🧬 Weekly Recap

Dec 28 - Jan 04, 2026

Missed a day? Here are the top research signals and tools from the past week, summarized for your Sunday reading.

🏆 Top Signals of the Week

🗓️ Sunday, Dec 28

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

The development of small-molecule tyrosine kinase inhibitors remains a high-priority strategy in modern oncology, particularly those targeting the Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2) to disrupt pathological angiogenesis. This study utilized a dual-methodology approach to evaluate a novel series of five coumarin nucleoside conjugates ( 5a - 5e ) as potential anti-cancer agents. Initially, the compounds’ drug-likeness was confirmed via ADMET prediction, which established favorable pharmacokinetic profiles. This was followed by an integrated MTT cytotoxicity screening against Oct1 (head and neck) and C33a (cervical) cancer cell lines, which identified compound 5d as the most potent cellular agent. The core of the investigation involved a comprehensive in silico analysis targeting the VEGFR-2 tyrosine kinase domain (TKD). Molecular docking revealed that all five compounds possess significantly superior predicted binding affinities compared to the native ligand, ATP (- 25.44 kJ/mol). Critically, the primary cellular lead 5d (- 29.46 kJ/mol) and the strongest binder 5e (- 31.30 kJ/mol) both surpassed the affinity of the clinical benchmark, Sorafenib (- 28.80 kJ/mol), confirming their high potential as competitive inhibitors. Further validation using Molecular Dynamics (MD) simulation and MMPBSA analysis demonstrated exceptional dynamic stability and thermodynamic preference for the TKD-ligand complexes, firmly supporting the predicted binding hypothesis. In conclusion, compounds 5d and 5e are validated lead candidates possessing favorable absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, direct cellular cytotoxicity, and a robust computationally modeled dual-action profile. Future research is urgently mandated to perform VEGFR-2-specific functional assays to definitively validate the predicted anti-angiogenic mechanism and conduct in-vivo studies to assess therapeutic efficacy.

Why it matters: Essential ground-truth data for validating next-gen foundation models like Boltz or Chai.

🗓️ Monday, Dec 29

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

The development of small-molecule tyrosine kinase inhibitors remains a high-priority strategy in modern oncology, particularly those targeting the Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2) to disrupt pathological angiogenesis. This study utilized a dual-methodology approach to evaluate a novel series of five coumarin nucleoside conjugates ( 5a - 5e ) as potential anti-cancer agents. Initially, the compounds’ drug-likeness was confirmed via ADMET prediction, which established favorable pharmacokinetic profiles. This was followed by an integrated MTT cytotoxicity screening against Oct1 (head and neck) and C33a (cervical) cancer cell lines, which identified compound 5d as the most potent cellular agent. The core of the investigation involved a comprehensive in silico analysis targeting the VEGFR-2 tyrosine kinase domain (TKD). Molecular docking revealed that all five compounds possess significantly superior predicted binding affinities compared to the native ligand, ATP (- 25.44 kJ/mol). Critically, the primary cellular lead 5d (- 29.46 kJ/mol) and the strongest binder 5e (- 31.30 kJ/mol) both surpassed the affinity of the clinical benchmark, Sorafenib (- 28.80 kJ/mol), confirming their high potential as competitive inhibitors. Further validation using Molecular Dynamics (MD) simulation and MMPBSA analysis demonstrated exceptional dynamic stability and thermodynamic preference for the TKD-ligand complexes, firmly supporting the predicted binding hypothesis. In conclusion, compounds 5d and 5e are validated lead candidates possessing favorable absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, direct cellular cytotoxicity, and a robust computationally modeled dual-action profile. Future research is urgently mandated to perform VEGFR-2-specific functional assays to definitively validate the predicted anti-angiogenic mechanism and conduct in-vivo studies to assess therapeutic efficacy.

Why it matters: Essential ground-truth data for validating next-gen foundation models like Boltz or Chai.

