Molecular Docking and Simulation Studies in Drug Discovery: Principles, Applications, and Current Limitations

Quantum-Driven High-Throughput Docking: Advances in Molecular Scoring and Drug Discovery.

Recent developments in QM-based molecular docking and HTD are reviewed, focusing on cases where QM is used explicitly in docking workflows.

Computational Study of Enzyme Inhibition.

How Can Structure-Based Computational Methods Support Antibiotic Drug Discovery in Addressing Antimicrobial Resistance?

Antimicrobial resistance (AMR) has emerged as a major global health challenge, necessitating the development of innovative strategies to accelerate antibiotic drug discovery. Traditional drug development approaches are often time-consuming, costly, and resource-intensive, creating a growing need for computational methods that can improve research efficiency and therapeutic candidate identification. This study investigates the role of computational protein structure prediction in advancing antibiotic drug discovery through a conceptual literature review of recent research published between 2022 and 2026. The review examines the application of protein structure prediction techniques, including deep learning, machine learning, molecular simulations, and AlphaFold-based approaches, in supporting structure-based drug design. Particular attention is given to their contributions in drug target identification, binding site prediction, protein–ligand interaction analysis, virtual screening, and candidate prioritization. The findings indicate that computational approaches significantly enhance the efficiency of early-stage drug discovery by enabling accurate structural modeling, rapid screening of large compound libraries, and improved identification of promising antibacterial targets. Furthermore, the integration of artificial intelligence with molecular modeling techniques has strengthened prediction accuracy and facilitated the discovery of novel therapeutic candidates against drug-resistant pathogens. Despite these advancements, challenges related to prediction reliability, biological complexity, computational resource requirements, and dependence on high-quality datasets continue to affect the robustness of current approaches. Additionally, the reviewed studies emphasize the necessity of experimental validation to confirm computational findings and ensure clinical applicability. Overall, the study concludes that computational protein structure prediction plays a critical role in accelerating antibiotic drug discovery and offers substantial potential for addressing antimicrobial resistance through more efficient and data-driven therapeutic development strategies.

Open-Sourced In Silico Drug Screening.

This chapter describes a structure-based computational approach to perform high-throughput ligand screens of chemical libraries using open-source software programs and illustrates this workflow with the enzymatic molecular target NAD(P)H:quinone oxidoreductase1 (NQO1), which is overexpressed in a number of human solid tumors.

FlexAutoDock: A Flexible Platform for Automated Molecular Docking and Virtual Screening of Natural and Synthetic Compounds

Drug discovery (DD) is a complex, time-consuming, and resource-intensive process that involves the identification of therapeutic targets, selection of bioactive compounds, and extensive experimental validation. The discovery of promising therapeutic compounds from large libraries of phytochemicals and synthetic molecules remains a major challenge in modern drug development. Screening millions of compounds through conventional experimental approaches requires substantial time, cost, and computational resources. In recent years, in- silico molecular docking has emerged as an important computational approach for predicting interactions between small molecules and target proteins, thereby helping researchers prioritize promising compounds for further investigation. Several molecular docking webservers, including iScreen, SwissDock, CB-Dock2, DockThor, and MTiOpenScreen, have been developed to support virtual screening studies. However, many currently available platforms still face some important limitations. Most existing tools lack integrated repositories of medicinal plant-derived phytochemicals and organism-derived bioactive compounds, automated mapping between plants and their associated phytochemicals, and flexible ligand retrieval using chemical names, SMILES strings, PubChem CIDs, or drug names. In addition, many platforms require extensive manual protein and ligand preparation, provide limited support for AlphaFold-predicted protein structures, and lack efficient large-scale multi-target virtual screening. Most existing docking platforms offer limited support for interactive inspection of docked protein-ligand complexes, often requiring users to download the results and analyse them using external molecular visualization software. To address these limitations, we developed FlexAutoDock, an automated cloud-based molecular docking platform that provides a unified environment for protein-ligand docking and large-scale virtual screening. Unlike existing web servers, FlexAutoDock integrates curated repositories of medicinal plant- derived phytochemicals, organism-derived bioactive compounds, and synthetic compounds from the ZINC database while supporting flexible ligand acquisition through medicinal plant or organism selection, chemical names, SMILES strings, PubChem CIDs, and drug-name queries. The platform further streamlines the docking workflow through automated protein structure retrieval from the Protein Data Bank and AlphaFold databases, receptor and ligand preparation, chain-specific protein selection, blind and site-specific docking, interactive visualization of predicted protein-ligand complexes, and scalable multi-target virtual screening. The resulting platform enables rapid, flexible, and large-scale virtual screening while simplifying the molecular docking workflow, providing researchers with an accessible computational resource for accelerating early-stage drug discovery. FlexAutoDock offers a fast, reliable, and accessible computational platform for molecular docking and virtual screening, freely available to the scientific community at http://103.99.177.82:3000/.

Pharmacophore-based virtual screening, molecular docking, MD simulation, MM-GBSA energy calculation and DFT analysis for the in silico discovery of a SaFtsZ inhibitor

An integrated computational strategy involving pharmacophore mapping, molecular docking, molecular dynamics simulations, and density functional theory (DFT) analysis was employed to identify potential SaFtsZ inhibitors, highlighting compound 15 as a computationally predicted scaffold for the development of SaFtsZ-targeted antibacterial agents.