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Daily Signal February 27, 2026 · 9 min read

Issue #57: Discovery of potent ALK tyrosine kinase inhibitors for thyroid cancer via machine learning modeling, molecular docking, MD simulations, and DFT study.

Protein Design Digest - 2026-02-27 - Discovery of potent ALK tyrosine kinase inhibitors for thyroid cancer via machine learning modeling, molecular docking, MD simulations, and DFT study.

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Discovery of potent ALK tyrosine kinase inhibitors for thyroid cancer via machine learning modeling, molecular docking, MD simulations, and DFT study.

The ever-increasing need for effective therapeutic management of thyroid cancer (TC) necessitates the exploration of novel approaches for advanced drug discovery. The current study employed a robust computational pipeline integrating Machine Learning (ML) algorithms, QSAR modeling, molecular docking, molecular dynamics (MD), density functional theory (DFT), and network pharmacology to identify novel Anaplastic Lymphoma Kinase (ALK) tyrosine kinase inhibitors. An initial library of 3546 compounds from the CHEMBL4247 database was systematically filtered to 578. This screening utilized Lipinski’s rule of five, aided by QSAR and detailed PaDEL descriptor analysis. An ensemble ML model, specifically a Voting Classifier (VC) combining XGBoost, LightGBM, and ExtraTrees algorithms, attained high predictive accuracy (ROC-AUC = 0.99), facilitating a strong classification and prioritization of active leads. Molecular docking experiment identified five top hit ligands (60, 63, 124, 130, 204) having docking score ranging from -9.0 to -10.4 kcal/mol and also confirmed their strong binding affinities, which surpassed the native co-crystallized ligand used as a standard. Later on, ADMET studies were executed to explore their physicochemical properties. MD simulation trajectories and MM/PBSA analyses validated the notably conformational stability and favorable binding free energies of these hit complexes. Network pharmacology was incorporated to understand tentative mechanisms of action and potential off-targets, generating a protein-protein interaction (PPI) network. DFT-based frontier molecular orbital (FMO) analysis showed Ligand124 possessed the highest electrophilicity and optimal polarizability, consistent with its marked interaction stability in MD simulations. In addition, the molecular mechanisms of hit compounds against TC were elucidated using a network pharmacology approach, which revealed a compound-target network with crucial hub targets like AKT1 and TP53. Significant correlations with cancer-related pathways, such as PI3K-Akt and MAPK signaling, as well as key involvement in kinase activity, phosphorylation, and membrane signaling complexes, were observed by the enrichment analysis of the main targets. These comprehensive results imply that investigated hit compounds probably modulate the oncogenic signaling networks, especially those controlling cell survival, proliferation, and drug resistance, in order to achieve its anti-TC therapeutic actions. These findings highlight the fundamental ability of integrating ML and computational chemistry to accelerate therapeutic development for TC.

Why this matters: Enhances small-molecule or peptide docking accuracy for targeted drug discovery.

Also Worth Reading

Development of DHODH inhibitors incorporating virtual screening, pharmacophore modeling, fragment-based optimization methods, ADMET, molecular docking, molecular dynamics, PCA analysis, and free energy landscape.

The overexpression of dihydroorotate dehydrogenase (DHODH) in various malignant tumor cells is significantly associated with ferroptosis, making DHODH inhibition a promising strategy for cancer therapy. In this study, we employed an integrated approach to screen and optimize DHODH inhibitor candidates. First, virtual screening of the FDA-approved drug library identified 20 potential compounds (with the positive control AG-636 as a benchmark, docking score: 133.166). Subsequent pharmacophore modeling (ROC curve value >0.8) further narrowed the candidates to six compounds, which underwent fragment displacement optimization. All optimized compounds were evaluated for absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Molecular docking identified compounds 65:[(4S)-2,2-dimethyl-1,3-dioxolan-4-yl]methyl 3-(4-{[(2S)-2-hydroxypropyl]oxy}phenyl) (docking score: 197.362) and 66: [(4S)-2,2-dimethyl-1,3-dioxolan-4-yl]methyl 4-(4-{[(2S)-2-hydroxypropyl]oxy}phenyl) (docking score: 202.623) as high-affinity candidates. Molecular dynamics (MD) simulations, principal component analysis (PCA), and free energy landscape (FEL) analyses confirmed stable binding conformations for both compounds. Notably, compound 66: [(4S)-2,2-dimethyl-1,3-dioxolan-4-yl]methyl 4-(4-{[(2S)-2-hydroxypropyl]oxy}phenyl) exhibited minimal conformational changes, suggesting superior binding stability. This study advances compound 66: [(4S)-2,2-dimethyl-1,3-dioxolan-4-yl]methyl 4-(4-{[(2S)-2-hydroxypropyl]oxy}phenyl) as a promising DHODH inhibitor candidate through a multimodal workflow integrating structure-based pharmacophore design, fragment optimization, ADMET profiling, and advanced molecular simulations, providing a novel avenue for DHODH-targeted antitumor therapies.

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.

scDock: Streamlining drug discovery targeting cell-cell communication via scRNA-seq analysis and molecular docking

Summary Identifying drugs that target intercellular communication networks represents a promising therapeutic strategy, yet linking single-cell RNA sequencing (scRNA-seq) analysis to structure-based drug screening remains technically challenging and requires substantial bioinformatics expertise. We present scDock, an integrated and user-friendly pipeline that seamlessly connects scRNA-seq data processing, cell–cell communication inference, and molecular docking-based drug discovery. Through a single configuration file, users can execute the complete workflow, from raw scRNA-seq data to ranked drug candidates, without programming skills. scDock automates the identification of disease-relevant ligand–receptor interactions from scRNA-seq data and perfoms structure-based virtual screening against these communication targets using Protein Data Bank (PDB) or AlphaFold-predicted protein structures. The pipeline generates comprehensive outputs at each stage, enabling users to explore intercellular signaling alterations and discover therapeutic compounds targeting specific cell–cell communications. scDock addresses a critical gap by providing an accessible end-to-end solution for communication-targeted drug discovery from single-cell data. Availability and Implementation scDock is freely available at https://github.com/Andrewneteye4343/scDock . It is implemented in R, Python, shell scripts, and supports Linux systems, including Ubuntu and Debian.

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Pipeline Tip

Use GPU-accelerated MD refinement to lift model quality in under 2 hours.

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The protein structure is the language of life; design is its poetry. — Recep Adiyaman

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