Recep Adiyaman
bioinformatics

Issue #24: In Silico Discovery of RIOK3 Inhibitors Against Pancreatic Ductal Adenocarcinoma Using Homology Modelling, Molecular Docking, Molecular Dynamics Simulations, ADMET Prediction, and MTT assay

January 16, 2026 Daily Intelligence
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In Silico Discovery of RIOK3 Inhibitors Against Pancreatic Ductal Adenocarcinoma Using Homology Modelling, Molecular Docking, Molecular Dynamics Simulations, ADMET Prediction, and MTT assay

🧬 Abstract

Abstract Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer strongly linked to RIO Kinase 3 (RIOK3), which promotes progression by stabilizing and phosphorylating Focal Adhesion Kinase (FAK). Advances in protein structure prediction, particularly AlphaFold2, have significantly enhanced our understanding of protein dynamics, aiding in the identification of potential inhibitors for targeted therapies. This study used structure-based virtual screening, molecular dynamics simulations, ADMET/toxicity prediction, and in vitro validation to identify potential inhibitors of RIOK3 for PDAC treatment. The 3D structure of RIOK3 was predicted using AlphaFold2 and docked with FDA-approved drugs via AutoDock Vina. Pharmacokinetic and pharmacodynamic properties were assessed with SwissADME, and in vitro validation was performed using MTT assays to assess cell viability and growth inhibition. Four top-scoring compounds were identified, with binding energies between − 11.3 and − 10.4 kcal/mol. Venetoclax showed the most stable complex with RIOK3, followed by Conivaptan and Irinotecan. Drospirenone showed weaker binding. Molecular dynamics simulations and MM/GBSA analysis supported the stability of these complexes. SwissADME and ProTox-II confirmed that the compounds met drug-likeness criteria but exhibited distinct pharmacokinetic and toxicity profiles. In vitro MTT assays showed concentration-dependent growth inhibition in PANC-1 cells, with Conivaptan having the lowest IC₅₀ value. This study identifies RIOK3 as a promising therapeutic target for PDAC, with Venetoclax, Conivaptan, Drospirenone, and Irinotecan as repurposable candidates for further research. Further studies should include biochemical assays, expanded cytotoxicity profiling in multiple PDAC cell lines, and in vivo evaluations to validate RIOK3-targeted therapies for PDAC treatment.

Why it matters: Critical for improving fold accuracy and reducing structural uncertainty in de novo design.

⭐ Additional Signals

Benchmarking all-atom biomolecular structure prediction with FoldBench.

Accurate prediction of biomolecular complex structures is fundamental for understanding biological processes and rational therapeutic design. Recent advances in deep learning methods, particularly all-atom structure prediction models, have significantly expanded their capabilities to include diverse biomolecular entities, such as proteins, nucleic acids, ligands, and ions. However, comprehensive benchmarks covering multiple interaction types and molecular diversity remain scarce, limiting fair and rigorous assessment of model performance and generalizability. To address this gap, we introduce FoldBench, an extensive benchmark dataset consisting of 1522 biological assemblies categorized into nine distinct prediction tasks. Our evaluations reveal critical performance dependencies, showing that ligand docking accuracy notably diminishes as ligand similarity to the training set decreases, a pattern similarly observed in protein-protein interaction modeling. Furthermore, antibody-antigen predictions remain particularly challenging, with current methods exhibiting failure rates exceeding 50%. Among evaluated models, AlphaFold 3 consistently demonstrates superior accuracy across the majority of tasks. In summary, our results highlight significant advancements yet reveal persistent limitations within the field, providing crucial insights and benchmarks to inform future model development and refinement.

OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding

Modern coding scaffolds turn LLMs into capable software agents, but their ability to follow scaffold-specified instructions remains under-examined, especially when constraints are heterogeneous and persist across interactions. To fill this gap, we introduce OctoBench, which benchmarks scaffold-aware instruction following in repository-grounded agentic coding. OctoBench includes 34 environments and 217 tasks instantiated under three scaffold types, and is paired with 7,098 objective checklist items. To disentangle solving the task from following the rules, we provide an automated observation-and-scoring toolkit that captures full trajectories and performs fine-grained checks. Experiments on eight representative models reveal a systematic gap between task-solving and scaffold-aware compliance, underscoring the need for training and evaluation that explicitly targets heterogeneous instruction following. We release the benchmark to support reproducible benchmarking and to accelerate the development of more scaffold-aware coding agents.

MetalloDock: Decoding Metalloprotein-Ligand Interactions via Physics-Aware Deep Learning for Metalloprotein Drug Discovery.

Accurate prediction of metalloprotein-ligand interactions is critical for metalloprotein-targeted drug discovery. Conventional docking tools and existing deep learning (DL) models fail to reliably capture metal-ligand interactions, hampering the discovery of potent metalloprotein inhibitors. Here, we propose MetalloDock, the first DL-based docking framework specially designed for metalloprotein targets. By innovatively integrating an autoregressive spatial decoding engine with a physics-constrained geometric generation paradigm, MetalloDock can precisely reconstruct metal coordination geometries and accurately capture metal-ligand interactions, which enhance both the accuracy of metalloprotein-ligand docking and binding affinity prediction. Extensive evaluations on our custom-built benchmark data set demonstrate that MetalloDock outperforms existing methods, including AlphaFold3, in docking success rate and virtual screening performance for metalloprotein targets. In real-world applications, MetalloDock successfully identified multiple novel hit compounds in a virtual screening campaign targeting the prostate-specific membrane antigen. Additionally, it enabled rational drug design for acidic polymerase endonuclease, leading to the discovery of potent inhibitors. These results highlight the broad applicability of MetalloDock in accelerating metalloprotein-targeted drug discovery and provide a standardized framework for future evaluation of metalloprotein-specific docking algorithms.

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🏢 Industry Insight & Applications

⚡ Quick Reads

Shaping a pro-carcinogenic hepatic microenvironment by TCDD: An integrated approach combining network toxicology, machine learning, molecular docking, molecular dynamics and experimental validation.

The increasing prevalence of environmental contaminants has raised concerns regarding their potential contribution to hepatic dysfunction and associated diseases. 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), classified as a Group 1 carcinogen and the most toxic congener of dioxins, has been implicated in adverse hepatic outcomes. However, the molecular mechanisms by which TCDD-driven signaling cascades establish a pro-carcinogenic microenvironment in the liver remain insufficiently elucidated. By integrating network toxicology with machine learning, CYP1A2, CYP2C9, and HSP90AB1 were identified as the core targets of TCDD-elicited hepatocellular carcinoma (HCC). Stable complex formation between TCDD and each target, exhibiting low conformational flexibility and robust binding affinity, was revealed through molecular docking and molecular dynamics simulations. Subsequently, TCDD-elicited hepatotoxic effects were predominantly demonstrated by Protein-Protein Interaction (PPI), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and immune infiltration analyses to be mediated via activation of chemical carcinogenesis-receptor signaling pathways, perturbation of lipid metabolism, and disruption of immune microenvironment homeostasis. In male C57BL/6 J mice, TCDD exposure was associated with significantly elevated serum markers indicative of hepatocellular injury, metabolic dysfunction, systemic inflammation, and pre-neoplastic transformation, together with markedly disturbed oxidative-stress indices. Then, histopathological examination revealed disrupted hepatic cords, collagen deposition, lipid accumulation, and widespread apoptosis, further complemented by glycogen metabolic disturbances, enhanced proliferation, and elevated pre-neoplastic biomarkers, which collectively established a pro-carcinogenic hepatic microenvironment. Immunofluorescence results indicated significant promotion of M1 (pro-inflammatory) macrophage polarization and suppression of M2 (anti-inflammatory) phenotypes, resulting in an increased M1/M2 ratio and a pro-inflammatory microenvironment. Consistently, down-regulation of CYP1A2 and CYP2C9 and up-regulation of HSP90AB1 were shown by immunofluorescence/Western blotting/RT-qPCR, impairing signaling networks and immune homeostasis and ultimately leading to the establishment of hepatotoxicity and carcinogenic microenvironments. Collectively, the TCDD “target binding-pathway dysregulation-immune imbalance-pathological damage” cascade has been systematically delineated, providing novel targets and a theoretical framework for therapeutic intervention against pollutant-associated liver diseases.

