TL;DR
Recent developments in QM-based molecular docking and HTD are reviewed, focusing on cases where QM is used explicitly in docking workflows.
Molecular docking and molecular dynamics (MD) simulations have become indispensable tools in modern drug discovery, enabling researchers to accelerate the identification and optimisation of therapeutic compounds. This comprehensive review examines the fundamental principles underlying these computational approaches, their diverse applications in pharmaceutical development, and the significant limitations that currently constrain their predictive accuracy and applicability. We discuss structure-based drug design methodologies, scoring functions, binding-affinity prediction, conformational sampling strategies, and the integration of artificial intelligence into computational drug discovery. Furthermore, we address critical challenges, including protein flexibility representation, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) prediction accuracy, and the persistent discrepancy between in silico predictions and experimental validation. Recent advances in hardware acceleration, force-field development, and machine learning are reshaping the landscape of computational drug discovery. This review synthesises current knowledge and highlights future opportunities for enhancing the reliability and efficiency of molecular docking and simulation studies in pharmaceutical research.
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.
The rapid emergence of antimicrobial resistance demands the discovery of new antibacterial targets and inhibitors. Staphylococcus aureus filamenting temperature-sensitive protein (SaFtsZ), an essential cytoskeletal protein involved in bacterial cytokinesis and Z-ring formation, has gained attention as a promising target for antibacterial drug discovery. In the present study, an integrated computational strategy involving pharmacophore mapping, molecular docking, molecular dynamics (MD) simulations, and density functional theory (DFT) analysis was employed to identify potential SaFtsZ inhibitors. Initially, large compound library was screened from the Pharmit database using a structure-based pharmacophore model to identify molecules with key interaction features required for SaFtsZ inhibition. The selected 200 candidates were further evaluated through molecular docking to determine their binding affinity and interaction pattern within the active site of SaFtsZ. Among the screened molecules, compound 15 (CID 135468497) exhibited the highest binding affinity with a docking score of −10.8 kcal mol−1. Subsequent MD simulation confirmed the stability of the protein–ligand complex, while DFT analysis provided insights into the electronic characteristics and reactivity of compound 15. These findings highlight compound 15 as a computationally predicted scaffold for the development of SaFtsZ-targeted antibacterial agents. However, experimental validation is required to confirm the computational results.