🗓️ Tuesday, Dec 30

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

The development of small-molecule tyrosine kinase inhibitors remains a high-priority strategy in modern oncology, particularly those targeting the Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2) to disrupt pathological angiogenesis. This study utilized a dual-methodology approach to evaluate a novel series of five coumarin nucleoside conjugates ( 5a - 5e ) as potential anti-cancer agents. Initially, the compounds’ drug-likeness was confirmed via ADMET prediction, which established favorable pharmacokinetic profiles. This was followed by an integrated MTT cytotoxicity screening against Oct1 (head and neck) and C33a (cervical) cancer cell lines, which identified compound 5d as the most potent cellular agent. The core of the investigation involved a comprehensive in silico analysis targeting the VEGFR-2 tyrosine kinase domain (TKD). Molecular docking revealed that all five compounds possess significantly superior predicted binding affinities compared to the native ligand, ATP (- 25.44 kJ/mol). Critically, the primary cellular lead 5d (- 29.46 kJ/mol) and the strongest binder 5e (- 31.30 kJ/mol) both surpassed the affinity of the clinical benchmark, Sorafenib (- 28.80 kJ/mol), confirming their high potential as competitive inhibitors. Further validation using Molecular Dynamics (MD) simulation and MMPBSA analysis demonstrated exceptional dynamic stability and thermodynamic preference for the TKD-ligand complexes, firmly supporting the predicted binding hypothesis. In conclusion, compounds 5d and 5e are validated lead candidates possessing favorable absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, direct cellular cytotoxicity, and a robust computationally modeled dual-action profile. Future research is urgently mandated to perform VEGFR-2-specific functional assays to definitively validate the predicted anti-angiogenic mechanism and conduct in-vivo studies to assess therapeutic efficacy.

Why it matters: Essential ground-truth data for validating next-gen foundation models like Boltz or Chai.

🗓️ Wednesday, Dec 31

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

The development of small-molecule tyrosine kinase inhibitors remains a high-priority strategy in modern oncology, particularly those targeting the Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2) to disrupt pathological angiogenesis. This study utilized a dual-methodology approach to evaluate a novel series of five coumarin nucleoside conjugates ( 5a - 5e ) as potential anti-cancer agents. Initially, the compounds’ drug-likeness was confirmed via ADMET prediction, which established favorable pharmacokinetic profiles. This was followed by an integrated MTT cytotoxicity screening against Oct1 (head and neck) and C33a (cervical) cancer cell lines, which identified compound 5d as the most potent cellular agent. The core of the investigation involved a comprehensive in silico analysis targeting the VEGFR-2 tyrosine kinase domain (TKD). Molecular docking revealed that all five compounds possess significantly superior predicted binding affinities compared to the native ligand, ATP (- 25.44 kJ/mol). Critically, the primary cellular lead 5d (- 29.46 kJ/mol) and the strongest binder 5e (- 31.30 kJ/mol) both surpassed the affinity of the clinical benchmark, Sorafenib (- 28.80 kJ/mol), confirming their high potential as competitive inhibitors. Further validation using Molecular Dynamics (MD) simulation and MMPBSA analysis demonstrated exceptional dynamic stability and thermodynamic preference for the TKD-ligand complexes, firmly supporting the predicted binding hypothesis. In conclusion, compounds 5d and 5e are validated lead candidates possessing favorable absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, direct cellular cytotoxicity, and a robust computationally modeled dual-action profile. Future research is urgently mandated to perform VEGFR-2-specific functional assays to definitively validate the predicted anti-angiogenic mechanism and conduct in-vivo studies to assess therapeutic efficacy.

Why it matters: Essential ground-truth data for validating next-gen foundation models like Boltz or Chai.

