Horizon 2020 SME Instruments

Funding

Revolutionary new quantum chemical software for molecular simulations - No. 767912 - QCLAB

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 767912.

About the project

Drug discovery and development is a very complex and multidisciplinary field connected to biology, chemistry and medical sciences. The process of discovery and development takes a 10-15 years long timespan and huge financial effort to see from start to success. During this period millions of possible drug candidates are examined. It costs several billion EUR and 5 to 10 years to introduce a new drug on average. According to our aiming R&D costs could be reduced thanks to the unique simulation tool developed by our team.

We have already developed a unique algorithm, which makes high complexity calculations possible and effectively on GPU. Further on we have finished the integration of a GPU based and thus massively parallel integrator module. This module calculates integrals for quantum chemistry (QC) on parallel systems effectively. Our software is also able to simulate such large-scale molecules in reasonable time with high accuracy, which has not been possible until now. We address to open new research areas, as we can accurately calculate not only the features of large molecules but the attachment of active substances, which was managed by approximations so far.

Work performed

There have been work done on our GPU based modules to speed up calculations and make simulations more precise (namely, our parallel integrator module is capable of efficiently simulating up to g-type molecular orbitals on the GPU). This is a qunique capability in this field. We proceed to increase the speed of our modules and extend the portion within the numerical algorithm (SCF-HF) where our modules can be utilized. With the help of our early adopters we identified other significant parts and moved it on the GPU. To be able to simulate extremely large systems a less computation intensive method has to be used together with QC methods. These methods are called molecular mechanics (MM). They are a classical approximations of the forces within a molecule like chemical bonds, electrostatic force, etc. It is beneficial to combine the MM methods and the more precise QM methods with a layered simulation approach called ONIOM. This method balances between the precision and computational cost effectively and can combine several different methods together.

The main goal of the project is to speed up original drug research by helping pharmaceutical researchers to simulate and get results much faster than previously. To this end a conformational space search algorithm is added as well. The purpose of this algorithm is to determine possible binding sites on the surface of a protein given a possible ligand candidate. To this end a machine learning approach was used on both simulation and experimental data. 

Progress beyond the state of the art

The main needs of the pharmaceutical industry are reduce the number of molecular candidates, decrease the failure rate by cheap simulation, and use less experiments in the first stage of the drug R&D.


We have made commitment to enable fast and precise simulations by two facts. The first one is the selected precise simulation method (QC is based on physical first principles) was made available to run on GPUs. The second one is the fact that our highly specialized framework makes the calculation of high impulse moment orbitals (like f and g) available. These facts make our unique. The availability of these high impulse orbitals are necessary to be able to simulate chemical bond formation and breaking in biologically relevant molecules and systems.
In order to increase the performance of our simulator we added the CPU as a computing engine as well. In this way both the GPU and the CPU cores are utilized as much as possible. This CPU-GPU hybrid is also a highly innovative solution in the field of QC. 


The previously listed facts and results make available the usage of QC simulations as a powerful tool within the pharmaceutical industry. These simulations can achieve the desired precision, can work together with other simulation methods, available to a wide scale of systems, flexible and experimentally validated. The introduction of QC was blocked by the trendemous computational time that is reduced by two orders of magnitude with our software module. As a consequence the main economical benefit of our product is the shortening of the phase of basic research during pharmaceutical drug research. This means cheaper and faster time to market. The advantages of our simulation tool will consequently result more frequent launch of new, more efficient and cheaper medicines, which would bring a significant change especially for Europe’s senescent society.