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arXiv:2404.14101 · Journal of Molecular Modeling

Efficient molecular conformation generation with quantum-inspired algorithm

Yunting Li, Xiaopeng Cui, Zhaoping Xiong, Zuoheng Zou, Bowen Liu, Bi-Ying Wang, Runqiu Shu, Huangjun Zhu, Nan Qiao, Man‐Hong Yung

Published Jun 25, 2024• 6 citations
arXivHTMLPDF
DOI
10.1007/s00894-024-05962-9

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Coupling of quantum chemistry models and high-performance algorithms for the global exploration of the energy landscape of atomic and molecular systems

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The primary aim of this thesis is to develop efficient methods for characterizing molecular conformations at a quantum level. Various methods devoted to the computation of molecular potential energy are reviewed, as well as the most popular potential energy surfaces (PES) global exploration schemes. In this context, a key contribution of this thesis is the coupling of the robotics-inspired Iterative Global exploration and LOcal Optimization (IGLOO) method, implemented in the MoMA software, with the quantum Density-Functional based Tight-Binding (DFTB) potential, implemented in the deMonNano software. The IGLOO algorithm integrates the motion planning Rapidly-exploring Random Trees (RRT) algorithm with local optimization and structural filtering. A proof of concept has been done through the identification of low-energy conformations of the alanine dipeptide.The IGLOO/DFTB coupling has been applied to the mapping of the PES of three close-sized molecules of the phthalate family (dibutyl phthalate DBP, benzyl butyl phthalate BBP and di-2-ethylhexyl phthalate DEHP), providing detailed insights into their different conformational landscapes. Various geometrical descriptors have been used to analyze their structure-energy relationships. Coulomb interactions, steric hindrance, and dispersive interactions have been found to drive the geometric properties and a strong correlation has been evidenced between the two dihedral angles describing the side-chains orientation of the phthalate molecules. The results demonstrate the method's capability to identify low-energy minima without prior knowledge of the PES.Furthermore, an innovative algorithm for the large-scale generation of molecular structures, including a conformational variety, is presented. It combines molecular graph generation with atom or fragment addition techniques. It is applied to provide an extensive database of 3D structures of hydrogenated amorphous carbon (a-CH) molecules. The analysis of the database generated in this study provides a comprehensive understanding of the relationship between the geometrical and electronic descriptors of a-C:H structures. These properties are compared with those of compact Polycyclic Aromatic Hydrocarbons and linear chains, representing limit cases.Finally, a review is given on methods aiming at identifying saddle points and transition paths between low-energy conformations on the PES. A first step toward the identification of transition paths between low-energy conformations using a motion planning algorithm, known as Transition-based Rapidly-exploring Random Trees (T-RRT), is presented. A similarity measure, designated as the Symmetrized Segment-Path Distance (SSPD), is used to compare the generated trajectories. Subsequently, a clustering technique, namely the Hierarchical Clustering Analysis (HCA), is employed to group similar trajectories in order to identify the common pathways, thereby providing valuable insights into the dynamics of conformational changes. The methodology has been successfully applied to the identification of low-energy paths between two minima of the alanine dipeptide PES.Overall, the research presents significant advancements in the exploration of complex molecular PES at a quantum level including (i) the IGLOO/DFTB coupling (ii) a novel algorithm for 3D structure generation of large-scale molecules and (iii) an original scheme allowing for the identification of multiple transition paths. Correlations between the structural, energetic and electronic properties have been evidenced for the polluting phthalate molecules and astrophysically relevant hydrogenated amorphous carbon (a-CH) molecules. These contributions pave the way for future research, aiming to extend these methods to larger and more complex systems.

•Sep 27, 2024•0 citations
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.
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.
The primary aim of this thesis is to develop efficient methods for characterizing molecular conformations at a quantum level. Various methods devoted to the computation of molecular potential energy are reviewed, as well as the most popular potential energy surfaces (PES) global exploration schemes. In this context, a key contribution of this thesis is the coupling of the robotics-inspired Iterative Global exploration and LOcal Optimization (IGLOO) method, implemented in the MoMA software, with the quantum Density-Functional based Tight-Binding (DFTB) potential, implemented in the deMonNano software. The IGLOO algorithm integrates the motion planning Rapidly-exploring Random Trees (RRT) algorithm with local optimization and structural filtering. A proof of concept has been done through the identification of low-energy conformations of the alanine dipeptide.The IGLOO/DFTB coupling has been applied to the mapping of the PES of three close-sized molecules of the phthalate family (dibutyl phthalate DBP, benzyl butyl phthalate BBP and di-2-ethylhexyl phthalate DEHP), providing detailed insights into their different conformational landscapes. Various geometrical descriptors have been used to analyze their structure-energy relationships. Coulomb interactions, steric hindrance, and dispersive interactions have been found to drive the geometric properties and a strong correlation has been evidenced between the two dihedral angles describing the side-chains orientation of the phthalate molecules. The results demonstrate the method's capability to identify low-energy minima without prior knowledge of the PES.Furthermore, an innovative algorithm for the large-scale generation of molecular structures, including a conformational variety, is presented. It combines molecular graph generation with atom or fragment addition techniques. It is applied to provide an extensive database of 3D structures of hydrogenated amorphous carbon (a-CH) molecules. The analysis of the database generated in this study provides a comprehensive understanding of the relationship between the geometrical and electronic descriptors of a-C:H structures. These properties are compared with those of compact Polycyclic Aromatic Hydrocarbons and linear chains, representing limit cases.Finally, a review is given on methods aiming at identifying saddle points and transition paths between low-energy conformations on the PES. A first step toward the identification of transition paths between low-energy conformations using a motion planning algorithm, known as Transition-based Rapidly-exploring Random Trees (T-RRT), is presented. A similarity measure, designated as the Symmetrized Segment-Path Distance (SSPD), is used to compare the generated trajectories. Subsequently, a clustering technique, namely the Hierarchical Clustering Analysis (HCA), is employed to group similar trajectories in order to identify the common pathways, thereby providing valuable insights into the dynamics of conformational changes. The methodology has been successfully applied to the identification of low-energy paths between two minima of the alanine dipeptide PES.Overall, the research presents significant advancements in the exploration of complex molecular PES at a quantum level including (i) the IGLOO/DFTB coupling (ii) a novel algorithm for 3D structure generation of large-scale molecules and (iii) an original scheme allowing for the identification of multiple transition paths. Correlations between the structural, energetic and electronic properties have been evidenced for the polluting phthalate molecules and astrophysically relevant hydrogenated amorphous carbon (a-CH) molecules. These contributions pave the way for future research, aiming to extend these methods to larger and more complex systems.