelerating ageing research and drug discovery using advances in deep learning
One of the major areas of biomedicine, where vast amount of data is available and even marginal advances may result in billions of Quality Adjusted Life Years (QALY) is ageing research. Most of the deadly diseases are age-related and share many common pathways with “normal” ageing. Many of these pathways are actionable and may be targeted to prevent the onset of or treat age-related diseases.
In this talk we will discuss the current trends in ageing research in the context of the pharmaceutical industry and present new results in developing aging biomarkers using blood biochemistry (www.Aging.AI) and transcriptomic data and demonstrate the applications of deep learning to drug discovery and drug repurposing using large data sets of transcriptional response data and pharmacological properties of small molecules.
Recent publications and projects:
1. Aliper, et al. (2016). Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Molecular pharmaceutics. In print, ACS editors’ choice award
2. Putin, et al. (2016). Deep biomarkers of human aging: Application of deep neural networks to biomarker development. Aging 8, no. 5 1-021.
3. Mamoshina, et al, (2016). Applications of deep learning in biomedicine. Molecular pharmaceutics, 13(5), 1445-1454.
Recent project in regenerative medicine: www.Embryonic.AI