Recep Adiyaman
bioinformatics

Issue #18: Integrated QSAR, molecular docking, and dynamics-based discovery of a potent selective HDAC1 inhibitor with therapeutic potential in aggressive cancers.

January 10, 2026 Daily Intelligence
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Integrated QSAR, molecular docking, and dynamics-based discovery of a potent selective HDAC1 inhibitor with therapeutic potential in aggressive cancers.

šŸ§¬ Abstract

This research introduces a comprehensive computational and experimental approach aimed at the systematic design of selective Histone Deacetylase 1 (HDAC1) inhibitors, which hold therapeutic promise for treating aggressive cancers. A comprehensive Quantitative Structure-Activity Relationship (QSAR) model was constructed utilizing 1168 experimentally validated HDAC1 inhibitors, incorporating molecular descriptors associated with hydrogen bonding, steric, and electronic properties. The validated model, with a R 2 of 0.80 and a Q 2 of 0.80, was utilized for the virtual screening of the ChemDiv HDAC library, successfully identifying high-potential hits. The leading compounds underwent receptor-based molecular docking with the HDAC1 crystal structure (PDB ID: 4BKX), which highlighted essential interactions such as zinc ion coordination and Ļ€-Ļ€ stacking. Notably, compound 0356-0096 demonstrated a higher binding affinity than the reference inhibitor vorinostat. Molecular dynamics (MD) simulations conducted over a duration of 500 ns demonstrated the stability of the complex and a decrease in flexibility, as evidenced by analyses of Root Mean Square Deviation (RMSD) and Fluctuation (RMSF). The analysis of simulation trajectories through Principal Component Analysis (PCA) and the mapping of the Free Energy Landscape (FEL) revealed stable low-energy conformations that align with thermodynamically favorable binding conditions. The results of ADMET profiling demonstrated that the lead compounds exhibit good oral bioavailability, low toxicity, and favorable metabolic stability. Validation through in vitro methods using the MTT assay on MDA-MB-231 (triple-negative breast cancer) and A431 (epidermoid carcinoma) cell lines revealed significant, dose-dependent cytotoxic effects, with IC 50 values of 2.7 Ī¼M and 91.6 nM, respectively. The computed Selectivity Index (SI) demonstrated a preferential cytotoxic effect on cancer cells in comparison to normal NRK kidney cells. This integrative QSAR-docking-MD-FEL-MTT approach effectively identified compound 0356-0096 as a potent and selective HDAC1 inhibitor. By combining predictive computational models with empirical validation, it provides a structured pathway for the preclinical development of targeted epigenetic cancer therapeutics.

Why it matters: Expands the searchable sequence space for novel folds and high-affinity binders.

ā­ Additional Signals

Synthesis, Anticancer Evaluation, and Molecular Docking of Triazolylmethyl-Dihydroquinazolinyl Benzoate Derivatives as Potential PARP-1 Inhibitors.

Quinazolinone derivatives have emerged as promising scaffolds in medicinal chemistry due to their broad spectrum of biological activities, including anticancer potential. Incorporation of triazole rings through click chemistry has further boosted the pharmacological relevance of such compounds, due to the triazole’s stability, bioisosterism, and ability to engage in key interactions with biological targets. Motivated by these properties, a library of 24 triazolylmethyl-dihydroquinazolinyl benzoate (TDB) derivatives (7a-x) was synthesized using a click chemistry strategy, starting from anthranilamide and phthalic anhydride. The structures of the synthesized compounds were established through IR, 1 H NMR, 13 C NMR, and HRMS spectral analysis. The anticancer potential of all derivatives was evaluated by using SRB assay, with compounds 7j and 7q displaying notable activity, with GI 50 values of 22 and 48 Āµg/mL, respectively. In addition, compounds 7a, 7e, 7f, 7l, 7u, 7v, and 7x displayed moderate activity, with GI 50 values ranging from 58 to 77 Āµg/mL. In addition, molecular docking studies were performed using poly(ADP-ribose) polymerase-1 as the target enzyme, and the results confirmed that the TDB derivatives exhibited strong binding affinity. Furthermore, molecular dynamics simulations were conducted to evaluate the stability of the docked complexes, specifically for compounds 7j and 7q, which confirmed that the TDB derivatives formed stable interactions with poly(ADP-ribose) polymerase-1.

Mechanistic study of plastic monomers in gestational diabetes mellitus: A network toxicology and molecular docking approach.

