Search Results (585)

Computational chemistry has played a central role in early-stage drug discovery by accelerating target selection, hit identification, and lead optimization. This review summarizes recent developments in molecular docking, pharmacophore modeling, molecular dynamics (MD), and virtual screening (VS), with a focus on their application in practical drug discovery workflows. Advances in docking protocols, including consensus scoring, physics-based rescoring, and ensemble approaches, addressed the challenges of receptor flexibility. Both ligand-based and structure-based pharmacophore models facilitated scaffold hopping and guided library prioritization. MD simulations were used to assess binding pose stability, identify cryptic binding pockets, and characterize solvent interactions. These simulations also supported free-energy calculations using endpoint and alchemical methods. Large-scale VS campaigns employed curated compound libraries, often composed of make-on-demand molecules, and relied on high-performance computing or cloud infrastructure to screen up to 10⁹ compounds. Hits were validated using orthogonal biophysical assays and filtered by absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. Integrated pipelines combining pharmacophore modeling, docking, MD, and free-energy calculations improved enrichment rates and reduced the number of compounds requiring synthesis. Several case studies demonstrated the identification of nanomolar-affinity leads from ultra-large screening campaigns. The review also addressed ongoing challenges, such as inconsistent scoring of binding affinity, protonation, and tautomeric errors, dataset bias, and reproducibility issues. Strategies to mitigate these limitations included standardized library preparation, adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, and the use of prospective benchmarking protocols. The review discussed emerging trends, including the use of quantum chemistry for electronic structure refinement, ensemble docking guided by cryo-electron microscopy (cryo-EM) data, and the integration of computational tools with automated synthesis and high-throughput screening in closed-loop discovery systems. These approaches have the potential to accelerate the design–make–test cycle, increase hit novelty, and improve decision-making in early drug development programs. Full article

Background: Triple−negative breast cancer (TNBC) remains among the most aggressive and therapeutically unresponsive subtypes due to the absence of ER, PR, and HER2 targets. Casein Kinase II (CK2), a pleiotropic serine/threonine kinase overexpressed in TNBC, represents a compelling target for rational drug design. Methods: Here, we present an AI−integrated benchmarking framework combining virtual drug discovery, molecular dynamics simulations, machine learning−driven QSAR modeling, and quantum−mechanical electronic structure analysis to identify potent CK2 inhibitors from natural product chemical space. Results: A validated XP docking protocol (ROC–AUC = 0.748) screened ~480,000 compounds, yielding seven hits, with superior binding to the reference inhibitor CX−4945. Among these, Anastatin B, 3,4,8,9,10−pentahydroxy−dibenzo−[b,d]pyran−6−one, Rhein, and aloe emodin acetate exhibited highly favorable docking scores (−11.6 to −13.1 kcal mol⁻¹) and stable 200 ns binding dynamics, reflected by RMSD ≤ 2 Å and compact Rg trajectories. MM−PBSA/MM−GBSA analyses confirmed robust thermodynamic stability, while DFT−derived HOMO–LUMO gaps (3.8–4.3 eV) suggested optimal electronic reactivity for kinase inhibition. Machine learning QSAR models demonstrated strong predictive performance, with the best stacking models achieving test R² ≈ 0.69 and consistent cross−validation performance (CV R² ≈ 0.66–0.69), supporting reliable prediction of pIC₅₀ values and prioritization of top−ranked scaffolds. Conclusions: Collectively, this integrative framework bridges AI−based learning and biophysical validation, establishing a reproducible paradigm for de novo CK2 inhibitor discovery in TNBC. Full article

