Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage

16:00 - 18:00
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Emre Sahin
Mr. Emre Sahin
High Performance Software Engineer, STFC Hartree Centre, UK

Emre is a High-Performance Software Engineer at the Hartree Centre, home to some of the most advanced computing, data and AI technologies in the UK. He has experience of applied HPC solutions to a range of diversified research fields. As a member of the Hartree National Centre for Digital Innovation (HNCDI), Emre is working in partnership with IBM towards application of Quantum Machine Learning methodologies in early stages of material science, drug discovery and computational pathology. He also worked on projects focusing on tackling compute and memory intensive problems and developing novel scalable stochastic and hybrid mathematical methods and algorithms such as scalable hybrid Monte Carlo algorithms for variety of supercomputing and accelerator architectures for large-scale linear algebra, optimization, computational finance, environmental models, computational biology. He holds a BSc. (Hons.) of Electric-Electronic Engineering degree from Izmir Institute of Technology. He also has experience in developing and applying Machine Learning and A.I. algorithms for several platforms such as drone technologies and embedded systems for the automotive industry.


Machine Learning (ML) for Ligand Based Virtual Screening (LB-VS) is an important in-silico tool for discovering new drugs in a faster and cost-effective manner, especially for emerging diseases. We propose a general-purpose framework combining a classical Support Vector Classifier (SVC) algorithm with quantum kernel estimation for LB-VS on real-world databases, and we argue in favor of its prospective quantum advantage. We heuristically prove that our quantum integrated framework can, at least in some relevant instances, provide a tangible advantage compared to the state-of-art classical algorithms operating on the same dataset, showing strong dependence on target and features selection method.

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