Drug Discovery

Analog Quantum Computing for Drug Discovery

Leveraging Analog Quantum Computing with Neutral Atoms for Solvent Configuration Prediction in Drug Discovery

Authors: Mauro D’Arcangelo, Daniele Loco, Fresnel team, Nicolaï Gouraud, Stanislas Angebault, Jules Sueiro, Pierre Monmarché, Jérôme Forêt, Louis-Paul Henry, Loïc Henriet, and Jean-Philip Piquemal


Lay summary

Proteins are long chains of molecules (amino acids) that interact with each other, causing the chain to fold creating complex structures with pockets. The water in the cell penetrates the inner part of the protein, filling its pockets, affecting the size and scaffold of the protein, and mediating the interaction between a protein and a ligand. Investigating where and how many water molecules a protein may have in its pockets is crucial for designing medicines able to inhibit the toxic behavior of the targeted protein.

Computational methods for understanding protein hydration have significantly advanced alongside experimental approaches, saving time and reducing costs, facilitating drug discovery processes. The downside is that classical simulations are usually costly, and the time required to provide accurate predictions can be extremely long, mainly if the cavity under investigation is occluded enough.

An alternative approach is first finding the water density in the protein pockets and then extracting the water molecules’ position from the density.

However, the number of configurations—the different ways water molecules can be placed in a pocket—corresponding to a given density remains potentially extremely large for classical methods.

PASQAL, in collaboration with Qubit Pharmaceuticals, is developing a hybrid quantum/classical approach that uses a classical algorithm to find the water density information in the protein and then a quantum algorithm to locate the water molecules inside any pocket, even in the buried ones.

As proof of concept, a preliminary version of the novel quantum water placement algorithm was successfully tested on Fresnel 1, the first PASQAL commercial neutral atoms quantum computer.

This is the first time such an experiment has been conducted with the use of a real quantum computer, showing the capacity of quantum technologies to contribute to advance investigations in healthcare. The full version of the PASQAL water placement algorithm will be implemented on the next generation neutral atom machine that will be operating with1000 qubits.


Read the full story in our blog: Quantum Algorithm Tested on a Commercial Quantum Device Can Help Discover Drugs – PASQAL



We introduce quantum algorithms able to sample equilibrium water solvent molecules configurations within proteins thanks to analog quantum computing. To do so, we combine a quantum placement strategy to the 3D Reference Interaction Site Model (3D-RISM), an approach capable of predicting continuous solvent distributions. The intrinsic quantum nature of such coupling guarantees molecules not to be placed too close to each other, a constraint usually imposed by hand in classical approaches. We present first a full quantum adiabatic evolution model that uses a local Rydberg Hamiltonian to cast the general problem into an anti-ferromagnetic Ising model. Its solution, an NP-hard problem in classical computing, is embodied into a Rydberg atom array Quantum Processing Unit (QPU). Following a classical emulator implementation, a QPU portage allows to experimentally validate the algorithm performances on an actual quantum computer. As a perspective of use on next generation devices, we emulate a second hybrid quantum-classical version of the algorithm. Such a variational quantum approach (VQA) uses a classical Bayesian minimization routine to find the optimal laser parameters. Overall, these Quantum-3D-RISM (Q-3D-RISM) algorithms open a new route towards the application of analog quantum computing in molecular modelling and drug design.

Follow the link to read the full preprint: https://arxiv.org/abs/2309.12129