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Daily Signal May 05, 2026 · 9 min read

Issue #101: Investigation of the potential mechanism by which methylparaben induces psoriasis: an integrated study using network toxicology, molecular docking, molecular dynamics simulation, and eight machine learning algorithms.

Protein Design Digest #101: Investigation of the potential mechanism by which methylparaben induces …

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Investigation of the potential mechanism by which methylparaben induces psoriasis: an integrated study using network toxicology, molecular docking, molecular dynamics simulation, and eight machine learning algorithms.

Psoriasis is a chronic inflammatory skin disease with limited safe and effective treatments. Methylparaben, a widely used preservative in cosmetics, pharmaceuticals, and food, is an emerging environmental pollutant linked to immune-related skin disorders, but its role and mechanism in psoriasis remain unclear. This study explored its potential mechanism using network toxicology, molecular docking, molecular dynamics simulation, and eight machine learning algorithms. Methylparaben targets were retrieved from GeneCards and TCMSP, and psoriasis-related targets from CTD and GeneCards. Overlapping targets were screened with Venny 2.1.0. A PPI network was constructed via STRING, and core targets identified using Cytoscape 3.10.2. GO and KEGG enrichment analyses were performed on DAVID. Molecular docking evaluated the binding affinity of methylparaben with key targets. A total of 138 compound-related and 5,592 psoriasis-related targets were identified. Core targets such as INS, HIF1A, and PPARG are involved in regulating immune-inflammatory responses, keratinocyte proliferation and differentiation, and oxidative stress. GO analysis revealed enrichment in xenobiotic metabolism, lipopolysaccharide response, and metal ion binding. KEGG analysis highlighted pathways related to cancer, chemical carcinogenesis from reactive oxygen species, and drug metabolism via cytochrome P450 enzymes. Molecular docking showed stable binding of methylparaben to INS (-4.5 kcal/mol), HIF1A (-5.9 kcal/mol), and PPARG (-5.5 kcal/mol), primarily through hydrogen bonds and hydrophobic interactions. Methylparaben may exert its effects on psoriasis via multi-target and multi-pathway mechanisms, influencing inflammation, oxidative stress, and cellular regulation. These findings provide valuable insight into its toxicological mechanism and potential therapeutic application.

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

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Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction

The rapid growth of molecular foundation models and general-purpose large language models has encouraged a scale-centric view of artificial intelligence in drug discovery, in which larger pretrained models are expected to supersede compact cheminformatics models and task-specific graph neural networks (GNNs). We test this assumption on 22 molecular property and activity endpoints, including public ADMET and Tox21 benchmarks and two internal anti-infective activity datasets. Across 167,056 held-out task–molecule evaluations under structure-similarity-separated five-fold cross-validation (37,756 ADMET, 77,946 Tox21, 49,266 anti-TB and 2,088 antimalaria), classical machine-learning (ML) models such as RF(ECFP4) and ExtraTrees(RDKit descriptors) win ten primary-metric tasks, GNNs such as GIN and Ligandformer win nine, and pretrained molecular sequence models such as MoLFormer and ChemBERTa2 win three. Rule-based SAR reasoning baselines, represented by GPT5.5-SAR and Opus4.7-SAR, do not win under the prespecified primary metrics, although train-fold-derived SAR knowledge provides measurable but uneven gains for SAR reasoning and interpretation. These results indicate that compact, specialized models remain highly effective for molecular property and activity prediction. The performance differences among classical ML, GNN and pretrained sequence models are often modest and endpoint-dependent, whereas larger or more general models do not provide a universal predictive advantage. Large models may still add value for zero-shot reasoning, SAR interpretation and hypothesis generation, but the results suggest that predictive performance depends on the alignment among molecular representation, inductive bias, data regime, endpoint biology and validation protocol.

Identification of paucinervin D as a natural sphingosine-1-phosphate receptor 1 agonist: Insights from pharmacophore modeling, docking, molecular dynamics simulations, and density functional theory.

Sphingosine-1-phosphate receptor 1 (S1PR1), a member of the G protein-coupled receptor (GPCR) family, is a crucial therapeutic target for various diseases. Activation of S1PR1 has been recognized as an effective therapeutic strategy for multiple sclerosis (MS), inflammatory bowel disease (IBD), and psoriasis. Natural products (NPs) serve as a rich source of bioactive compounds for drug discovery. Here, we aimed to discover novel S1PR1 agonists from NPs via multi-level virtual screening (VS). Using a validated HipHop pharmacophore model, we screened a database containing 54,642 NPs, followed by molecular docking. Based on binding mode analysis, four candidate S1PR1 agonists (NPC323626, NPC264112, NPC469907, and NPC22192) were selected. Subsequent molecular dynamics (MD) simulations and binding free energy calculations confirmed the stability of the receptor-ligand complexes and their binding affinities. Among the four candidates, NPC469907 exhibited the strongest binding affinity for S1PR1, with a value of -58.08 ± 0.13 kJ/mol. Furthermore, hydrogen bonds formed between NPC469907 and Glu121 of S1PR1 were found to be essential for receptor activation. Quantum mechanical calculations further revealed that the phenyl-ring-attached hydrogen site in NPC469907 could be modified without compromising its ability to activate S1PR1. The analysis of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) indicated that NPC469907 possessed favorable pharmacokinetic properties and low toxicity. In conclusion, our study identified NPC469907 as a promising natural S1PR1 agonist and established an effective VS strategy for the discovery of novel S1PR1 agonists.

Natural compounds extracted from medicinal herbs in the treatment of Parkinson’s disease; Molecular Docking, Molecular Dynamics simulation, and Quantum Mechanical calculations.

The dopamine D3 receptor (D3R), which belongs to class A of G-protein-coupled receptors (GPCRs), is a promising target and is significantly involved in the pathology of Parkinson’s disease progression. This study examines the inhibitory effects of natural compounds on D3R, highlighting their potential therapeutic applications in mitigating disease progression. Molecular docking simulations were conducted using AutoDock4.2, MOE, ICM, and Vina to evaluate the binding affinity of selected phytochemicals relative to levodopa (compound 1), a standard dopaminergic drug. Compounds such as 5, 13, and 15 demonstrated superior docking scores compared to compound 1. The ADMET analyses revealed favorable bioavailability and drug-likeness profiles, especially for 5, 13, and 15 compounds. Additionally, the stability of the 5_D3R and 13_D3R complexes relative to the 1_D3R complex was confirmed through a molecular dynamics (MD) simulation, which supports the biological potential of polygoni and green tea. According to the binding free energy calculated using MMPB(GB)SA, 5_D3R and 13_D3R complexes exhibit greater stability, which is in agreement with the MD simulation. Finally, a rigorous three-layer ONIOM (M06-2X/6-31G∗:PM6:AMBER) analysis confirmed that compounds 5 and 13 effectively inhibit D3R, highlighting their potential as promising drug candidates.

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Synthesis and Evaluation of Isatin Analogs as Potential Urease and Tyrosinase Inhibitors: An Approach of Molecular Docking.

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Pipeline Tip

Use local MSA generation (colabfold_search) to bypass speed bottlenecks.

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Deep learning is not a magic wand, but a powerful lens for structural biology. — Recep Adiyaman

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