A Data-Driven Perspective on Drug Discovery | HBKU
Hamad Bin Khalifa University


A Data-Driven Perspective on Drug Discovery

A Data-Driven Perspective on Drug Discovery

The COVID-19 pandemic has ushered in a revolution in the process of developing a preventive vaccine or therapeutic prescription drugs for the virus. As of mid-April 2020, there are over 200 pharmaceutical companies, university research teams, and health organizations testing over 100 vaccine candidates and 120 potential drugs in various stages of clinical/preclinical development.

In this webinar, experts from Qatar Computing Research Institute (QCRI) will present the pros and cons of some of the treatment directions currently being pursued,  and will showcase a set of quantitative measures widely utilized to measure a drug’s effectiveness.

The webinar will also illustrate various modes of biological information available for the SARS-CoV-2 virus. Similarly, experts will highlight available resources on drug structure, representations, targets, and mechanism of actions.

The second part of this webinar will present a mathematical framework design for the drug repurposing (synergy) task with data-driven machine learning solutions to identify potential drug candidates for SARS-CoV-2.

Speakers Bios:

Raghvendra Mall is a research scientist at QCRI, and works on designing data-driven models for computational biology with a primary focus on network biology and structural bioinformatics. 

Ehsan Ullah is a software engineer in the health group at QCRI. His background is in electrical engineering and computer science.  Ullah’s research interests focus on the application of statistical and machine learning methods and approaches in medical sciences.

Stefano Giovanni Rizzo is a postdoctoral researcher at QCRI  working on deep learning research problems in the areas of predictive analytics, urban mobility, and anomaly detection. He has been working on machine learning projects since obtaining his master’s degree.

This lecture will be held in English

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