After I completed my PhD in Computational Chemistry at the University of Lund and postdoctoral research at the University of Cambridge and the Czech Academy of Sciences, I joined AstraZeneca in 2004. I currently lead the Discovery Sciences Computational Chemistry team within the IMED Biotech Unit, providing computational solutions for drug discovery.

I am passionate about pushing the boundaries of using artificial intelligence and machine learning in drug discovery. A key focus for me has been on building both the team within IMED and collaborating with external experts to advance innovation in drug design and synthesis.

Through a pioneering collaboration with the University of Muenster, my team demonstrated the first application of recurrent Neural Networks to molecular design which has been published in two recent, highly-cited articles. This methodology allows us to design novel drug molecules using machine learning to navigate the breadth of chemical space and to exploit our vast knowledge base.


I am fascinated by applying the latest artificial intelligence and machine learning technologies to drug discovery. It has the potential, together with further progress in automation, to transform the drug discovery process.

Ola Engkvist Associate Director, Discovery Sciences Computational Chemistry, IMED Biotech Unit

Key Achievements

Associate Director, Discovery Sciences Computational Chemistry, IMED Biotech Unit

2018

Key speaker on Artificial Intelligence in drug discovery at ELRIG Drug Discovery 2018

2018

Key speaker at RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry

2009

Appointed to lead the Discovery Sciences Computational Chemistry team within the IMED Biotech Unit

  Featured publications

Computational prediction of chemical reactions: current status and outlook.

Drug Discovery Today. 2018; 23(6): 1203-1218. Engkvist O, Norrby P-O, Selmi N et al. Publication link: https://www.sciencedirect.com/science/article/pii/S1359644617305068

The rise of deep learning in drug discovery.

 Drug Discovery Today. 2018; 23(6): 1241-1250. Chen H, Engkvist O, Wang Y, et al. Publication link: https://www.sciencedirect.com/science/article/pii/S1359644617303598

Molecular de-novo design through deep reinforcement learning.

Journal of Cheminformatics. 2017; 9(48). Olivecrona M, Blaschke T, Engkvist O, Chen H. Publication link: https://jcheminf.biomedcentral.com/articles/10.1186/s13321-017-0235-x

Application of Generative Autoencoder in De Novo Molecular Design.

Molecular Informatics. 2018; 37(1-2): 1700123. Blaschke T, Olivecrona M, Engkvist O et al. Publication link: https://onlinelibrary.wiley.com/doi/full/10.1002/minf.201700123

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry.

Molecular Informatics. 2016; 35(11-12): 615-621, Tetko I.V., Engkvist O, Koch U et al. Publication link: https://onlinelibrary.wiley.com/doi/full/10.1002/minf.201600073