In Silico Drug Development

SNIC 2021/3-14


SNIC Large Compute

Principal Investigator:

Leif Eriksson


Göteborgs universitet

Start Date:


End Date:


Primary Classification:

10407: Theoretical Chemistry

Secondary Classification:

30103: Medicinal Chemistry

Tertiary Classification:

10602: Biochemistry and Molecular Biology



Computational chemistry has an important role to fill in the development of new pharmaceuticals. With HPC clusters coupled to the latest developments in algorithms and software, we are able to screen vast libraries of compounds searching for new drug candidates, create in silico models of target proteins, and explore protein-protein interactions crucial for e.g. signaling pathways in cancer cells. We focus our studies on several multi-protein complexes as targets for cancer therapy, in order to identify small molecule inhibitors able to block modes of action or signaling pathways. These involve the multimeric UPRosome including the activated IRE1 receptor, mRNA substrate, RtcB ligase, and phosphorylating/dephosphorylating enzymes ABL and PTP1B; and studies of several death inducing or death effector complexes such as the apoptosome, stressosome, and necrosome. We also explore the mechanism and identify selective inhibitors to the heavily upregulated MTHFD2 playing a key role in cancer cell drug resistance, and the small peptide AGR2 which recent results have shown to be an inducer of tumorogenesis. We follow well-established protocols, involving protein preparation (homology modelling if needed), protein-protein docking calculations according to a recent ‘consensus structure’ protocol developed in our group, followed by replica MD simulations to determine stabilities and key interactions. Normally, the MD simulations carried out are 500-1000 ns each, and performed in triplicate, placing high demands for HPC resources. In the drug development projects, we perform systematic docking of large databases (up to 1bn compounds), refined docking of top ranked ligands, and detailed BPMD and MD simulations of resulting complexes, followed by additional hit-to-lead optimizations. The size of the compound libraries we use in our research requires the use of massively parallel execution. We are currently extending our work into the area of Machine Learning de novo drug discovery, with very promising initial data. The size and extent of the simulations, and amount of data processed in the screening campaigns, justifies the 900 000 coreh/month currently applied for. The research group has been highly successful in using in silico methods for drug development, with several patented drugs already developed, and with a steady stream of high quality publications resulting from the use of the SNIC resources. From recent years’ SNIC allocations, new compounds targeting e.g., the histone acetyl transferase Tip60 have been developed and proven in vivo to hold great promise in the treatment of triple negative breast cancer. We have developed new compounds inhibiting the retinoic acid degrading enzyme CYP26B1 that can be used to treat various dermatology conditions with little or no side effects, compounds blocking the extracellular domain of RET kinase involved in thyroid cancer, and molecules targeting the motor kinesin KIF18B playing an important part in cell division and in DNA damage repair. Most recently, our use of HPC resources has led to the development of a novel compound to combat the lethal brain tumor glioblastoma multiforme, for which in vivo tests indicate that we may be able to cure the patients completely.