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IEEE International Conference on Quantum Computing and Engineering (QCE)

An Experimental Approach to Quantum Molecular Docking

Giacomo Lancellotti, Gianmarco Accordi, Gianluca Palermo

Published Sep 15, 2024• 4 citations
Publisher

Abstract

With the continuous growth of quantum hardware and its increasing accessibility, there is a shift away from theoretical research towards real-world problems. One of the most promising applications of quantum computing is drug discovery, which is a complex and multi-disciplinary process. To increase the success probability of finding new drugs, we can introduce an in-silico step to virtual screen the drug candidates. In this paper, we address the molecular docking problem of virtual screening. In particular, we analyze the performance of a molecular docking application, which is modeled as a Quadratic Unconstrained Binary Optimization (QUBO) instance. We leveraged the computational power offered by different quantum computing models, gate-based and quantum annealing, to speed the solution of the QUBO formulation. Experimental results show how quantum annealer is still the best in class for our case study. Moreover, we report here the experience and the takeaways we obtained by executing quantum molecular docking on publicly accessible quantum hardware and software.

DOI
10.1109/qce60285.2024.00066

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With the continuous growth of quantum hardware and its increasing accessibility, there is a shift away from theoretical research towards real-world problems. One of the most promising applications of quantum computing is drug discovery, which is a complex and multi-disciplinary process. To increase the success probability of finding new drugs, we can introduce an in-silico step to virtual screen the drug candidates. In this paper, we address the molecular docking problem of virtual screening. In particular, we analyze the performance of a molecular docking application, which is modeled as a Quadratic Unconstrained Binary Optimization (QUBO) instance. We leveraged the computational power offered by different quantum computing models, gate-based and quantum annealing, to speed the solution of the QUBO formulation. Experimental results show how quantum annealer is still the best in class for our case study. Moreover, we report here the experience and the takeaways we obtained by executing quantum molecular docking on publicly accessible quantum hardware and software.
Molecular docking is important in drug discovery but is burdensome for classical computers. Here, we introduce Grid Point Matching (GPM) and Feature Atom Matching (FAM) to accelerate pose sampling in molecular docking by encoding the problem into quadratic unconstrained binary optimization (QUBO) models so that it could be solved by quantum computers like the coherent Ising machine (CIM). As a result, GPM shows a sampling power close to that of Glide SP, a method performing an extensive search. Moreover, it is estimated to be 1000 times faster on the CIM than on classical computers. Our methods could boost virtual drug screening of small molecules and peptides in future.
Molecular docking is an important step in drug discovery, enabling the evaluation of receptor-ligand affinity while reducing experimental costs and increasing the number of possible tests. However, the high computational cost associated with molecular docking remains a limiting factor that can restrict both the experimental precision and the scale of the problems being addressed. To improve the future applicability of molecular docking, recent works have proposed the use of quantum algorithms based on Gaussian Boson Sampling quantum computers and also gate-based quantum computers. In this work, we propose the use of Quantum Circuit Evolution (QCE) for solving the molecular docking problem, a gate based and gradient-free quantum evolutionary method whose evolution is driven by the random application of unitary operations to a quantum circuit. The proposed algorithm demonstrated the ability to find the best solution to the problem in fewer steps than the methods presented in previous studies, exhibiting fast and stable convergence.
Abstract Molecular docking is an essential step in the drug discovery process involving the detection of three-dimensional poses of a ligand inside the active site of the protein. In this paper, we address the Molecular Docking search phase by formulating the problem in quadratic unconstrained binary optimization terms, suitable for an annealing approach. We propose a problem formulation as a weighted subgraph isomorphism between the ligand graph and the grid of the target protein pocket. In particular, we applied a graph representation to the ligand embedding all the geometrical properties of the molecule including its flexibility, and we created a weighted spatial grid to the 3D space region inside the pocket. The proposed quantum annealing-based method for molecular docking achieves valid ligand placements. Compared to simulated annealing, quantum solvers sampled fewer but higher-quality solutions with lower root-mean-square deviation, demonstrating competitive performance within hardware limits.
Abstract Molecular docking 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 focussed 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 simpler to manipulate within the target cavity. 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 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.
Molecular docking (MD) is a crucial task in drug design, which predicts the position, orientation, and conformation of the ligand when it is bound to a target protein. It can be interpreted as a combinatorial optimization problem, where quantum annealing (QA) has shown a promising advantage for solving combinatorial optimization. In this work, we propose a novel quantum molecular docking (QMD) approach based on a QA-inspired algorithm. We construct two binary encoding methods to efficiently discretize the degrees of freedom with an exponentially reduced number of bits and propose a smoothing filter to rescale the rugged objective function. We propose a new quantum-inspired algorithm, hopscotch simulated bifurcation (hSB), showing great advantages in optimizing over extremely rugged energy landscapes. This hSB can be applied to any formulation of an objective function under binary variables. An adaptive local continuous search is also introduced for further optimization of the discretized solution from hSB. Concerning the stability of docking, we propose a perturbation detection method to help rank the candidate poses. We demonstrate our approach on a typical data set. QMD has shown advantages over the search-based Autodock Vina and the deep-learning DIFFDOCK in both redocking and self-docking scenarios. These results indicate that quantum-inspired algorithms can be applied to solve practical problems in drug discovery even before quantum hardware become mature.
Molecular docking (MD) is a crucial task in drug design, which predicts the position, orientation, and conformation of the ligand when bound to a target protein. It can be interpreted as a combinatorial optimization problem, where quantum annealing (QA) has shown promising advantage for solving combinatorial optimization. In this work, we propose a novel quantum molecular docking (QMD) approach based on QA-inspired algorithm. We construct two binary encoding methods to efficiently discretize the degrees of freedom with exponentially reduced number of bits and propose a smoothing filter to rescale the rugged objective function. We propose a new quantum-inspired algorithm, hopscotch simulated bifurcation (hSB), showing great advantage in optimizing over extremely rugged energy landscapes. This hSB can be applied to any formulation of objective function under binary variables. An adaptive local continuous search is also introduced for further optimization of the discretized solution from hSB. Concerning the stability of docking, we propose a perturbation detection method to help ranking the candidate poses. We demonstrate our approach on a typical dataset. QMD has shown advantages over the search-based Autodock Vina and the deep-learning DIFFDOCK in both re-docking and self-docking scenarios. These results indicate that quantum-inspired algorithms can be applied to solve practical problems in the drug discovery even before quantum hardware become mature.