In Silico Identification of Lepiotaprocerin C as a Promising PIM-1 Kinase Inhibitor: An Integrated Docking, Molecular Dynamics, MM/PBSA, QSAR, and ADMET Study.

Proviral Integration site for Moloney murine leukemia virus-1 (PIM-1) kinase, a serine/threonine kinase overexpressed in various malignancies, plays a critical role in promoting cell survival and proliferation, making it a promising target for anticancer therapy. This study employed an integrated in silico approach to evaluate Lepiotaprocerin derivatives (A to L) from Macrolepiota procera as potential PIM-1 inhibitors. Molecular docking of 12 Lepiotaprocerins revealed Lepiotaprocerin C as the most potent compound, exhibiting superior binding affinity (-11.4 kcal/mol) compared with the reference inhibitor AZD1208. Binding site validation using CASTp, PrankWeb, and blind docking confirmed the ATP-binding pocket as the active cavity. The Lepiotaprocerin C-PIM-1 complex demonstrated enhanced stability during 200 ns molecular dynamics simulations, maintaining low RMSD and strong hydrogen-bond interactions, supported by a favorable Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) binding free energy (-22.0 ± 2.1 kcal/mol). Based on quantitative structure activity relationship (QSAR) analysis, the calculated pIC 50 value of Lepiotaprocerin C was 8.67. QSAR modeling ( R 2 = .74, Q 2 = 0.90) confirmed robust predictive capacity, while absorption, distribution, metabolism, and elimination and PerMM analysis indicated favorable pharmacokinetic and permeability profiles. Prediction of Activity Spectra for Substances and toxicity predictions further revealed high antineoplastic potential (Pa = 0.881) and a nontoxic safety profile. These results highlight Lepiotaprocerin C as a promising, stable, and safe inhibitor of PIM-1 kinase, warranting further in vitro and in vivo validation for potential anticancer drug development.

Evaluation of Drug-Excipient Compatibility of Ibuprofen with Eggshell-Derived Calcium Citrate Using FTIR, DSC, and Molecular Docking Studies

Abstract Ethnomedicinal Relevance : The use of eggshells for nutritional and medicinal purposes has long been documented in African folklore, where crushed or powdered shells are traditionally administered to enhance bone strength, treat calcium deficiency, and promote general well-being. Despite this ethnomedicinal relevance, their potential application as pharmaceutical excipients remains underexplored. Background : Drug-excipient incompatibilities are critical considerations in the development of stable and effective pharmaceutical formulations. This study investigated the compatibility of ibuprofen with eggshell-derived calcium citrate using Fourier-transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and molecular docking approaches. Methods : Calcium citrate was prepared from chicken eggshells via reaction with citric acid and characterised. Binary mixtures of ibuprofen and calcium citrate were evaluated for potential interactions using FTIR and DSC. In silico molecular docking studies were conducted using AutoDock Vina, and docking methodology was validated using re-docking of a known ibuprofen-calcium interaction. Results : FTIR spectra of the binary mixtures showed minor peak shifts, particularly at 1710 cm⁻¹ (C=O) and 3300 cm⁻¹ (O-H), suggesting weak physical interactions. DSC thermograms demonstrated slight broadening and depression of the ibuprofen melting endotherm, indicating no significant incompatibility. Molecular docking revealed a binding affinity of -4.7 kcal/mol, primarily mediated by ionic interactions between ibuprofen’s carboxyl group and calcium ions. Conclusion : Ibuprofen exhibits acceptable compatibility with eggshell-derived calcium citrate. These findings suggest its potential as a sustainable and cost-effective pharmaceutical filler in oral drug formulations.

Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulations to Elucidate the Potential Mechanism of Ermiao San in Osteoarthritis.

This study aims to identify the active components and molecular mechanisms of Ermiao San (EMS) in the treatment of osteoarthritis (OA) through network pharmacology, molecular docking, and molecular dynamics simulations. EMS compounds and their targets were retrieved from TCMSP; OA-related targets were collected from five public databases. Potential drug-disease target interactions were analyzed using STRING 12.0 and Cytoscape 3.10.2. Functional and pathway enrichment analyses were performed on the 90 overlapping targets. Molecular docking was performed with CB-Dock2 and LigPlot+ v2.2.8 platforms, followed by a comprehensive evaluation of key compounds via SwissADME. We identified 46 active chemicals, 187 EMS-specific targets, and 1718 OA-related targets, with 90 overlapping targets. Molecular docking and molecular dynamics simulations analysis revealed a strong binding potential of key EMS compounds to target proteins. These findings suggest that EMS exerts its anti-OA effects through multicomponent, multi-target, and multipathway interactions.

Network pharmacology, molecular docking and molecular dynamics simulation suggest CE-326597 as an antimalarial molecule.

Malaria remains a major health challenge, intensified by the spread of drug-resistant strains. To address this, we explored natural products and derivatives for antimalarial potential using in silico approaches. Through a similarity-based optimization strategy, molecular docking (Vina) and deep learning-based screening (Gnina) identified CE-326597 as a strong binder of Plasmodium falciparum enoyl-ACP reductase. Molecular dynamics simulations with a self-assembly setup showed spontaneous binding, with the ligand approaching the target from bulk solvent within 15 ns and maintaining stable interactions throughout 200 ns. Network pharmacology revealed that CE-326597 disrupts key parasite pathways (SRC, EGFR, ESR1) and modulates host receptors (HTR1A, HTR7, DRD4) linked to immune regulation. With a favorable safety profile from Phase 1 clinical trials for obesity, CE-326597 emerges as a promising repurposing candidate for malaria. Further in vitro and in vivo studies are warranted to confirm its therapeutic potential. Supplementary information The online version contains supplementary material available at 10.1007/s40203-025-00508-0.

Calycosin ameliorates high-altitude pulmonary edema by regulating macrophage polarization through the PPAR-γ/NF-κB pathway: a comprehensive analysis of network pharmacology, molecular docking, and experimental validation.

The rapid ascent to high-altitude regions poses a substantial risk for the development of high-altitude pulmonary edema (HAPE), a serious condition characterized by non-cardiogenic pulmonary edema and associated acute pulmonary hypertension. Calycosin, a flavonoid compound derived from Astragalus mongholicus Bunge, has documented antioxidant and anti-inflammatory properties; however, its protective role and mechanistic actions against HAPE have not been fully elucidated. This study aimed to investigate the prophylactic benefits of calycosin against HAPE and to delineate its underlying mechanism, with a focus on macrophage polarization via the PPAR-γ/NF-κB signaling axis. Rats were allocated into six groups (n=6) and administered corn oil (vehicle), calycosin (20 and 40 mg/kg), or dexamethasone (4 mg/kg) before exposure to a hypobaric chamber simulating 6000 m altitude. The protective effects of calycosin were assessed through comprehensive evaluations, including hemodynamic measurements, arterial blood gas analysis, lung wet/dry weight ratio, histopathological assessment, and quantification of inflammatory cytokines and oxidative stress parameters. To investigate the underlying mechanism, potential therapeutic targets of calycosin related to HAPE were identified using multiple drug and disease databases (DrugBank, SwissTargetPrediction, GeneCards, OMIM). Core targets were prioritized through PPI network construction, functional enrichment (GO and KEGG), and molecular docking. The predicted interaction was further assessed by molecular dynamics simulations. Key findings from the bioinformatic analysis were subsequently validated in vivo and in vitro (via a PPAR-γ-knockdown cell model) by immunofluorescence and Western blot analysis. Calycosin administration alleviated hypoxia-induced impairments in pulmonary hemodynamics, right ventricular stress (as reflected by BNP levels), lung edema, and tissue injury. It also rebalanced pro- and anti-inflammatory cytokine levels (TNF-α, IL-6; TGF-β, IL-10) and reduced oxidative stress (evidenced by HIF-1α and MDA suppression, and GSH-Px restoration). Bioinformatics analysis identified 16 common targets, with TNF, PPARG, EGFR, and ESR1 as core genes. Enrichment outcomes highlighted immunomodulation and nuclear receptor signaling as key mechanisms. Molecular docking indicated a high-affinity interaction between calycosin and PPAR-γ (binding energy: -8.8 kcal/mol), the stability of which was further confirmed by molecular dynamics simulations. Experimental validation confirmed that calycosin enhanced PPAR-γ expression, impeded NF-κB p65 nuclear translocation, and facilitated a shift in macrophage polarization from the M1 to the M2 phenotype. Calycosin demonstrates protective effects against HAPE, significantly ameliorating the pathophysiological process including the associated acute pulmonary hypertension. The underlying mechanism is mediated through the upregulation of PPAR-γ, subsequent inhibition of NF-κB signaling, and reprogramming of macrophage polarization. These findings nominate calycosin as a prospective natural candidate for preventing HAPE.