🗓️ Thursday, Jan 01

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

The development of small-molecule tyrosine kinase inhibitors remains a high-priority strategy in modern oncology, particularly those targeting the Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2) to disrupt pathological angiogenesis. This study utilized a dual-methodology approach to evaluate a novel series of five coumarin nucleoside conjugates ( 5a - 5e ) as potential anti-cancer agents. Initially, the compounds’ drug-likeness was confirmed via ADMET prediction, which established favorable pharmacokinetic profiles. This was followed by an integrated MTT cytotoxicity screening against Oct1 (head and neck) and C33a (cervical) cancer cell lines, which identified compound 5d as the most potent cellular agent. The core of the investigation involved a comprehensive in silico analysis targeting the VEGFR-2 tyrosine kinase domain (TKD). Molecular docking revealed that all five compounds possess significantly superior predicted binding affinities compared to the native ligand, ATP (- 25.44 kJ/mol). Critically, the primary cellular lead 5d (- 29.46 kJ/mol) and the strongest binder 5e (- 31.30 kJ/mol) both surpassed the affinity of the clinical benchmark, Sorafenib (- 28.80 kJ/mol), confirming their high potential as competitive inhibitors. Further validation using Molecular Dynamics (MD) simulation and MMPBSA analysis demonstrated exceptional dynamic stability and thermodynamic preference for the TKD-ligand complexes, firmly supporting the predicted binding hypothesis. In conclusion, compounds 5d and 5e are validated lead candidates possessing favorable absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, direct cellular cytotoxicity, and a robust computationally modeled dual-action profile. Future research is urgently mandated to perform VEGFR-2-specific functional assays to definitively validate the predicted anti-angiogenic mechanism and conduct in-vivo studies to assess therapeutic efficacy.

Why it matters: Essential ground-truth data for validating next-gen foundation models like Boltz or Chai.

🗓️ Saturday, Jan 03

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

The development of small-molecule tyrosine kinase inhibitors remains a high-priority strategy in modern oncology, particularly those targeting the Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2) to disrupt pathological angiogenesis. This study utilized a dual-methodology approach to evaluate a novel series of five coumarin nucleoside conjugates ( 5a - 5e ) as potential anti-cancer agents. Initially, the compounds’ drug-likeness was confirmed via ADMET prediction, which established favorable pharmacokinetic profiles. This was followed by an integrated MTT cytotoxicity screening against Oct1 (head and neck) and C33a (cervical) cancer cell lines, which identified compound 5d as the most potent cellular agent. The core of the investigation involved a comprehensive in silico analysis targeting the VEGFR-2 tyrosine kinase domain (TKD). Molecular docking revealed that all five compounds possess significantly superior predicted binding affinities compared to the native ligand, ATP (- 25.44 kJ/mol). Critically, the primary cellular lead 5d (- 29.46 kJ/mol) and the strongest binder 5e (- 31.30 kJ/mol) both surpassed the affinity of the clinical benchmark, Sorafenib (- 28.80 kJ/mol), confirming their high potential as competitive inhibitors. Further validation using Molecular Dynamics (MD) simulation and MMPBSA analysis demonstrated exceptional dynamic stability and thermodynamic preference for the TKD-ligand complexes, firmly supporting the predicted binding hypothesis. In conclusion, compounds 5d and 5e are validated lead candidates possessing favorable absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, direct cellular cytotoxicity, and a robust computationally modeled dual-action profile. Future research is urgently mandated to perform VEGFR-2-specific functional assays to definitively validate the predicted anti-angiogenic mechanism and conduct in-vivo studies to assess therapeutic efficacy.

Why it matters: Essential ground-truth data for validating next-gen foundation models like Boltz or Chai.

🗓️ Sunday, Jan 04

AlphaFold for Docking Screens.

🧬 Abstract

AlphaFold is an AI system developed by Google DeepMind to generate three-dimensional structures of proteins without experimental data. The models created with AlphaFold are available on the AlphaFold Protein Structure Database (AlphaFoldDB) ( https://alphafold.ebi.ac.uk/ ). The AlphaFold database is searchable by sequence and protein identification. This chapter focuses on an AlphaFold model and its use for docking screens using Molegro Virtual Docker. We rely on Jupyter Notebooks to integrate docking simulations and build regression models based on the atomic coordinates of protein-pose complexes. Our study focuses on constructing a neural network regression model to predict the inhibition of cyclin-dependent kinase 19 (CDK19). This enzyme is a target for anticancer drugs and does not have experimental data for its atomic coordinates. We utilize the Molegro Data Modeller to construct a regression model based on docking results of inhibitors for which binding affinity data is available. All CDK19 datasets and Jupyter Notebooks discussed in this work are available at GitHub: https://github.com/azevedolab/docking#readme .

