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.
A hybrid quantum-classical approach for molecular docking is proposed leveraging the MVWCP formalism with a variational full-basis encoding (FBE) strategy, which enables efficient encoding of classical binary variables with Bloch sphere vectors and proves that a global minimizer of the FBE objective can always be chosen to be a pure product state.
Tianqi Chen, A. Mak, Jianguo Li et al.· 0 citations
Designing molecules with optimized properties remains a fundamental challenge due to the intricate relationship between molecular structure and properties. Traditional computational approaches that address the combinatorial number of possible molecular designs become unfeasible as the molecular size increases, suffering from the so-called "curse of dimensionality" problem. Recent advances in quantum computing hardware present new opportunities to address this problem. Here, we introduce the quantum ensemble variational optimization (QEVO) method for near-term and early fault-tolerant quantum computing platforms. QEVO efficiently maps molecular structures onto an orthonormal basis of binary strings and samples from a superposition state generated by a variational ansatz. The ansatz is iteratively optimized to identify molecular candidates with the desired property. Our numerical simulations demonstrate the potential of QEVO to design drug-like molecules with anticancer properties, operating in combinatorial spaces composed of up to [Formula: see text] solutions, while employing a shallow quantum circuit that requires only a modest number of qubits. We envision that QEVO could be applied to a wide range of complex problems, offering practical solutions to problems with combinatorial complexity.
F. Calcagno, Delmar G A Cabral, Ivan Rivalta et al.· Proceedings of the National...· 0 citations
We analyzed the performance of quantum algorithms for studying protein–ligand systems, using both simulator and real hardware. To achieve this, we selected thrombin and 5 ligands as a test case and employed a decomposition strategy using density matrix embedded theory, dividing the ligand systems into three fragments each. First, the energy of one of the fragments was calculated using the Variational Quantum Eigensolver (VQE) using a state vector simulator, while the energy of the remaining fragments was calculated using Couple Cluster Singles and Doubles (CCSD), with the protein treated as point charges located at the respective atomic sites. Through these noiseless simulations, we evaluated the results under ideal conditions using the state-of-the-art Unitary CCSD ansatz to validate the efficacy of the strategy in a controlled setting without quantum noise. Subsequently, we evaluated the impact of approximations in the ansatz, optimizer, and active space size, which are necessary to decrease the computational cost, in order to target real hardware during the current Noisy Intermediate-Scale Quantum era. Finally, we performed the VQE calculations using a superconducting quantum computer developed by the RIKEN RQC–Fujitsu Collaboration Center and also analyzed the noise effect through simulations. The results demonstrated that the use of quantum algorithms can enhance the binding energy correlation value, offering potential applications in the workflow of computer-aided drug design.
Hironobu Kitajima, Carlos Bistafa, Takao Kobayashi et al.· Journal of Chemical Informat...· 0 citations
Q-Score is introduced, encoding GNN-predicted orbital donor-acceptor energies into a weighted graph and scoring binding by solving a maximum-weight vertex clique problem via Digitized-Counterdiabatic QAOA, enriching for strong orbital interactions at twice the random rate.
Kangyu Zheng, Yidong Zhou, Ruihao Li et al.· 0 citations