Postdoctoral Fellow: Machine Learning Methods for RNA drug discovery

Do you have expertise in, or passion for, Machine Learning, Computational Structural Biology and Drug Discovery? Would you like to apply your knowledge to impact RNA-targeted therapies’ development in a company that follows the science and turns ideas into life-changing medicines? Then this postdoc position at AstraZeneca might be the one for you!
About AstraZeneca:
AstraZeneca is a global, science-led, patient-centred biopharmaceutical company focusing on discovering, developing, and commercialising prescription medicines for some of the world’s most serious diseases. But we’re more than a global leading pharmaceutical company. At AstraZeneca, we‘re dedicated to being a Great Place to Work and empowering employees to push the boundaries of science and fuel their entrepreneurial spirit. There’s no better place to make a difference in medicine, patients, and society.
About the Postdoc Programme:
Bring your expertise, apply your knowledge, follow the science, and make a difference. AstraZeneca’s Postdoc Programme is for self-motivated individuals looking to deliver exciting, high-impact projects in a collaborative, engaging and innovative environment. You will work with multidisciplinary scientific teams from a diverse set of backgrounds and a world-class academic mentor specifically aligned to your project. Our postdocs are respected as specialists, encouraged to speak up, and supported to share their research at conferences, publish papers, achieve their goals and make a difference to our patients.
This is a 3-year programme, with an initial 2-year period and a 1-year merit - based extension.
About the Opportunity:
AstraZeneca’s Early Respiratory & Immunology (Early R&I) therapy area is centred on delivering life-changing products that advance world health and help fight and cure disease. Early R&I is one of three main therapeutic research areas within AstraZeneca that provide candidate drugs for late-stage clinical development. Here, you will have the chance to make a real difference in people’s lives.
We are looking for a Postdoctoral Fellow who will develop and apply innovative approaches based on machine learning and physics-based methods to RNA structure prediction, identification of RNA druggable sites and small-molecule binding.
As a Postdoc, you will be focusing on your research project and conducting innovative and creative science leading to the publication of outstanding papers.
This will include data collection and annotation for ligand-RNA/DNA and protein-RNA/DNA interactions from a structural perspective. Depending on your background, you will take time to become familiar with basic knowledge of molecular biology, protein and RNA structure and their interaction with ligands and current ML methods for structure prediction and scoring of both protein and RNA. As well as with current ML methods for protein/ligand and RNA/DNA ligand interactions.
You will be applying these methods to a set of microRNAs of relevance for Early R&I. You will develop ML methods to predict RNA structures with druggable structural motifs as well as develop an ML method to identify ligand binding pockets and poses. Due to the paucity of available datasets, you will use the larger dataset of available protein-ligand, RNA-protein and DNA-protein poses and transfers learning techniques to train an ML method for predicting putative druggable sites and pockets in RNA.
You will be supported by Leonardo De Maria (Principal Scientist of Computational Chemistry) at AstraZeneca and receive academic guidance and mentorship from one of the world's leading academic experts in RNA structure predictions.
Qualification, Skills & Experience:
Essential Requirements
PhD in Computational Biology, Computational Chemistry, or a relevant field
Experience in computational modelling of biomolecule structure and dynamics (proteins, DNA or RNA)
Python programming expertise and experience with Unix and high-performance computing environments
Knowledge of machine learning and deep learning techniques



Desirable Requirements
Experience in structure-based drug design for protein, DNA or RNA targets
Knowledge of RNA structure
Basic bio-informatics knowledge and capabilities: where to find/how to interpret sequence information and how to work with it, such as sequence alignments



Why Should You Apply?
RNA-based therapeutics is emerging as a new and very active research field. As a drug target, RNA poses several new and complex challenges. RNA structures display, in general, greater plasticity than proteins. Compared with protein-ligand binding sites those on RNA can be less deep, more polar, solvated, and conformationally flexible. Machine Learning applications in drug discovery and development have seen exponential growth and are now impacting running drug development projects, with AstraZeneca both ground breaking and leading in the area. You will get to work on two hot research topics: the application of ML to drug discovery and the development of novel RNA-based therapies.
As a member of the Early R&I Computational Chemistry team, you will be part of the Early R&I Medicinal Chemistry Department, in day-to-day contact with a large team of medicinal chemists.
You will be based at our Gothenburg in-silico Center of Excellence, where dozens of computational chemists and machine learning specialists co-locate, support each other, and exchange ideas.
This postdoc position will allow you to pursue exciting, fundamental research that will have direct therapeutic applications in a vibrant research site, tight collaboration with internal and external partners, and publish in high-quality journals.
Are you already imagining yourself joining our team? Good, because we can’t wait to hear from you!