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 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 BioPharmaceuticals R&D 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, Computational Chemistry, Discovery Sciences, R&D

Key Achievements









Computational prediction of chemical reactions: current status and outlook.永利皇冠游戏网站

Drug Discovery Today. 2018; 23(6): 1203-1218. Engkvist O, Norrby P-O, Selmi N et al. Publication link:

The rise of deep learning in drug discovery.永利皇冠游戏网站

 Drug Discovery Today. 2018; 23(6): 1241-1250. Chen H, Engkvist O, Wang Y, et al. Publication link:

Molecular de-novo design through deep reinforcement learning.永利皇冠游戏网站

Journal of Cheminformatics. 2017; 9(48). Olivecrona M, Blaschke T, Engkvist O, Chen H. Publication link:

Application of Generative Autoencoder in De Novo Molecular Design.永利皇冠游戏网站

Molecular Informatics. 2018; 37(1-2): 1700123. Blaschke T, Olivecrona M, Engkvist O et al. Publication link:

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry.永利皇冠游戏网站

Molecular Informatics. 2016; 35(11-12): 615-621, Tetko I.V., Engkvist O, Koch U et al. Publication link: