molecular docking

Stages

1. 1.
   Active ingredient identification(in_silico_filter)

   To define the candidate chemical components of the decoction for downstream target and mechanism analysis.

   Selection: network pharmacology identification of active ingredients from ZRGCDMD

2. 2.
   Disease target identification(in_silico_filter)

   To define the disease-associated target space relevant to insomnia comorbid with depression.

   Selection: identification of targets associated with depression comorbid with insomnia

3. 3.
   Functional enrichment analysis(secondary_characterization)

   To interpret the identified targets in terms of biological processes and pathways relevant to the disease context.

   Selection: GO and KEGG analysis of identified targets/pathways

4. 4.
   PPI target prioritization(hit_picking)

   To prioritize a smaller set of important targets from the broader target landscape.

   Selection: protein-protein interaction network analysis to identify important targets

5. 5.
   Molecular docking evaluation(confirmatory_validation)

   To test whether prioritized active ingredient-target pairs have plausible binding interactions in silico.

   Selection: binding energy estimation between active ingredients and primary targets

Steps

1. 1.
   Identify active ingredients from ZRGCDMD by network pharmacologycomputation method

   Generate the candidate active ingredient set from the decoction.

   Ingredient identification is needed before target and pathway analyses can be performed.

2. 2.
   Identify targets associated with depression comorbid with insomnia

   Define the disease-relevant target set for mechanism analysis.

   Disease-associated targets are needed to connect formula components to the comorbidity context.

3. 3.
   Perform GO and KEGG enrichment analysis

   Interpret identified targets in terms of biological processes and pathways.

   Enrichment analysis follows target identification because it requires the target set as input.

4. 4.
   Use protein-protein interaction network analysis to prioritize important targetscomputation method

   Prioritize important targets for downstream interpretation and docking.

   Target prioritization narrows the candidate space before docking to a smaller set of important targets.

5. 5.
   Dock active ingredients against primary targets and evaluate binding energiescomputation method

   Assess the plausibility of prioritized ingredient-target interactions.

   Docking is performed after target prioritization so that a smaller set of primary targets can be evaluated structurally.

Stages

1. 1.
   Virtual screening by molecular docking(broad_screen)

   The abstract states that docking enables rapid virtual screening of large libraries and helps prioritize candidates before experimental validation.

   Selection: predicted interaction with target proteins and high binding affinity

2. 2.
   Computational refinement with AI/ML and molecular dynamics(secondary_characterization)

   The abstract says AI/ML can enhance predictive accuracy and that molecular dynamics can refine candidate compounds, optimize binding affinities, and predict pharmacokinetic properties.

   Selection: improved predictive accuracy, refined candidate ranking, optimized binding-affinity and pharmacokinetic predictions

3. 3.
   Experimental in vitro validation(confirmatory_validation)

   The abstract explicitly states that docking limitations necessitate complementary in vitro validation and that future research should validate computational predictions experimentally.

   Selection: experimental confirmation of computational predictions

Steps

1. 1.
   Screen large compound libraries against target proteins by molecular dockingscreening method

   Rapidly evaluate natural products, existing drugs, and synthetic molecules against key pathogenic targets.

   This is the low-cost, high-throughput prioritization step described as reducing time and financial costs before experimental validation.

2. 2.
   Refine prioritized candidates with AI/ML integration and molecular dynamicscomputational refinement methods

   Improve predictive accuracy, uncover novel scaffolds, refine candidate ranking, optimize binding affinities, and predict pharmacokinetic properties.

   This step follows initial docking because it addresses limitations of static docking predictions before experimental validation.

3. 3.
   Experimentally validate computational predictions in vitro

   Confirm whether computationally prioritized compounds retain activity in biological testing and compensate for the lack of cellular context in docking.

   The abstract explicitly states that in vitro validation is needed after computational prioritization because docking relies on homology models and static structures and lacks cellular context.

Stages

1. 1.
   Structure-informed design and target-site mapping(library_design)

   The review states that design can be optimized by including structural data and that structural mapping helps identify residues suitable for mutagenesis and covalent attachment.

   Selection: Use structural data to design modular photoswitchable ligands and identify residues near the ligand binding pocket amenable to mutagenesis and covalent attachment.

2. 2.
   Computational modeling of target-ligand complexes(functional_characterization)

   The review states that modeling of target protein-ligand complexes can shed light on different activities of the two photoswitch isomers, the effect of site-directed mutations on binding, and ion channel subtype selectivity.

   Selection: Model the target protein in complex with the photoswitchable ligand to understand isomer-specific activities, mutation effects on binding, and subtype selectivity.

3. 3.
   Integration with experimental data for optimization(confirmatory_validation)

   The review explicitly concludes that integration of computational modeling with experimental data greatly facilitates photoswitchable ligand design and optimization.

   Selection: Combine computational modeling with experimental data to facilitate design refinement and optimization.

Stages

1. 1.
   Isolation of flavonoids(library_build)

   The abstract identifies isolation as an early current trend in flavonoid research and development.

   Selection: Obtain flavonoid ingredients from natural sources for downstream study.

2. 2.
   Identification of flavonoids(functional_characterization)

   Identification follows isolation in the abstract's stated research and development sequence.

   Selection: Determine what flavonoid ingredients have been obtained.

3. 3.
   Characterisation of flavonoids(secondary_characterization)

   The abstract explicitly lists characterisation as part of the R&D progression before functions and applications.

   Selection: Characterise flavonoids after identification for downstream functional interpretation.

4. 4.
   Functional assessment of flavonoids(functional_characterization)

   The abstract places functions before applications, implying that function informs later health-benefit use.

   Selection: Assess flavonoid functions before application-oriented use.

5. 5.
   Application prediction and health-benefit application(decision_gate)

   The abstract states that applications on health benefits come after earlier study stages and that molecular docking and bioinformatics are used to predict potential applications and manufacturing.

   Selection: Use functional understanding and computational prediction to support health-benefit applications and potential drug relevance.