Proteins are known to play multifunctional role including transportation, storage, structural support among others. Among the proteins, hemoglobin (Hb) is known to transport oxygen and binds with many xenobiotics and therapeutic drugs. But, in this paper, the binding of human hemoglobin (HHb) with Ivermectin (IVM) that is a broad-spectrum antiparasitic agent used during COVID-19 pandemic has been presented. Multi-spectroscopic experimental techniques and in-silico docking methods have been used for the intended study. IVM quenched HHb fluorescence via a dynamic quenching process with a quenching constant (Ksv) on the order of 105 M− 1. TCSPC studies confirmed the dynamic quenching by showing a decrease in the lifetime of the fluorophore (HHb) in presence of IVM. The binding affinity (Kb) from UV-Vis and fluorescence studies was on the order of 105 M− 1. The binding constant was observed to increase with rise in temperature. Thermodynamic parameters of the interaction (ΔG⁰, ΔH⁰, ΔS⁰) indicated a spontaneous binding process with negative free energy changes and an entropy driven process due to T∆S0 > ∆H0. A positive value of both enthalpy (ΔH⁰) and entropy (ΔS⁰) changes accompanying the binding showed the driving role of hydrophobic forces in the interaction. Synchronous fluorescence results revealed no considerable changes in the microenvironment of tryptophan (Trp) and tyrosine (Tyr). A visible conformational variation in the secondary structure of HHb especially the α-helix was evident from 3D-fluorescence and circular dichroism (CD) studies. Theoretical docking studies showed the binding site of IVM in the central cavity of HHb. The limitation of this study includes the lack of molecular dynamics simulations to model real-time stability of the complex.
Dermatophytic infections remain a persistent therapeutic challenge, particularly due to the increasing resistance to conventional antifungal agents. In search of alternative antifungal scaffolds, a series of 2-pyridyl–imine derivatives (PYE1–PYE11) was synthesized and evaluated for antimicrobial, cytotoxic, and biomolecular interaction properties. All compounds exhibited antimicrobial activity, with the ortho-vanillin derivative PYE10 showing the highest potency, including remarkable activity against Proteus mirabilis (MIC = 0.01 mg/mL) and Trichophyton mentagrophytes (MIC = 0.05 mg/mL). Fluorescence displacement assays provided evidence for PYE10–DNA interactions involving both intercalative and minor-groove-associated binding modes, while human serum albumin binding studies revealed 1:1 complex formation, indicating appreciable interaction with HSA. Molecular docking and molecular dynamics simulations identified favorable and stable binding of the most active compounds within the CYP51B active site, supporting the experimentally observed antifungal activity. Computational thermodynamic analysis indicated that Fe2⁺ chelation is protonation-dependent under physiological conditions. Structure–activity relationship analysis revealed that antimicrobial activity is influenced by the electronic nature of substituents, aromatic planarity, molecular flexibility, and the ability to establish multiple intermolecular interactions. In silico ADMET evaluation demonstrated favorable drug-likeness and pharmacokinetic profiles, highlighting these pyridyl–imine derivatives as promising candidates for further antimicrobial development.
A new approach for the prioritized hit compound optimization directions at the hit-to-lead (H2L) stage is proposed in this study. The high variability in the level of expert assessment among researchers and inefficient resource management during the H2L stage exert a strong influence on the success probability of the overall drug discovery process, which highlights the demand of implementing decision support systems (DSS) designed to integrate the advantages of various commonly accepted practices. Within the framework of the study, an algorithm was developed that combines methods of structure-based drug design (SBDD) with a reverse fragment-based drug design (R-FBDD) approach, allowing for the formation of rationalized recommendations for hit molecule growth directions based on the analysis of fragment contributions to the binding energy and the estimation of fragment group efficiency (GE). Retrospective application of the method to a series of Notum carboxylesterase inhibitors demonstrated a good correlation between the prioritized growth vectors proposed by the algorithm and the experimentally implemented H2L optimization strategies, confirming the potential of the proposed approach as a decision support tool at the hit compound optimization stage.
The rapid expansion of biomedical literature has led to a growing number of contradictory pharmacological reports, where the same drug interactions are described with opposing outcomes across studies. Critically, many such reports are only apparent contradictions arising from mismatched experimental conditions such as dosage, strain, endpoint, or population, rather than logically incompatible claims under comparable settings. These inconsistencies complicate drug safety evaluation and hinder the reliability of automated biomedical knowledge systems. In this paper, we introduce PARADOX, a framework for the detection and analysis of pharmacological contradictions in scientific text. The system integrates BioBERT-based embeddings, a Siamese similarity model, and an iterative refinement using a snowball process to progressively extract and refine conflicting relational statements. Each candidate contradiction is semantically verified using GPT-4o-mini, producing a structured contradiction graph derived from validated outputs. Beyond detection, we introduce a three-stage decision-tree protocol to distinguish real contradictions from apparent ones and outline a conditional biological modulation map that summarizes how identical pharmacological inputs may yield divergent outcomes under different conditions. On more than 18,000 evaluated pairs, the model recovered 39 validated contradiction candidates with only three misses. Qualitative analysis shows both genuine logical conflicts and opposing statements better explained by contextual differences or missing metadata. The results highlight the potential of contradiction-aware mining to strengthen pharmacovigilance, enhance literature consistency checking, and support more robust biomedical reasoning.
ADCs rely on linker chemistry to balance systemic stability with conditional activation. Glycoengineering provides additional design opportunities by incorporating terminal sialylated glycans that support multimeric interactions and PEG conjugation. Here, we applied an integrated computational workflow combining structural prediction with AlphaFold3, glycan construction with GLYCAM, docking with DiffDock, and all-atom free-energy perturbation (FEP) simulations in GROMACS using a double-decoupling framework to evaluate the effects of three Fc-linked glycan architectures: a short sialic acid glycan, a branched sialylated glycan with a PEG-accessible branch, and a branched sialylated glycan incorporating both ROS-cleavable branch and a PEG-accessible branch, on Pertuzumab in complex with HER2. Short glycans conferred modest stabilization but limited conformational engagement, while branched variants produced the strongest stabilization, linking structural effects with PEG accessibility. In branched variants, the ROS-responsive branch exhibited reduced stabilization consistent with a conditionally labile architecture, while the PEG-accessible branch remained structurally intact. These findings demonstrate how glycan branching and linker architecture influence relative Fc stabilization and structural behavior within modeled systems and support the use of an integrated computational workflow combining structural modeling, docking, and free-energy calculations to prioritize complex linker chemistries for further experimental validation.
