Escaping the Combinatorial Explosion: Expert-Enhanced Heuristic Navigation of Chemical Space (eHACS)

Computational molecular design is vital for identifying functional molecules to address critical societal challenges, such as the accelerating antibiotic resistance and our demanding combat of cancer. This quest, however, faces a combinatorial explosion in the number of potential drug candidates – the chemical space made up of all small drug-like molecules – which is unfathomably large (estimated at 10⁶⁰). Testing any significant fraction of these molecules, either computationally or experimentally, is simply impossible. Thus, heuristic optimization methods, or automated ‘de novo’ design, is the long-standing Holy Grail of drug discovery. Unfortunately, these methods have so far failed to deliver on their promise and must be complemented by inefficient and slow post-processing involving expert chemists. We anticipate that a new, knowledge-guided molecular design, integrating the expert early on, will help focus and steer the overall optimization and drastically reduce the need for manual post-processing. To unlock these possibilities, knowledge-based expert guidance, modern molecular design, and empowering machine learning must be integrated. To research and pursue this new approach, we have assembled an interdisciplinary team of leading scientists from chemistry, biomedicine, and data science (visual data science as well as machine learning). The eHACS project includes research on new, enabling technologies (data-science-empowered solutions) as well as their deliberate application to overcome challenges in drug discovery and to design potent inhibitors of targets for cancer and antibiotics.

The project is led by Vidar Jensen, Deparment of Chemistry. Ruth Brenk will lead a workpacke in this project.