Why it matters: Provides actionable mutations to enhance catalytic efficiency or thermostability.

⚡ Selected Quick Reads

• UPLC-Q-TOF/MS-based spectrum-effect correlation combined with chemometrics and molecular docking for quality assessment and screening of bioactive components with hemostatic, antinociceptive, and anti-inflammatory activities in Liparis nervosa.: Ethnopharmacological relevance Liparis nervosa (LN) occurs in Southwest China and is traditionally used as a hemostatic and detoxifying agent; however, the pharmacodynamic basis for its medicinal properties is unclear; this impedes the quality standardization and clinical application of this herb. Aim of the study This study aimed to establish an integrated quality assessment system for LN by combining comprehensive chemical profiling with pharmacological evaluation to identify bioactive components and quality markers. Materials and methods Chemical profiling of ten regional LN specimens via UPLC-Q-TOF/MS revealed 53 shared components and characteristic fingerprints. Concurrently, systematic evaluation of hemostatic, antinociceptive, and anti-inflammatory activities was used to identify bioactive fractions. Using spectrum-effect modeling, which integrates techniques such as gray relational analysis, partial least squares regression, and bivariate correlation linked chromatographic features to bioactivities, these pharmacological effects were correlated with specific chemical components. Molecular docking was performed to validate target interactions. Orthogonal design coupled with spectrum-effect relationship analysis was used to pinpoint potential quality markers. Results As the first comprehensive study to systematically identify bioactive fractions and quality markers of LN, this work developed a tripartite evaluation framework integrating chemical profiling, pharmacological verification, and molecular docking-based target validation. Conclusions This methodology advances the standardization of LN, supports the interpretation of its pharmacological mechanisms of action, and facilitates the development of multi-target phytotherapeutic agents using LN bioactives.
• Combining network pharmacology, machine learning, molecular docking, molecular simulation dynamics and experimental validation to explore the mechanism of Zhenwu decoction in treating major depression through TNF-α pathways.: Background Major depressive disorder (MDD) is a severe psychophysiological condition characterized by cognitive decline, low energy, weight loss, insomnia, and increased suicide risk, posing a significant burden on global health. Zhenwu decoction (ZWD), a traditional Chinese medicine, has shown therapeutic potential in alleviating MDD symptoms. However, its complex composition has limited the understanding of its underlying pharmacological mechanisms. This study aimed to explore the antidepressant mechanisms of ZWD in the treatment of MDD. Methods Active compounds and potential targets of ZWD were identified through database screening and network pharmacology analysis. These targets were intersected with MDD-related genes to construct a protein-protein interaction network. Core targets were further refined using random forest algorithms. Molecular docking and molecular dynamics simulations were employed to evaluate the binding affinity and stability between key compounds and core targets. Experimental validation was conducted in a lipopolysaccharide (LPS)-induced mouse model of depression using behavioral testing, measurement of inflammatory cytokines, and gene expression analysis. Results Network pharmacology and machine learning identified TNF-α signaling as key pathways in the antidepressant effects of ZWD. Enrichment analysis highlighted the involvement of Lipid and atherosclerosis, the IL-17 signaling pathway. Core targets, including PPARG, F10, AR, TNF, PIK3CG, ADH1C, and GABRA6, were predicted to mediate its effects. Molecular docking and dynamics simulations confirmed strong binding of ZWD components, especially kaempferol, to TNF-α, inhibiting its expression. In vivo, ZWD improved anxiety/depressive-like behaviors in LPS-treated mice, evidenced by better performance in the behavioral tests. ZWD also reduced neuroinflammation, with decreased Tnf-α levels, and reduced IBA-1 and GFAP staining, indicating reduced microglial and astrocyte activation. These results suggest that ZWD alleviates depression through modulation of TNF-α-mediated inflammation. Conclusions These findings suggest that ZWD exerts antidepressant effects primarily by modulating TNF-α-mediated inflammatory pathways, providing a comprehensive molecular and experimental framework supporting its clinical potential in MDD treatment.