Virtual screening of sweet peptides from milk protein and molecular dynamics simulations mechanism analysis.

Bioactive peptides derived from milk proteins have attracted increasing interest due to their potential as natural sweet-tasting compounds. In this study, an integrated in silico strategy was developed to identify sweet peptides from milk proteins. The approach combined machine learning models capable of predicting both sweet and bitter taste properties to improve the accuracy of peptide selection. Peptides generated from virtual enzymatic hydrolysis were screened using 3 machine learning models. Candidates predicted to be sweet and nonbitter were virtually screened to evaluate their binding affinity to the human sweet taste receptor T1R2/T1R3. Peptides with favorable docking scores were further evaluated for their pharmacokinetic properties using computational prediction tools. Based on the combined results of docking and an absorption, distribution, metabolism, excretion, and toxicity assessment, 5 peptides (MDG, MKG, TSG, CDSS, and DSTT) were selected for further analysis. Molecular docking interaction analysis revealed that hydrogen bonding and π-π stacking were the predominant interaction modes at the binding site. Molecular dynamics simulations confirmed the structural stability of the 5 complexes, with MDG, CDSS, and DSTT showing reduced binding fluctuation and minimal receptor conformational changes, suggesting stronger binding stability. Electronic tongue analysis validated the presence of detectable sweet taste signals for all 5 peptides. Among them, MDG, CDSS, and DSTT demonstrated particularly stable interactions and clear sweetness responses, highlighting their potential as candidates for natural sweetener development. This study presents a practical computational framework for the efficient screening and evaluation of sweet peptides from milk protein sources. The proposed strategy may support the discovery of dairy-derived sweeteners with potential applications in sugar-reduced or functional dairy products.

DeFlow: Decoupling Manifold Modeling and Value Maximization for Offline Policy Extraction

We present DeFlow, a decoupled offline RL framework that leverages flow matching to faithfully capture complex behavior manifolds. Optimizing generative policies is computationally prohibitive, typically necessitating backpropagation through ODE solvers. We address this by learning a lightweight refinement module within an explicit, data-derived trust region of the flow manifold, rather than sacrificing the iterative generation capability via single-step distillation. This way, we bypass solver differentiation and eliminate the need for balancing loss terms, ensuring stable improvement while fully preserving the flow’s iterative expressivity. Empirically, DeFlow achieves superior performance on the challenging OGBench benchmark and demonstrates efficient offline-to-online adaptation.

💡 Pipeline Tip

Always validate pLDDT scores before using AlphaFold models for docking.

🛠️ Resources

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

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