In Silico Drug Development
Title: In Silico Drug Development
DNr: NAISS 2023/4-6
Project Type: NAISS Large Storage
Principal Investigator: Leif Eriksson <>
Affiliation: Göteborgs universitet
Duration: 2024-01-01 – 2025-01-01
Classification: 10407 30103 10602


This Large Storage application is co-submitted with the Large Compute project application for the period Jan 1 - Dec 31 2024, and is a continuation of Large Storage 2022/4-6. 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; a range of protein complexes of the Integrated Stress Response (ISR); and studies of several death inducing or death effector complexes such as the apoptosome, stressosome, and necrosome. We will in the coming year initiate work on PROTACS and antibodies. We follow well-established protocols, involving homology modelling (if needed), protein-protein docking calculations according to a recent ‘consensus protocol' developed in our group, followed by replica MD simulations to determine stabilities and key interactions and mechanistic studies using QM/MM-MD. 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), 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 have recently extended our work into the area of Machine Learning for de novo drug discovery and protein interaction network determination, with very promising results. The size and extent of the simulations, and amount of data processed in the screening campaigns, justifies the resources applied for.