Past Events

Industry Talk: Vertex Pharmaceuticals
Wednesday 13 April 2022, 15:00 - 16:30

Our next Industry Talk will be on 13 April 2022 at 3 pm (UK time) from Vertex Pharmaceuticals discussing working in industry and collaboration.

Registration: free; the link to register is here; deadline 11 April 2022

Title: Adopting a collaborative mindset in industrial drug discovery in the AI era


Ewa Chudyk, Senior Research Scientist, Computational Drug Design, Vertex Pharmaceuticals
Meir Glick, Global Head of Computational Drug Design, Vertex Pharmaceuticals


The discovery of a new drug is a complex and expensive process, involving the collaboration of many interdisciplinary teams of scientists. Along the way, computational chemists can contribute to and accelerate the decision-making process by assessing available data with relevant analysis methods. At various stages of the process, this usually involves partnering with medicinal chemists, DMPK scientists, biologists, toxicologists, and formulation scientists, amongst others. Applying “predict first” solutions involves reaching for both traditional computational chemistry tools as well as novel AI-based methods when working as part of these interdisciplinary teams to identify opportunities of the highest potential impact on the project. Appropriate models are then applied for given hypotheses and assessed after experiments are performed.

In this talk, we will give a general overview of the Vertex Pharmaceuticals drug discovery approach. We will also provide a computational chemist’s perspective based on my own experience in the industry. This will include diverse examples of computational methods used depending on the project’s needs. These consist of mining large datasets, building machine learning-based predictive models, docking virtual compound databases, including quantum chemistry calculations in chemical synthesis planning, running molecular simulations of large protein complexes, or using deep learning models for cell segmentation image recognition, amongst others. We will also share practical considerations of working in the industry, such as project timescales or partnering with interdisciplinary experimental teams. Finally, we will discuss the impact of collaboration, both within the industry and with academia to seek innovative solutions to complex problems.


Ewa Chudyk works as Senior Research Scientist in the Computational Drug Design group at the Oxford site of Vertex Pharmaceuticals since May 2017. Prior to that she worked for almost 3 years for Evotec, a contract research organization company, as part of their research informatics team. In both organizations, her role was concentrated around supporting a variety of early-stage drug discovery projects, working with both internal and external scientists, including medicinal chemists, structure biologists and DMPK scientists. Her education includes postdoctoral training at the Technical University of Munich and a PhD from University of Bristol. Since 2015 Ewa volunteers in the UK chapter of the Molecular Graphics and Modelling Society (MGMS), currently acting as the MGMS Secretary, where her role involves co-organising conferences relevant to its community and encouraging interdisciplinary collaboration.

Meir Glick is the Global Head of Computational Drug Design at Vertex Pharmaceuticals. His team is responsible for rapidly generating testable hypotheses to identify and advance safe and efficacious molecules with fewer and shorter cycle times. Before that he was the Director of Informatics at Merck where his team leveraged data, computational predictions, and emerging technologies to enable discovery teams to focus on innovation, partnership, and creating impact. Before joining Merck in 2015 he was the head of in silico Lead Discovery at the Novartis Institutes for Biomedical Research where he worked since 2003. He was trained as a postdoc in computational chemistry at the University of Oxford and received his Ph.D. in computational chemistry from the Hebrew University of Jerusalem. He is the author of over 60 scientific papers in peer-reviewed journals, patents, and book chapters.