What is your research about?
My research at Aalto University and The Finnish Centre of Artificial Intelligence (FCAI) is focused on designing principled AI & quantum machine learning algorithms that address some of the most complex challenges that we are facing today in areas such as healthcare, energy, material discovery, telecom and IoT.
For instance, launching new drugs can take a decade, cost up to two billion dollars and only a miniscule number of these efforts ever make it past the clinical trials. To put it simply, drugs are molecules that target specific proteins or nucleic acids to generate a desired therapeutic response in the patient. One of the main challenges is to identify and design the most promising molecules and protein targets, and to mitigate potential side effects, which are caused by the molecules attaching to unintended proteins.
A mere brute-force approach is ruled out because the number of drug-like molecules is just too high. My group develops highly effective deep learning models for different components of the drug discovery pipeline, including multi-omics analysis, protein design, molecule generation and optimization, assay imputation, and retrosynthesis.
Likewise, for certain types of problems, quantum computing provides significant benefits compared to their classical counterparts. We are exploring ways to exploit the huge potential of quantum algorithms and quantum-inspired methods for some critical applications, especially in the context of combinatorial optimization and the so-called hybrid settings where a classical computing resource works together with a quantum processing unit or accelerator. We also have ongoing projects and collaborations on quantum circuit simulation and optimization.
How would you describe the impact of your research?
My research is motivated by complex real-world problems that necessitate rigorous scientific inquiry, and often leads to new algorithms, insights, and models that bring about significant practical impact. For instance, during my time as a PhD student at the Massachusetts Institute of Technology (MIT), we introduced a principled deep learning-based generative method for de novo protein design or inverse folding problem.
Our method turned out to be more accurate and about 20,000 times faster than the then state-of-the-art method Rosetta fixbb, thereby enabling rapid biomolecule engineering. We also investigated the capabilities of graph neural networks – one of the most prominent topics in machine learning currently – and our insights inspired, in part, novel algorithms and architectures that found much use across applications from diverse domains.
Following up on this line of work here at Aalto and FCAI – in our project HEALED funded by the Academy of Finland - we’ve been working on pushing the frontiers of drug discovery by combining the best of AI models and human experts: we enable the human experts to steer the design process interactively, thereby mitigating the vulnerability of AI to biases inherent in typical datasets, and thus affording better generalization. We’ve also developed new deep graph networks for temporal settings that are provably more powerful than the state of the art.