Graph neural networks (GNNs) have achieved great success in graph classification, with graph pooling methods being widely adopted for related tasks. Existing approaches typically rely on node ranking or clustering to coarsen graphs, but often fail to effectively leverage global structural information, leading to loss of critical substructures and limited interpretability—key limitations in molecular analysis and social network mining. To address these issues, we propose SparsePool, a graph pooling method that integrates node features and structural patterns through atomic decomposition. By dynamically decomposing graphs into interpretable atomic units via Boolean matrix factorization, SparsePool preserves semantically meaningful substructures while providing transparent evidence of retained patterns. We further introduce an Atomic Pooling Neural Network (APNN) for graph representation learning. Extensive experiments on relevant benchmarks including biochemical and social network datasets demonstrate that SparsePool outperforms state-of-the-art pooling methods, achieving an average classification accuracy improvement of 1.03% over baseline models while reducing structural information loss. We also discuss its compatibility with emerging quantum computing paradigms, such as quantum-accelerated sparse decomposition, as a promising direction for scaling graph processing in industrial contexts. Full article

A zinc complex of chelidonic acid (4-oxo-4H-pyran-2,6-dicarboxylic acid) was obtained by reaction with zinc oxide under isothermal conditions. Its composition was confirmed by elemental and thermogravimetric analyses, and its molecular structure was characterized using NMR and IR spectroscopy. Single-crystal X-ray diffraction revealed that the complex crystallizes as a one-dimensional coordination polymer, [ZnChel(H₂O)₄]_n, in the triclinic space group P-1, featuring a distorted octahedral Zn(II) center coordinated by two chelidonate ligands and four water molecules. This six-coordinate arrangement contrasts with previously described tetra-coordinated Zn–chelidonate complexes. Quantum-chemical calculations and molecular dynamics simulations indicated that, in aqueous solution, Zn(II) preferentially forms a monodentate ZnChel(H₂O)₅ species, consistent with the solid-state coordination environment. The interaction of the complex with bovine serum albumin (BSA) was examined by fluorescence, UV–Vis absorption, and circular dichroism spectroscopy, revealing a mixed static–dynamic quenching mechanism, moderate binding affinity, and hydrogen-bonding/van der Waals contributions accompanied by alterations in BSA secondary structure. These results expand the structural chemistry of chelidonic acid and provide biophysical insight into the protein-binding behavior of zinc chelidonate, supporting its potential relevance as a zinc-based bioactive compound. Full article

A classical fluid splitter produces the same patterns of energy redistribution as a Stern–Gerlach quantum device, with rotationally invariant coefficients of correlation between molecular paths. Alternative settings express a cosine squared relationship, leading to Tsirelson-type Bell violations with outcome independence. This result confirms the Correspondence Principle of quantum mechanics, where individual detection events express system-level properties according to Born’s Rule. Kochen–Specker contextuality and Bell Locality are not formally contradicted, but their interpretation is in question. Current definitions of “Local Realism” are limited to intrinsic particle properties. In contrast, quantum-like correlations require the acknowledgement of ensemble effects on dynamically inseparable entities, even when those entities are observed one at a time. Full article

Achieving selective cleavage of specific chemical bonds using ultrafast laser pulses remains a central challenge in ultrafast strong-field molecular physics. Here, we theoretically investigate the coherent control of strong-field dissociation of the heteronuclear molecular ion HD⁺ initially prepared in vibrationally excited states driven by an ultrashort pulse with a quadratic spectral phase. Our results reveal a pronounced sensitivity of both the total dissociation probability and the branching ratio (H⁺ + D vs. H + D⁺) to the chirp rate of the laser pulse. To uncover the underlying physical mechanism, we analyze the population dynamics in the coupled $1s\sigma$ and $2p\sigma$ electronic states and identify pronounced Rabi oscillations arising from the coherent interplay between multiphoton excitation and field-induced stimulated emission. By tuning the laser chirp rate, these oscillations can be suppressed via quantum interference, thereby reshaping the dissociation dynamics and significantly enhancing the dissociation probability of the H + D⁺ channel. These findings demonstrate that spectral-phase engineering provides a robust and versatile strategy for selective control of branching ratios in strong-field molecular dissociation. Full article

