Quantum molecular unfolding

Abstract

Molecular Docking (MD) is an important step of the drug discovery process which aims at calculating the preferred position and shape of one molecule to a second when they are bound to each other. During such analysis, 3D representations of molecules are manipulated according to their degree of freedoms: rigid roto-translation and fragment rotations along the rotatable bonds. In our work, we focused on one specific phase of the molecular docking procedure i.e. Molecular Unfolding (MU), which is used to remove the initial bias of a molecule by expanding it to an unfolded shape. The objective of the MU problem is to find the configuration that maximizes the molecular area, or equivalently, that maximizes the internal distances between atoms inside the molecule. We propose a quantum annealing approach to MU by formulating it as a High-order Unconstrained Binary Optimization (HUBO) which was possible to solve on the latest D-Wave annealing hardware (2000Q and Advantage). Results and performances obtained with quantum annealers are compared with state of art classical solvers.

Leveraging Quantum Annealing for Ligand Modelling in Drug Discovery

“…Mato et al published a solution that uses quantum annealing (QA) for molecular unfolding [40] . In this approach, the molecule is modelled as a Quadratic Unconstrained Binary Optimization (QUBO) problem.…”

“…Quantum annealing, on the other hand, offers a unique combination of speed and accuracy [40] . The quantum annealer uses quantum mechanics to solve optimization problems in a way that is faster and more accurate than classical approaches.…”

Quantum annealing-based clustering of single cell RNA-seq data

“…They are designed to solve optimization problems and, in this respect, are superior to their gate-model counterparts [ 12 , 13 ]. In the literature, we can already find examples of successful applications of quantum annealing technology for life-science applications, such as peptides design [ 14 , 15 ]. Thanks to starting in the equal superposition and quantum tunneling effect, we can much more extensively sample the loss energy function landscape, and so we are more likely to find the global minimum (Due to inherent noise in the quantum systems, common approach to get exact minimum is to at the end of annealing refine found minima with the classical algorithms) or ’difficult to access’ local minima, which the classical simulated annealing would miss.…”

Encoding Molecular Docking for Quantum Computers

“…A 100,000 qubit CIM has been reported to solve MAX-CUT 1000 times faster than advanced digital computers. Recently, they have also been explored in NP-hard problems in science like compressed sensing, RNA folding, molecular unfolding, and protein protonation . To solve an NP-hard problem with QCs, one way is to encode the problem into a quadratic unconstrained binary optimization (QUBO) model, , which is to minimize a quadratic form ( x T Q r × r x ), where x is r binary decision variables and Q is a coefficient matrix.…”

“…QCs , are reliable tools to efficiently solve NP-hard problems. In fact, QCs have already been used to solve molecular unfolding, which is a preparation step before molecular docking. However, QCs could not solve pose sampling or molecular docking directly.…”