RESPOND3 – Responsible early digital drug discovery
Drug discovery is a long marathon and our athletes need all the help they can get. This is a challenge we, in RESPOND3, want to answer to through the development of better computational methods. This is a highly interdisciplinary Digital Life Norway project that strives to replicate the dynamic of an early drug discovery project – a bold project for academia to venture in if we have in mind that the development of a new drug takes a long time and millions upon millions of dollars – but let us not get intimidated by that! The current pandemic has slowed down our pace, but now we are moving forward with monthly meetings (digital of course) to share and discuss our collective scientific efforts.
The RESPOND3 research group at a Zoom meeting. The effort is driven by 3 groups at the University of Bergen (UiB) (Nathalie Reuter, Bengt Erik Haug, Ruth Brenk), one group at Western Norway University of Applied Sciences (HVL) (Alexander Lundervold) and another one at University of Copenhagen (Helen Yu).
The RESPOND3 project sits on 4 work packages: 1) Computational drug-target affinity prediction, 2) Medicinal Chemistry, 3) Compound Assays and Structural Biology, and 4) Responsible Research and Innovation. This linear enumeration does not make justice to their interdependence. Packages 1), 2) and 3) are the three pillars of the modern structure-based drug design cycle; they inform each other iteratively: computational predictions of binding affinity allow to prioritize compounds to be synthesized which in turn can be tested for biological activity. What about the work package 4)? It is the medium in which the others are embedded, since responsible research and transparency is of utmost importance for a research project that balances the interests of different stakeholders: researchers, private investors, people in need of the drug, government, society. And the stakes are high for everyone involved when millions are invested, thousands of hours of labor are put forward, and the outcome is uncertain.
The research environment of the project provides the perfect playground to develop better computational tools to aid drug discovery. The reason is that the methods can not only be tested retrospectively, but also prospectively within the project, for example in aiding the development of therapeutics for Chronic Obstructive Pulmonary Disease (COPD).
This live feedback is key for the computational tools developers to bring about a tool that is useful for the medicinal chemists. Thanks to the growing amount of relevant drug discovery data available in public databases the use of modern machine learning methods is being explored to develop approaches that can further streamline drug discovery. The use of machine learning presents the potential to curb the computational cost versus accuracy trade-off that classical methods are known for.