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Daily Signal May 07, 2026 · 11 min read

Issue #103: Discovery of potent ALK tyrosine kinase inhibitors for thyroid cancer via machine learning modeling, molecular docking, MD simulations, and DFT study.

Protein Design Digest #103: Discovery of potent ALK tyrosine kinase inhibitors for thyroid cancer vi…

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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

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.

Simulated construction of tilmicosin nucleic acid aptamers based on molecular docking and molecular dynamics techniques.

Traditional aptamer screening methods often prove ineffective for small molecule targets, primarily due to the inherent structural limitations of such compounds. Their simple architecture, limited functional groups, and restricted spatial complexity drastically reduce the probability of identifying nucleic acid sequences that bind with both high affinity and specificity. Consequently, the screening process becomes inefficient and labor-intensive, frequently failing to yield aptamers of satisfactory performance for practical applications. This represents a significant technical hurdle in expanding the use of aptamers in small-molecule detection and therapeutics. Based on this, this study innovatively proposes an aptamer design method based on single-nucleotide docking assembly, using the small molecule temicloxacin as an example. Through molecular dynamics simulations (50 ns, RMSD convergence threshold of 0.15 nm), the dynamic conformational characteristics of tilmicosin were analyzed. Subsequently, saturated docking was performed on four classes of mononucleotides, screening out 32 high-affinity mononucleotides (atomic contact distance ≤4 Å). Methods such as depth-first search algorithm (DFS) and weighted graph theory model were introduced to obtain the representative single nucleotides of eight classes of functional modules and linkage assembly, and finally 63 non-redundant candidate sequences were screened. Molecular docking results indicate that the optimal aptamer Til-14 exhibits high binding affinity with tilmicosin. with an affinity of 298.16 ± 95.588 nM measured via SYBR Green I fluorescence assay. Colloidal gold colorimetric analysis confirmed its high affinity (Kd = 279.323 ± 87.234 nM) and excellent specificity. This innovative method successfully addresses the key limitations of the traditional SELEX process in screening aptamers for small molecule targets. By enhancing the efficiency and specificity of selection, it not only facilitates the discovery of high-performance aptamers but also establishes a novel, generalizable framework for the construction of nucleic acid aptamers targeting other small molecules.

Molecular docking approaches in mycetoma: Toward improved patient management.

Mycetoma is a neglected tropical disease characterised by chronic, granulomatous inflammation of the subcutaneous tissues, often leading to disfigurement, disability, and significant socioeconomic burdens. Caused by a diverse array of bacterial and fungal pathogens, eumycetoma is predominantly driven by Madurella mycetomatis, and current treatment strategies are limited and often ineffective. Conventional antifungal therapies, such as itraconazole, require prolonged administration, frequently combined with surgical interventions, yet cure rates remain suboptimal, and recurrence is common. The formidable protective grain, comprising microbial material, melanin, and host-derived substances, acts as a physical and biochemical barrier, impeding the penetration and efficacy of drugs. Additionally, issues such as toxicity, resistance, and high costs further complicate management, underscoring the urgent need for novel therapeutic strategies. Recent advancements in computational drug discovery, particularly molecular docking, offer promising avenues to accelerate the identification of effective anti-mycetoma agents. Molecular docking simulates the interaction between small molecules and target proteins, enabling rapid virtual screening of large compound libraries, including natural products, existing drugs, and synthetic molecules, against key pathogenic targets. This structure-based approach helps prioritise candidates with high binding affinity, guiding subsequent experimental validation and reducing both time and financial costs associated with traditional drug development. When integrated with artificial intelligence (AI) and machine learning (ML), these methods can enhance predictive accuracy, uncover novel bioactive scaffolds, and facilitate the repurposing of FDA-approved drugs such as montelukast and vilanterol. Key molecular targets in M. mycetomatis include enzymes and pathways critical for pathogen survival and virulence, notably cytochrome P450 (CYP51), dihydrofolate reductase (DHFR), chitin synthase, melanin biosynthesis pathways, and metal ion acquisition systems. Melanin production, via DHN-melanin, DOPA-melanin, and pyomelanin pathways, contributes to grain pigmentation and structural integrity, while metal ions such as iron and zinc are vital for enzymatic activities, grain formation, and fungal virulence. Disrupting metal ion homeostasis through targeting zincophores, siderophores, and zinc-binding proteins represents a promising therapeutic strategy to weaken grain robustness and enhance drug penetration. Despite the potential of molecular docking, limitations such as reliance on homology models, static protein structures, and the absence of cellular context necessitate complementary approaches, including molecular dynamics simulations and in vitro validation. These combined efforts can refine candidate compounds, optimise binding affinities, and predict pharmacokinetic properties. Furthermore, integrating docking results with clinical data and global collaboration platforms can accelerate the discovery of affordable, effective treatments tailored to endemic regions. In conclusion, leveraging molecular docking and computational methods to target essential M. mycetomatis pathways offers a promising frontier in mycetoma research. By identifying novel inhibitors and understanding pathogen biology at a molecular level, these approaches can inform targeted therapies, reduce treatment durations, and improve patient outcomes. Future research should focus on validating computational predictions experimentally and translating these findings into clinical practice, with an emphasis on accessible, cost-effective interventions for vulnerable populations affected by this neglected disease.

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Normalise thermal B-factors when comparing different crystal structures.

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