Plastics are widely used in various fields such as food packaging, textile fibers, building materials, and transportation. Although the relationship between plastic additives and diseases has been reported, there is limited research on the association between plastic monomers (PM) and gestational diabetes mellitus (GDM). This study aims to investigate the link between environmental PM and GDM. By employing advanced network toxicology and molecular docking techniques, we successfully elucidated the molecular mechanisms by which PM may induce GDM. Utilizing databases such as PubChem, SEA, Super-PRED, SwissTargetPrediction, PharmMapper, Gene Cards, and OMIM, we identified potential targets associated with the disease. Further analysis using STRING and Cytoscape software helped determine the core targets most significantly related to these metabolic disorders. Additionally, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted using the David database to characterize these core targets. Finally, molecular docking with CB-Dock2 was used to validate the binding affinity of PM to these target proteins. Our findings suggest that PM may potentially induce GDM by modulating the insulin signaling pathway through STAT3, AKT1, and TP53. In summary, this work provides novel insights into the mechanisms by which environmental pollutants may trigger GDM, thereby laying a theoretical foundation for disease prevention and treatment. It offers valuable references for the safety evaluation of plastics, urging food safety regulatory agencies to strengthen oversight and encouraging the public to reduce plastic usage.

Protein Language Models Trained on Biophysical Dynamics Inform Mutation Effects

Structural dynamics are fundamental to protein functions and mutation effects. Current protein deep learning models are predominantly trained on sequence and/or static structure data, which often fail to capture the dynamic nature of proteins. To address this, we introduce SeqDance and ESMDance, two protein language models trained on dynamic biophysical properties derived from molecular dynamics simulations and normal mode analyses of over 64,000 proteins. Both models can be directly applied to predict dynamic properties of unseen ordered and disordered proteins. SeqDance, trained from scratch, has attentions that capture dynamic interaction and co-movement between residues, and its embeddings encode rich representations of protein dynamics that can be further utilized to predict conformational properties beyond the training tasks via transfer learning. SeqDance predicted dynamic property changes reflect mutation effect on protein folding stability. ESMDance, built upon ESM2 (Evolutionary Scale Model II) outputs, substantially outperforms ESM2 in zero-shot prediction of mutation effects for designed and viral proteins which lack evolutionary information. Together, SeqDance and ESMDance offer a new framework for integrating protein dynamics into language models, enabling more generalizable predictions of protein behavior and mutation effects. Significance StatementThe sequence–structure (ensemble)–function relationship is central to biology. Protein dynamics in the structure ensemble play a decisive role in determining function and mutation effects, and are widely used to study thermodynamics, folding pathways, and dynamic interactions of ordered proteins, as well as the conformational variability of intrinsically disordered proteins. However, current state-of-the-art protein deep learning models, such as AlphaFold2,3 and ESM, focus on static structures and sequences, which failed to directly capture protein dynamics. Here, we address this gap by developing protein language models to learn dynamic properties of over 64,000 proteins. We show that the models Transformer attentions capture protein dynamic interactions, and our model can be applied to predict conformational properties and mutation effects.

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GC-MS phytochemical screening, molecular docking, MD simulation, and in vitro antioxidant assay of Euphorbia thymifolia L. extract.

Euphorbia thymifolia L., a medicinal herb traditionally recognized for its many medicinal properties, was investigated in the present study to evaluate the antioxidant potential of its hexane fraction and to elucidate its possible mechanism of action through Keap1-Nrf2 pathway modulation using in silico analyses, along with validation through in vitro assays. The plant material was initially extracted using ethanol, followed by fractionation with hexane. The obtained hexane fraction was subjected to GC-MS analysis to identify its phytoconstituents, which were further evaluated through in silico molecular docking and molecular dynamics (MD) simulations against Keap1 to predict their potential to activate Nrf2 signaling. The antioxidant activity of the fraction was subsequently validated using DPPH, nitric oxide scavenging, and reducing power assays. Phytochemical screening and GC-MS analysis confirmed the presence of bioactive fatty acid constituents such as palmitic acid, linoleic acid, and linolenic acid. Antioxidant evaluation using DPPH and nitric oxide radical scavenging assays demonstrated concentration-dependent activity, with ICā‚…ā‚€ values of approximately 204 Āµg/mL and 169.40 Āµg/mL, respectively, which were moderately comparable to the standard. Molecular docking studies revealed notable interactions of the key phytoconstituents with the Keap1 protein, particularly Taraxerol, which exhibited a binding affinity of - 11.6 kcal/mol. Furthermore, a 100 ns molecular dynamics simulation, along with post-simulation analyses, confirmed the stability of the ligand-protein complex. The present study shows that Euphorbia thymifolia L. includes bioactive compounds with significant antioxidant activity, as evidenced by in vitro experiments and Keap1-Nrf2 pathway interactions discovered in silico. The stable ligand-protein interaction shows that it has potential as a natural source for antioxidant therapy development and should be investigated further.

