# Molecular Docking

Using network pharmacology to predict shared targets of paeonol and rosacea, then validating in an LL-37-induced rosacea-like BALB/c mouse model, the study found that 150 mg/kg paeonol markedly lowered redness area and score, reduced inflammatory cell and CD4+ T-cell infiltration and angiogenesis, and downregulated S100A9 and p65 phosphorylation, indicating its effects correlate with suppressed S100A9-NF-κB signaling.

Using network pharmacology to predict shared targets of paeonol and rosacea, then validating in an LL-37-induced rosacea-like BALB/c mouse model, the study found that 150 mg/kg paeonol markedly lowered redness area and score, reduced inflammatory cell and CD4+ T-cell infiltration and angiogenesis, and downregulated S100A9 and p65 phosphorylation, indicating its effects correlate with suppressed S100A9-NF-κB signaling.

This review systematically compiles progress in the design, synthesis, and biological evaluation of indole-based small molecules as antitubercular candidates targeting the mycolic acid biosynthesis pathway (MmpL3, InhA, KasA/KasB), listing per-series optimized-compound MIC values (e.g., compounds 20-22 at 0.0195 µg/mL, compound 36a at 0.024 µM, and compound 82 with MIC50 0.015 µM in the MmpL3 direction; compounds 122a at 0.39 µM and 143f at 3.99 µM in the InhA direction) alongside molecular docking results, and noting that many indole series still lack direct biochemical inhibition data such as purified-enzyme IC50/Ki and genetic validation including resistance mutations or target overexpression.

This review systematically compiles progress in the design, synthesis, and biological evaluation of indole-based small molecules as antitubercular candidates targeting the mycolic acid biosynthesis pathway (MmpL3, InhA, KasA/KasB), listing per-series optimized-compound MIC values (e.g., compounds 20-22 at 0.0195 µg/mL, compound 36a at 0.024 µM, and compound 82 with MIC50 0.015 µM in the MmpL3 direction; compounds 122a at 0.39 µM and 143f at 3.99 µM in the InhA direction) alongside molecular docking results, and noting that many indole series still lack direct biochemical inhibition data such as purified-enzyme IC50/Ki and genetic validation including resistance mutations or target overexpression.

This work generates new EGFR inhibitor candidates by fine-tuning a GPT-2 model on roughly 500,000 molecules from the ChEMBL database and benchmarking it against an LSTM network trained on the same dataset, evaluating generated compounds for validity, distinctiveness, and novelty, filtering them by Lipinski's rule of five, synthetic accessibility, and drug-likeness scores, and docking selected candidates against EGFR (PDB ID: 1M17) to assess binding affinity, finding that GPT-2 excels at producing structurally varied molecules while the LSTM generates a larger fraction of chemically valid compounds, with many candidates showing good binding interactions with EGFR.

This work generates new EGFR inhibitor candidates by fine-tuning a GPT-2 model on roughly 500,000 molecules from the ChEMBL database and benchmarking it against an LSTM network trained on the same dataset, evaluating generated compounds for validity, distinctiveness, and novelty, filtering them by Lipinski's rule of five, synthetic accessibility, and drug-likeness scores, and docking selected candidates against EGFR (PDB ID: 1M17) to assess binding affinity, finding that GPT-2 excels at producing structurally varied molecules while the LSTM generates a larger fraction of chemically valid compounds, with many candidates showing good binding interactions with EGFR.