• Unraveling the mechanism of curcumin in coronary slow flow phenomenon through network pharmacology and molecular docking.: The coronary slow flow phenomenon (CSFP) is associated with an increased risk of adverse cardiovascular events, yet standardized treatment is lacking. Curcumin, a natural compound, has shown potential in alleviating angina and improving metabolic risk factors in CSFP, but its underlying molecular mechanisms remain unclear. This study employed an integrated computational strategy. Network pharmacology was used to identify potential targets of curcumin and CSFP from public databases, and common targets were identified. Functional enrichment analysis was performed on the common targets, and a protein-protein interaction network was constructed. Core targets were identified using MCODE and CytoHubba plugins in Cytoscape. Molecular docking evaluated the binding modes and affinities of curcumin with the core targets, while molecular dynamics simulations and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) calculations validated the stability and binding free energies of the complexes. A total of 120 predicted targets of curcumin and 435 CSFP-related targets were identified, yielding 19 common targets. Functional enrichment analysis revealed that curcumin may treat CSFP by modulating inflammatory response, vascular function, cell migration, proliferation, apoptosis, and oxidative stress. These targets were associated with key signaling pathways, including NF-κB, TNF, and HIF-1. Network analysis and topological algorithms identified five core targets: EGFR, ICAM1, NFKB1, PTGS2, and STAT3. Molecular docking results demonstrated that curcumin exhibited excellent binding affinity with all core targets. Molecular dynamics simulations confirmed that the curcumin-core target complexes remained structurally stable during the 100 ns simulation, and MM/GBSA calculations indicated significantly negative binding free energies, suggesting strong binding driving forces. Curcumin may exert therapeutic effects on CSFP through a multi-target mechanism, primarily by interacting with key proteins including EGFR, ICAM1, NFKB1, PTGS2, and STAT3, thereby regulating the NF-κB, TNF, and HIF-1 signaling pathways. This study provides a theoretical foundation for the application of curcumin in CSFP treatment, though further experimental validation is required.
• Unraveling the mechanism of curcumin in coronary slow flow phenomenon through network pharmacology and molecular docking.: The coronary slow flow phenomenon (CSFP) is associated with an increased risk of adverse cardiovascular events, yet standardized treatment is lacking. Curcumin, a natural compound, has shown potential in alleviating angina and improving metabolic risk factors in CSFP, but its underlying molecular mechanisms remain unclear. This study employed an integrated computational strategy. Network pharmacology was used to identify potential targets of curcumin and CSFP from public databases, and common targets were identified. Functional enrichment analysis was performed on the common targets, and a protein-protein interaction network was constructed. Core targets were identified using MCODE and CytoHubba plugins in Cytoscape. Molecular docking evaluated the binding modes and affinities of curcumin with the core targets, while molecular dynamics simulations and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) calculations validated the stability and binding free energies of the complexes. A total of 120 predicted targets of curcumin and 435 CSFP-related targets were identified, yielding 19 common targets. Functional enrichment analysis revealed that curcumin may treat CSFP by modulating inflammatory response, vascular function, cell migration, proliferation, apoptosis, and oxidative stress. These targets were associated with key signaling pathways, including NF-κB, TNF, and HIF-1. Network analysis and topological algorithms identified five core targets: EGFR, ICAM1, NFKB1, PTGS2, and STAT3. Molecular docking results demonstrated that curcumin exhibited excellent binding affinity with all core targets. Molecular dynamics simulations confirmed that the curcumin-core target complexes remained structurally stable during the 100 ns simulation, and MM/GBSA calculations indicated significantly negative binding free energies, suggesting strong binding driving forces. Curcumin may exert therapeutic effects on CSFP through a multi-target mechanism, primarily by interacting with key proteins including EGFR, ICAM1, NFKB1, PTGS2, and STAT3, thereby regulating the NF-κB, TNF, and HIF-1 signaling pathways. This study provides a theoretical foundation for the application of curcumin in CSFP treatment, though further experimental validation is required.
• Unraveling the mechanism of curcumin in coronary slow flow phenomenon through network pharmacology and molecular docking.: The coronary slow flow phenomenon (CSFP) is associated with an increased risk of adverse cardiovascular events, yet standardized treatment is lacking. Curcumin, a natural compound, has shown potential in alleviating angina and improving metabolic risk factors in CSFP, but its underlying molecular mechanisms remain unclear. This study employed an integrated computational strategy. Network pharmacology was used to identify potential targets of curcumin and CSFP from public databases, and common targets were identified. Functional enrichment analysis was performed on the common targets, and a protein-protein interaction network was constructed. Core targets were identified using MCODE and CytoHubba plugins in Cytoscape. Molecular docking evaluated the binding modes and affinities of curcumin with the core targets, while molecular dynamics simulations and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) calculations validated the stability and binding free energies of the complexes. A total of 120 predicted targets of curcumin and 435 CSFP-related targets were identified, yielding 19 common targets. Functional enrichment analysis revealed that curcumin