In-silico screening of marine fungal metabolites identifies potential FtsZ inhibitors against MDR-tuberculosis through docking and molecular dynamics analysis.

Tuberculosis (TB) remains a major global health challenge, intensified by the rise of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains of Mycobacterium tuberculosis. The bacterial cell division protein FtsZ, a key GDPase required for cytokinesis, represents a promising target for novel anti-TB therapeutics. This study aimed to identify potential FtsZ inhibitors among marine fungal metabolites using molecular docking, molecular dynamics (MD) simulations, and MM/GBSA analyses. Docking was performed with AutoDock Vina v1.2.0, followed by 200 ns MD simulations using Desmond to evaluate complex stability. Among 100 screened metabolites, Xanalteric acid II showed the strongest binding affinity (- 10.9 kcal/mol), interacting with Arg140 and Thr130 within the active site, outperforming the co-crystallized ligand (- 9.1 kcal/mol) and moxifloxacin (- 7.7 kcal/mol). The FtsZ-Xanalteric acid II complex exhibited stable RMSD and compact radius of gyration throughout the simulation. MM/GBSA analysis revealed a strong binding free energy (Ī”G_bind = - 74.77 Ā± 4.95 kcal/mol), dominated by van der Waals and lipophilic interactions. PCA, FEL, and DCCM analyses confirmed the structural rigidity and energetic stability of the complex. These findings highlight Xanalteric acid II as a promising marine-derived inhibitor of FtsZ and support the potential of marine metabolites in developing next-generation anti-TB agents.

Anticancer potential of Dendrocnide meyeniana through phytochemical profiling, ADMET analysis, molecular docking, and in silico cytotoxicity evaluation.

Phytochemicals are widely explored for cancer therapeutics due to their structural diversity and broad pharmacological activities. This study investigated the phytochemical composition and anticancer potential of Dendrocnide meyeniana using integrated in silico approaches. Gas chromatography-mass spectrometry (GC-MS) and ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) identified 78 compounds, confirming the plant’s rich chemical diversity. Four cancer-related targets- EGFR, p53, MMP7 and CDK8/Cyclin C were selected for molecular docking to identify potential inhibitors. Drug-likeness and ADMET profiling of nine bioactive candidates revealed Cryptotanshinone as the most promising compound, exhibiting favorable pharmacokinetic and safety properties. Molecular docking showed that Cryptotanshinone possessed strong binding affinities toward EGFR ( -8.8 kcal/mol), p53 (-8.7 kcal/mol), MMP7 (-8.7 kcal/mol), and CDK8/Cyclin C (-9.8 kcal/mol), comparable to or exceeding the reference drug Erlotinib (-9.0 kcal/mol for EGFR). Toxicity prediction indicated no hepatotoxic, mutagenic, or cytotoxic effects, though the compound showed potential carcinogenic activity possibly linked to pathway-specific interaction in cell-cycle regulation. Molecular dynamics simulation further validated the stability of the Cryptotanshinone-EGFR complex, exhibiting moderate RMSD values and limited structural fluctuations indicative of stable interactions. Collectively, these findings highlight Cryptotanshinone from D. meyeniana as a promising natural lead for anticancer drug development, characterized by strong binding affinity, favorable pharmacokinetics, and structural stability in silico. Further in vitro and in vivo studies are warranted to confirm its therapeutic efficacy and safety.

Multiparameter optimization of broad-spectrum thymidine phosphorylase inhibition, ADMET properties, molecular docking, and DFT screening of novel pyrazole-based thiadiazole/thiazole derivatives.

Thymidine phosphorylase (TP) is a key enzyme involved in pyrimidine nucleoside metabolism and plays a significant role in angiogenesis, tumor progression, and metastasis. In this study, we synthesized 12 pyrazole-based thiadiazole and thiazole derivatives and evaluated their in vitro TP inhibitory activity. The compounds exhibited IC 50 values ranging from 36.67 Ā± 3.50 Ī¼M to 61.23 Ā± 3.20 Ī¼M, with several derivatives showing comparable or superior activity to the standard inhibitor 7-deazaxanthine (IC 50 = 38.68 Ā± 1.12 Ī¼M). Structure-activity relationship (SAR) analysis revealed that electron-withdrawing halogen substituents on the aromatic ring enhanced TP inhibition, likely due to increased binding affinity at the TP active site. Molecular docking studies highlighted interactions, such as hydrogen bonding, Ļ€-Ļ€ stacking, and hydrophobic interactions, which support the observed inhibitory activity. Density Functional Theory (DFT) studies revealed electronic properties consistent with the observed activity, showing that compounds with smaller energy gaps exhibited higher reactivity. These findings suggest that pyrazole-based thiadiazole/thiazole hybrids are lead candidates for the development of novel TP inhibitors with anticancer applications.

