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S. Durdağı

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Open access Jul 2026

New 3H-1,2-Dithiole-3-thione derivatives: Design and synthesis, H2S-release profile, in vitro anticancer activity, and in silico multi-target assessment.

Cancer continues to be a leading cause of global mortality, highlighting the ongoing need for novel anticancer compounds that offer high efficacy with improved side effect profiles. In the present study, a series of 3H-1,2-dithiole-3-thione derivatives (DTT-S1-18) were synthesized as promising anticancer agents, and the structures of products were confirmed by spectral techniques. H2S-releasing experiments showed that most of the compounds released higher amounts of H2S slowly over time compared to standard ADT-OH. All compounds were tested for antiproliferative activity on HT-29, PC-3, MCF-7, and HUVEC cell lines. Compounds DTT-S6 (3-nitrophenyl derivative) and DTT-S8 (methionine derivative) have the lowest IC50 values of 41.6 and 38.9 µM on the MCF-7 cell line, respectively. Based on the wound healing and colony formation assays performed in MCF-7 cells, the wound areas were not significantly changed after treatment with compounds DTT-S6 and DTT-S8, whereas compound DTT-S8 at double IC50 dose inhibited colony formation by 81.82%. In addition, molecular docking, MD simulations, MM/GBSA binding free energy calculations, and binary QSAR analyses were performed to explore the potential target interactions and predicted activity profiles of the synthesized compounds toward inflammation-related proteins, including COX-1, COX-2, 5-LOX, and iNOS, thereby supporting the development of mechanistic hypotheses for future validation. Furthermore, structure-activity relationship (SAR) analyses were conducted to correlate the structural characteristics of the synthesized compounds with their H2S releasing potential and biological profiles. Overall, this work integrates experimental anticancer evaluation with computational pathway and structure-based cancer/inflammation analyses to characterize novel DTT-based H2S donors. The findings identify particularly compound DTT-S8, as a promising in vitro anticancer candidate, while the computational results suggest a putative involvement of inflammation-related targets, particularly the COX-2/5-LOX axis, which requires direct biochemical and cellular validation.

Semra Altunsoy, Y. Yilmaz, Tuğba Güngör et al. · 0 citations
Open access Aug 2026

Targeting BCL-2 through Deep Learning-Based Drug Repurposing: A Multimodal Approach Combining Diffusion-Based Generative Modeling, Neural Relational Inference, and In Vitro Validation

Accurate identification of repurposable BCL-2 ligands requires not only plausible bound complex structures but also a dynamic description of how ligand binding reshapes residue-level communication. Here, we present a multimodal BCL-2 repurposing workflow built with diffusion-based generative modeling for ligand-specific complex generation and an extended neural relational inference (NRI) framework for trajectory-level interaction analysis. NeuralPlexer was applied to a library of 3094 FDA-approved drugs to generate BCL-2-ligand complex conformations at scale, yielding 1294 structurally acceptable complexes for downstream prioritization. To complement static scoring, filtered candidates were evaluated by molecular docking, anticancer QSAR classification, all-atom molecular dynamics (MD) simulations, and MM/GBSA binding free-energy calculations. We then extended NRI to protein–ligand trajectories to quantify residue-ligand and residue–residue dynamic couplings, enabling comparison of candidate-specific interaction signatures against the reference BCL-2 inhibitor Venetoclax. Among the prioritized compounds, Relugolix emerged as one of the most compelling hits, combining favorable binding energetics with an NRI-derived interaction pattern closely resembling that of Venetoclax. In vitro experiments supported BCL-2 inhibition by Relugolix in a TR-FRET assay and reduced viability of LN-18 glioma cells (IC50 = 23.55 μM). Together, these results establish a strategy that couples generative complex prediction with graph-based dynamic inference for structure-guided drug repurposing and identify Relugolix as a tractable scaffold for future BCL-2 inhibitor design.

Ehsan Sayyah, H. Tunc, A. Çelebi et al. · 0 citations