Innovative quantum chemical software accelerates drug research
Design and synthesis of new materials can accelerate scientific discovery in different fields. New nanoparticles can help increase catalysis efficiency and reduce the carbon footprint of the chemical industry. In the transport field, new battery materials are expected to power the electric revolution, making electric vehicles a global viable option. New dye molecules for solar cells help the renewable energy sector step out of the shadow of fossil energy production. The list can be extended almost indefinitely. Nowhere is the discovery of new molecules more important than in the pharmaceutical industry. “Before a new molecule with drug-like properties is deemed suitable for patients, it has to go through rigorous testing. The research and development journey of a new medicine to move from lab to market takes around 10 years and costs around USD 5 billion,” notes Illés József, coordinator of the EU-funded QCLAB project. The cost of drug development is exorbitant and will keep increasing over time. “The study of specific biological pathways – a series of molecular interactions in a cell that lead to a change in cells – can reveal many clues about complex diseases. Identifying which step of the pathway is affected in each patient leads to a more personalised treatment. As a result, a one-drug-fits-all approach does not hold true. We have to review the pharmaceutical research process so that new tailored drugs will not be impossible to buy or manufacture because of the high research costs,” explains József.
A quantum leap in drug discovery
This is where the QCLAB project can help. Researchers unveiled a novel computational tool called BrianQC: it can reduce the number of candidate molecules, lower the failure rate by cheap simulation, and decrease the number of experiments in the first stage of drug research and development. “In the pharmaceutical research chain, there is a step called high-throughput screening which tests thousands of molecules for their ability to demonstrate the desired biochemical effect on the protein target. From a theoretical side, scientists have to compute many times how drug candidates bond on a very large biomolecule. Using older technology, it is computationally very expensive to do it even once, let alone a million times,” adds József.
Mixing technologies to boost computational efficiency
Project researchers combined different technologies to help realise this virtual high-throughput screening. In particular, the team leveraged neural networks that aid in the prescreening process of candidate molecules, and high-accuracy computational quantum mechanical modelling methods (density functional theory) to investigate the structure of molecules. Less accurate, yet faster approaches just like molecular mechanics (which enable the modelling of large molecular systems) served as a building block for full-stack virtual high-throughput screening. A resounding success, the new quantum chemical software is now part of the daily research operations of one of the most prominent US-based pharmaceutical companies. BrianQC is commercially available and one may request a free trial.
Keywords
QCLAB, BrianQC, quantum chemical software, high-throughput screening, computational tool, drug discovery, pharmaceutical research