Exploring the Therapeutic Potential of Secondary Metabolites from Pogostemon cablin (Aceh Patchouli Oil) as Anti-Breast Cancer Agents: A Network Pharmacology, ADMET, and Molecular Docking Approach

Abstract Breast cancer remains a major global health challenge with increasing incidence and mortality. Limitations of long-term therapies have driven the search for safer and more effective anticancer agents. Aceh patchouli oil (Pogostemon cablin Benth.) is rich in bioactive compounds, such as terpenoids and sesquiterpenes, with potential anticancer activity; however, its molecular mechanisms remain unclear. This study employed an in silico approach integrating network pharmacology, molecular docking, and ADMET evaluation to assess the multitarget potential of patchouli oil compounds against breast cancer. A total of 714 common targets between patchouli oil compounds and breast cancerā€“related proteins were identified, with AKT1 and EGFR identified as key targets involved in the PI3K/AKT, MAPK, Ras, and estrogen signaling pathways. Docking results indicated that several bioactive compounds could interact with AKT1 and EGFR, including one compound showing a stronger binding affinity toward EGFR than the control compound. ADMET evaluation revealed generally favorable drug-likeness profiles. These findings support the potential of Aceh patchouli oil as a multitarget anti-breast cancer candidate and warrant further in vitro and in vivo validation.

Integrated network pharmacology, and molecular docking characterization of the hepatoprotective properties of dendrobine

Abstract Dendrobium has long been used in traditional medicine to manage liver disorders. Dendrobine is a major alkaloid compound isolated from Dendrobium that has demonstrated promising hepatoprotective activity in preliminary pharmacological studies, but the molecular mechanisms through which it functions remain insufficiently defined. Here, we elucidated the potential mechanisms by which dendrobine mitigates liver injury using an integrative approach that combines network pharmacology with molecular docking. Putative dendrobine-associated targets relevant to liver injury were first predicted through network pharmacology analyses. A protein-protein interaction (PPI) network was subsequently constructed to identify central therapeutic targets. Molecular docking was then applied to validate the interactions between dendrobine and key proteins. Ten core targets were ultimately identified (NR1H4, ESR1, KDR, NOS3, GSK3B, IL-2, CASP3, PRKACA, PPARA and PPARG), and dendrobine showed strong predicted affinity for these proteins. These findings indicate that dendrobine may exert hepatoprotective effects by modulating these central molecular targets and associated signaling proteins. Overall, this work provides a systematic framework for delineating the pharmacological actions and potential protein targets of natural bioactive compounds.

Exploring the Scoring Function Space with Lasso Regression.

Artificial intelligence (AI) successfully integrates several emerging and established techniques to build models to address complex systems, including those from biological sources. In developing novel technologies to address protein-ligand interactions, AI showed relevant results for the structural modeling of protein targets (e.g., AlphaFold) and for building new scoring functions to address intermolecular interactions. Analysis of protein-ligand interactions is central to any docking screen project, and these AI developments have great potential to contribute to speeding up drug discovery and increasing the reliability of the computational methods employed to study intermolecular interactions. In this chapter, we present the Lasso regression method available in the program SAnDReS 2.0 and discuss its application to build a regression model to predict the inhibition of a protein target used in developing anticancer drugs. We explain the scoring function concept to get further insights into developing models to predict binding affinity. We focused our discussions on open-source software and freely accessible databases to build our regression models. Also, we made available all the codes discussed here at GitHub: https://github.com/azevedolab/docking#readme .

Deep contrastive learning enables genome-wide virtual screening.

Recent breakthroughs in protein structure prediction have opened new avenues for genome-wide drug discovery, yet existing virtual screening methods remain computationally prohibitive. We present DrugCLIP, a contrastive learning framework that achieves ultrafast and accurate virtual screening, up to 10 million times faster than docking, while consistently outperforming various baselines on in silico benchmarks. In wet-lab validations, DrugCLIP achieved a 15% hit rate for norepinephrine transporter, and structures of two identified inhibitors were determined in complex with the target protein. For thyroid hormone receptor interactor 12, a target that lacks holo structures and small-molecule binders, DrugCLIP achieved a 17.5% hit rate using only AlphaFold2-predicted structures. Finally, we released GenomeScreenDB, an open-access database providing precomputed results for ~10,000 human proteins screened against 500 million compounds, pioneering a drug discovery paradigm in the post-AlphaFold era.

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The protein structure is the language of life; design is its poetry. ā€” Recep Adiyaman

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