TL;DR
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.
Recent developments in QM-based molecular docking and HTD are reviewed, focusing on cases where QM is used explicitly in docking workflows.
Current rational drug design relies predominantly on computational (CADD/AIDD) methods that model binding thermodynamics and static conformations of target proteins, primarily in their inactive states. However, the kinetic parameters that govern experimental efficacy—such as catalytic turnover and signaling potency—are determined by molecular interactions with transition states (TS), intermediate states (IS), and the entire continuum of conformations along the least free-energy activation pathway. The absence of this dynamic dimension has fundamentally limited the predictive power and success rate of conventional structure-based approaches. Here, we present a structural database that systematically maps the complete activation trajectories of pharmaceutically relevant targets, encompassing TS, IS, and all connecting conformational ensembles. This resource offers multiple strategic advantages for drug discovery: enabling rational targeting of previously “undruggable” proteins, facilitating biased agonism/antagonism design, revealing cryptic allosteric sites in inactive conformations, identifying novel transient pockets along the activation route, rationalizing the mechanisms of existing drugs, predicting mutational effects on activation barriers, and prospectively forecasting drug resistance and off-target liabilities. We demonstrate the utility of this database through representative case studies and provide implementation guidelines for integration into existing discovery pipelines. More detailed information can be found at our website: https://www.momedpamdb.com/en. Terminology The following terms are clarified in this document: Stable state (SS): In this document, this term refers exclusively to, and is synonymous with, the protein’s inactive state (IAS). Note that other states may also be stabilized into meta-stable states by certain means. Unstable state (US): This term encompasses all states other than SS, even if they appear computationally meta-stable on the free energy surface. Activated state (AS): The meta-stable working state of the protein. Transition state (TS): The state with the highest free energy along the least-energy pathway on the free energy surface that connects the inactive state to the activated state of the target protein. Intermediate state (IS): The state(s) located at a local minimum along the least-energy pathway, excluding SS and AS.
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/.
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.