may treat CSFP by modulating inflammatory response, vascular function, cell migration, proliferation, apoptosis, and oxidative stress. These targets were associated with key signaling pathways, including NF-κB, TNF, and HIF-1. Network analysis and topological algorithms identified five core targets: EGFR, ICAM1, NFKB1, PTGS2, and STAT3. Molecular docking results demonstrated that curcumin exhibited excellent binding affinity with all core targets. Molecular dynamics simulations confirmed that the curcumin-core target complexes remained structurally stable during the 100 ns simulation, and MM/GBSA calculations indicated significantly negative binding free energies, suggesting strong binding driving forces. Curcumin may exert therapeutic effects on CSFP through a multi-target mechanism, primarily by interacting with key proteins including EGFR, ICAM1, NFKB1, PTGS2, and STAT3, thereby regulating the NF-κB, TNF, and HIF-1 signaling pathways. This study provides a theoretical foundation for the application of curcumin in CSFP treatment, though further experimental validation is required.
• Combining network pharmacology, machine learning, molecular docking, molecular simulation dynamics and experimental validation to explore the mechanism of Zhenwu decoction in treating major depression through TNF-α pathways.: Background Major depressive disorder (MDD) is a severe psychophysiological condition characterized by cognitive decline, low energy, weight loss, insomnia, and increased suicide risk, posing a significant burden on global health. Zhenwu decoction (ZWD), a traditional Chinese medicine, has shown therapeutic potential in alleviating MDD symptoms. However, its complex composition has limited the understanding of its underlying pharmacological mechanisms. This study aimed to explore the antidepressant mechanisms of ZWD in the treatment of MDD. Methods Active compounds and potential targets of ZWD were identified through database screening and network pharmacology analysis. These targets were intersected with MDD-related genes to construct a protein-protein interaction network. Core targets were further refined using random forest algorithms. Molecular docking and molecular dynamics simulations were employed to evaluate the binding affinity and stability between key compounds and core targets. Experimental validation was conducted in a lipopolysaccharide (LPS)-induced mice model of depression using behavioral testing, measurement of inflammatory cytokines, and gene expression analysis. Results Network pharmacology and machine learning identified TNF-α signaling as key pathways in the antidepressant effects of ZWD. Enrichment analysis highlighted the involvement of Lipid and atherosclerosis, the IL-17 signaling pathway. Core targets, including PPARG, F10, AR, TNF, PIK3CG, ADH1C, and GABRA6, were predicted to mediate its effects. Molecular docking and dynamics simulations confirmed strong binding of ZWD components, especially kaempferol, to TNF-α, inhibiting its expression. In vivo, ZWD improved anxiety/depressive-like behaviors in LPS-treated mice, evidenced by better performance in the behavioral tests. ZWD also reduced neuroinflammation, with decreased Tnf-α levels, and reduced IBA-1 and GFAP staining, indicating reduced microglial and astrocyte activation. These results suggest that ZWD alleviates depression through modulation of TNF-α-mediated inflammation. Conclusions These findings suggest that ZWD exerts antidepressant effects primarily by modulating TNF-α-mediated inflammatory pathways, providing a comprehensive molecular and experimental framework supporting its clinical potential in MDD treatment.
• Exploring the Anti-Inflammatory Molecular Mechanism of Gentiana szechenyii Kanitz. Based on UPLC-MS/MS Combined With Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation.: This study explored the anti-inflammatory mechanisms of Gentiana szechenyii Kanitz. (GS), a Tibetan medicinal herb, by combining UPLC-MS/MS, network pharmacology, molecular docking, and molecular dynamics (MD) simulation. Using the lipopolysaccharide (LPS)-induced RAW264.7 cell inflammation model, the anti-inflammatory effect of GS was confirmed by detecting the release amount of nitric oxide (NO) and the levels of inflammatory factors tumor necrosis factor (TNF) and interleukin-6 (IL-6). UPLC-MS/MS identified 40 constituents, whereas network analysis predicted 5 core compounds (isovitexin 4’,7-diglucoside, loganin, isoorientin-2″-O-glucoside, gentiopicroside, sweroside), 5 key targets (TNF, IL-6, GAPDH, epidermal growth factor receptor [EGFR], HSP90AA1), and three critical pathways (PI3K-Akt, hypoxia inducible factor-1 [HIF-1], IL-17). Molecular docking showed strong binding between core compounds and targets; the binding energies were all lower than -5 kcal mol -1 , among which isovitexin 4’,7-diglucoside had the lowest binding energy to EGFR (-9.4 kcal mol -1 ). MD simulation confirmed stable binding of TNF with the five core compounds. This study comprehensively clarifies the pharmacodynamic material basis and mechanism of action of GS in anti-inflammation, providing an experimental basis for further development and utilization. It is expected to be applied to the adjuvant treatment of inflammation-related diseases such as chronic bronchitis and pharyngitis in the future, thereby promoting the modernization of Tibetan medicine.

