Advanced HPC based drug discovery with converged Deep Physics and AI

Presentation of the problem and objective of the experiment

As time constraints and agility become crucial in the pharmaceutical industry, especially in the early stages of the drug discovery process, development of a consolidated and unified HPC framework paves the way for a major breakthrough. Such a framework will dramatically reduce the structure generation time, binding free energy calculation time, and computing costs, allowing faster delivery of a collection of optimally designed candidate drugs to their business partners. With this framework, we aim to reduce drug discovery time by 25% and costs by 20%. 

Short description of the experiment

The goal of this experiment is to synergistically combine the expertise in the high-end calculation of absolute binding free energy of protein-ligand complex developed by Qubit Pharmaceuticals and Iktos’s AI-based deep generative modelling algorithms for novel compound generation and lead optimization. The combination of physics-based simulation and AI-driven deep generative modelling algorithms may lead to the development of a highly efficient drug discovery technology.

Given their technical expertise and domain applicability, the scaffold hopping approach is a well-suited case study for both companies to develop and explore commercial opportunities in the early drug discovery arena. Specifically, the new technology aims to discover structurally novel compounds starting from known actives by modifying the central core structure of the molecule.

Organisations involved:

End User: Iktos
Domain Expert: Qubit Pharmaceuticals