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 BioPharmaceuticals R&D, providing artificial intelligence solutions for drug discovery.

My work revolves around better understanding how we can use machine learning and artificial intelligence (AI) to deliver small molecule clinical candidates faster. Using generative AI, we are now able to explore the chemical space extremely efficiently with accurate predictions of synthetic routes and molecular properties

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 BioPharmaceuticals R&D and collaborating with external experts to advance innovation in drug design and synthesis.

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 Senior Director, Head of Molecular AI, Discovery Sciences, R&D, AstraZeneca

Key Achievements

Associate Director, Computational Chemistry, Discovery Sciences, R&D, AstraZeneca

2021

Professor in Machine Learning and AI for molecular design at Chalmers University of Technology

2021

Trustee at the Cambridge Crystallographic Data Centre

2018

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

  Featured publications

Improving de novo molecular design with curriculum learning

Nature Machine Intelligence. 2022; 4, Guo, J., Fialková, V., Arango, J.D. et al. Publication link: http://www.nature.com/articles/s42256-022-00494-4#citeas

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: http://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: http://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: http://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: http://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: http://onlinelibrary.wiley.com/doi/full/10.1002/minf.201600073

Veeva ID: Z4-57592
Date of preparation: August 2023