🛠️ Tools & Datasets

• 🛠 Tool: RFdiffusion - State-of-the-art generative model for de novo protein design.
• 🛠 Tool: ProteinMPNN - High-speed sequence design optimized for fixed-backbone folding.
• 💾 Dataset: AlphaFold Structure Database - 200M+ predicted structures from DeepMind/EMBL-EBI.
• 💾 Dataset: Uniprot Knowledgebase - The world’s most comprehensive resource for protein sequence and annotation.
• 🛠 Tool: ProteinMPNN - High-speed sequence design optimized for fixed-backbone folding.
• 🛠 Tool: OpenFold - Fast, trainable, and open implementation of AlphaFold2.
• 💾 Dataset: Uniprot Knowledgebase - The world’s most comprehensive resource for protein sequence and annotation.
• 💾 Dataset: PDB-REDO - Optimized protein structure database with refined models.
• 🛠 Tool: OpenFold - Fast, trainable, and open implementation of AlphaFold2.
• 🛠 Tool: ChimeraX - Next-gen molecular visualization for large data sets.
• 💾 Dataset: PDB-REDO - Optimized protein structure database with refined models.
• 💾 Dataset: Protein Data Bank (PDB) - The single global archive for macromolecular structure data.
• 🛠 Tool: ChimeraX - Next-gen molecular visualization for large data sets.
• 🛠 Tool: ReFOLD4 - Sophisticated protein structure refinement tool for improving model quality.
• 💾 Dataset: Protein Data Bank (PDB) - The single global archive for macromolecular structure data.
• 💾 Dataset: AlphaFold Structure Database - 200M+ predicted structures from DeepMind/EMBL-EBI.
• 🛠 Tool: ReFOLD4 - Sophisticated protein structure refinement tool for improving model quality.
• 🛠 Tool: FunFOLD5 - Automated system for protein ligand-binding site prediction and function annotation.
• 💾 Dataset: AlphaFold Structure Database - 200M+ predicted structures from DeepMind/EMBL-EBI.
• 💾 Dataset: Uniprot Knowledgebase - The world’s most comprehensive resource for protein sequence and annotation.
• 🛠 Tool: MultiFOLD/IntFOLD - High-performance protein structure prediction and quality assessment server.
• 🛠 Tool: PyMOL - Gold standard for molecular visualization and publication-quality imaging.
• 💾 Dataset: PDB-REDO - Optimized protein structure database with refined models.
• 💾 Dataset: Protein Data Bank (PDB) - The single global archive for macromolecular structure data.
• 🛠 Tool: PyMOL - Gold standard for molecular visualization and publication-quality imaging.
• 🛠 Tool: Chai-1 - Multi-modal foundation model for molecular structure prediction.
• 💾 Dataset: Protein Data Bank (PDB) - The single global archive for macromolecular structure data.
• 💾 Dataset: AlphaFold Structure Database - 200M+ predicted structures from DeepMind/EMBL-EBI.

