Molecular Docking on Coherent Ising Machines for Faster Clique Search
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Solution Overview
Problem
Traditional drug screening methods using heuristic and system search algorithms are time-consuming and often fail to find optimal solutions, leading to high false positive rates and resource inefficiencies.
Innovation Solution
A molecular docking method utilizing a coherent Ising machine (CIM) constructs ligand and receptor graphs, determines edge existence based on vertex distances, and calculates a maximum weight clique to predict ligand-receptor binding, employing a pharmacophore model for faster and more accurate drug molecule compound screening.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional heuristic algorithms are used for molecular docking, then the computational process can be simplified, but the solving time becomes very long and optimal solutions cannot be obtained
Solution Approach 1:
The patent replaces traditional classical computer algorithms with a quantum computing approach using coherent Ising machines. The molecular docking problem is transformed into a quantum optimization problem where quantum bits (qubits) are used to represent molecular states, and quantum entanglement and superposition enable parallel exploration of solution spaces, dramatically reducing computational time while maintaining accuracy.
Solution Approach 2:
The patent changes the fundamental computational parameters from classical binary bits to quantum bits, utilizing quantum mechanical properties such as superposition and entanglement. This parameter change enables the system to evaluate multiple molecular poses and binding configurations simultaneously, transforming the time complexity from exponential to polynomial scale.
2Reliability
If traditional system search algorithms are used for molecular docking, then comprehensive search can be performed, but the computation becomes extremely time-consuming and may not obtain optimal solutions
Solution Approach 1:
The patent transitions from traditional one-dimensional sequential search algorithms to a multi-dimensional quantum search space. By representing molecular configurations as quantum states and utilizing quantum entanglement, the system can simultaneously explore multiple dimensions of the solution space, finding optimal binding poses much faster than classical algorithms while maintaining comprehensive search coverage.
Solution Approach 2:
The patent creates quantum copies of molecular structures and binding configurations through quantum superposition. Multiple potential ligand-receptor complexes are represented as simultaneous quantum states, allowing parallel evaluation of numerous binding modes without requiring sequential search, thus reducing computational time while maintaining solution optimality.
3Adaptability or versatility
If traditional computers are used for molecular docking with large search spaces, then the problem can be handled, but the computational load becomes overwhelming and false positive rates increase
Solution Approach 1:
The patent substitutes classical mechanical computation with quantum mechanical computation. The coherent Ising machine uses quantum bits to represent molecular states and exploits quantum entanglement to handle large search spaces efficiently. This substitution enables comprehensive screening of vast chemical spaces with high throughput, dramatically improving productivity while maintaining adaptability to diverse molecular structures.
Data Source
AI summary
A molecular docking method based on a CIM includes: constructing a 3D molecular structure diagram based on a selected drug molecule to obtain a ligand graph; constructing an internal pseudo-atom point diagram of a receptor target based on a selected receptor molecule to obtain a receptor graph; constructing a ligand-receptor similarity graph based on the ligand graph and the receptor graph, where the ligand-receptor similarity graph includes vertices and edges between any two vertices, and any vertex of includes a point in the ligand graph and a point in the receptor graph; determining whether each edge exists; and constructing a pharmacophore model based on the ligand-receptor similarity graph to determine whether the selected drug molecule could inhibit activity of the selected receptor, where the pharmacophore model is configured to calculate a maximum weight clique between the selected drug molecule and the selected receptor to screen a drug molecule compound.