Deep Learning for drug discovery: applying deep adversarial networks for new molecule development
Neural networks and other machine learning models have recently been applied to many biological problems, including drug discovery. Applications of deep neural networks combined with domain expertise can help design de novo druglike compounds and generate large virtual chemical libraries, which can be more efficiently screened for in silico drug discovery purposes. This presentation will describe aspects of applications of deep adversarial networks and reinforcement learning for molecular de novo design. It will also briefly cover the Insilico Medicine drug discovery pipeline and how machine learning can be applied on each step.
Polina Mamoshina is a senior research scientist at Insilico Medicine, Inc, a Baltimore-based bioinformatics and deep learning company focused on reinventing drug discovery and biomarker development and a part of the computational biology team of Oxford University Computer Science Department. Polina graduated from the Department of Genetics of the Moscow State University. She was one of the winners of GeneHack a Russian nationwide 48-hour hackathon on bioinformatics at the Moscow Institute of Physics and Technology attended by hundreds of young bioinformaticians. Polina is involved in multiple deep learning projects at the Pharmaceutical Artificial Intelligence division of Insilico Medicine working on the drug discovery engine and developing biochemistry, transcriptome, and cell-free nucleic acid-based biomarkers of aging and disease. She recently co-authored seven academic papers in peer-reviewed journals.