🤖 AI in Research Recap

• Demis Hassabis: ‘The Thinking Game’ Hits 200M YouTube Views in 4 Weeks — What It Means for AGI Sentiment and AI–Crypto Traders - Blockchain News: Demis Hassabis: ‘The Thinking Game’ Hits 200M YouTube Views in 4 Weeks — What It Means for AGI Sentiment and AI–Crypto Traders    Blockchain News
• DeepMind AI Documentary by Greg Kohs Showcases Breakthroughs and Real-World Applications in 2025 - Blockchain News: DeepMind AI Documentary by Greg Kohs Showcases Breakthroughs and Real-World Applications in 2025    Blockchain News
• ‘The Thinking Game’ Documentary Surpasses 200M Views: Inside AGI Labs and AlphaFold’s AI Breakthroughs - Blockchain News: ‘The Thinking Game’ Documentary Surpasses 200M Views: Inside AGI Labs and AlphaFold’s AI Breakthroughs    Blockchain News
• Oriol Vinyals: “I use an AI personal assistant” - Diari ARA: Oriol Vinyals: “I use an AI personal assistant”    Diari ARA
• 200 million views in 4 weeks: Google AI CEO Demis Hassabis celebrates success of The Thinking Game docume - Times of India: 200 million views in 4 weeks: Google AI CEO Demis Hassabis celebrates success of The Thinking Game docume    Times of India
• ‘The Thinking Game’ crosses 200M views in 4 weeks: How Google’s viral documentary is spilling AGI secrets - financialexpress.com: ‘The Thinking Game’ crosses 200M views in 4 weeks: How Google’s viral documentary is spilling AGI secrets    financialexpress.com
• AlphaFold’s Five-Year Reign: 3 Million Researchers and the Dawn of a New Biological Era - FinancialContent: AlphaFold’s Five-Year Reign: 3 Million Researchers and the Dawn of a New Biological Era    FinancialContent
• 200 Million People Watched Globally: Why Did He Win the Nobel Prize? All Revealed in 1.5 Hours - 36Kr: 200 Million People Watched Globally: Why Did He Win the Nobel Prize? All Revealed in 1.5 Hours    36Kr

🏢 Industry & Real-World Applications

• Fapon Biopharma Publishes Pioneering Research in Cell Reports Medicine on FP008, an anti-PD 1 X IL-10M Fusion Protein for Cancer Immunotherapy - Laotian Times: Fapon Biopharma Publishes Pioneering Research in Cell Reports Medicine on FP008, an anti-PD 1 X IL-10M Fusion Protein for Cancer Immunotherapy    Laotian Times
• Oncodesign Precision Medicine and Navigo Proteins Announce the End of Their Collaboration in the Development of Radiotheranostics - Business Wire: Oncodesign Precision Medicine and Navigo Proteins Announce the End of Their Collaboration in the Development of Radiotheranostics    Business Wire
• Retro Bio commences first-in-human trial - Longevity.Technology: Retro Bio commences first-in-human trial    Longevity.Technology
• Protein Engineering for Pharmaceutical Biotechnology Training Course: Understand How to Advance Ther - PharmiWeb.com: Protein Engineering for Pharmaceutical Biotechnology Training Course: Understand How to Advance Ther    PharmiWeb.com
• Layoff Tracker: Nido Will Close Following Lead Candidate’s Disappointing Phase II Results - BioSpace: Layoff Tracker: Nido Will Close Following Lead Candidate’s Disappointing Phase II Results    BioSpace

Enjoyed this digest? Subscribe above to get these dailies in your inbox every morning.