Water exhibits unique interfacial properties that arise from the collective organization of its hydrogen-bond network. Establishing clear links between molecular-scale interactions and macroscopic observables remains a central challenge in understanding the behavior of liquid water. In this work, we combine experimental measurements of the contact angle of sessile water drops with quantum-chemical modeling of small water clusters (H₂O)_n (n = 2–6) to explore multiscale effects of hydrogen-bond cooperativity. The cluster calculations reveal a nonlinear, saturating evolution of hydrogen-bond geometries with increasing cluster size, reflecting the onset of cooperative many-body effects. Experimentally, the evolution of the apparent contact angle during evaporation is quantified using both conventional geometry and a non-invasive geometrical-optical method based on analysis of the dark refractive ring, which provides independent validation against conventional goniometric measurements. The evaporation dynamics are further interpreted within the diffusion-limited framework of the Popov model, indicating that the temporal evolution of the apparent contact angle is primarily consistent with geometry-controlled mass loss under diffusion-limited conditions, rather than requiring variations in intrinsic surface energy. By combining macroscopic contact-angle measurements with molecular-level cluster analysis, this study offers a qualitative multiscale perspective in which minimal cooperative hydrogen-bond motifs provide molecular context for interpreting interfacial behavior, without implying direct quantitative prediction of macroscopic interfacial observables. Full article

We investigate covalent bond healing and mechanical property recovery in a cross-linked epoxy vitrimer containing disulfide bonds by combining quantum chemical calculations and molecular dynamics simulations. Quantum chemical calculations based on the GRRM method are first performed to explore energetically accessible post-scission recombination pathways of sulfur-centered radicals generated by disulfide bond cleavage. The resulting energetic ordering of bonding configurations is incorporated into molecular dynamics simulations through recombination rules derived from the quantum chemical calculations, allowing assessment of network repair and mechanical response. The results indicate that sulfur-centered radicals can undergo post-scission recombination via transient interactions with the aromatic ring prior to reformation of the disulfide bond. Tensile simulations further show that disulfide bonds preferentially break compared with other covalent bonds in the cross-linked network. Incorporation of the recombination pathways identified by the quantum chemical calculations leads to enhanced bond reformation and partial recovery of mechanical properties compared with a model assuming direct sulfur–sulfur recombination only. Full article

In the present work, a novel series of eleven sulfonate derivatives with potent inhibitory activity against butyrylcholinesterase (BChE) is reported. Of these, compounds 2-[(E)-(2-Benzoylhydrazinylidene)methyl]phenyl 5-(dimethylamino)naphthalene-1-sulfonate (5c, IC₅₀ = 1.11 µM) and tert-butyl (2E)-2-[(2-{[5-(dimethylamino)naphthalene-1-sulfonyl]oxy}phenyl)methylidene]hydrazine-1-carboxylate (5b, IC₅₀ = 11.51 µM) exhibit stronger inhibitory activity than rivastigmine, the reference compound, and exhibit high selectivity for BChE over AChE (e.g., selectivity index 57 for 5c). Interestingly, compound 5c also exhibited anti-inflammatory effects, which is important for potential therapeutic applications, especially in Alzheimer’s disease. These new compounds were designed through a structure-based approach using molecular modeling techniques (docking, molecular dynamic (MD) simulations, and QTAIM (quantum theory of atoms in molecules) calculations). The most promising compounds show no detectable toxic effects and satisfy Lipinski’s rule of five, indicating that they represent attractive starting structures for the design of new derivatives acting as specific BChE inhibitors. In addition, our results indicate that relatively simple computational techniques such as docking calculations and toxicity prediction programs can be valuable when properly used in the search of new candidates for this particular target. Docking calculations show that the more active compounds of this series reach the bottom region of the gorge interacting with residues within the active site of BChE. However, our data further suggest that the use of more precise techniques, such as MD simulations and QTAIM analysis, is necessary to obtain detailed insight into ligand–enzyme interactions. Regarding QTAIM calculations, they demonstrate that such computations are very useful to evaluate the molecular interactions of the different molecular complexes. In summary, we report a new series of sulfonate derivatives as promising starting structures for the development of new selective BChE inhibitors. Full article

The accurate description of nonadiabatic quantum molecular dynamics represents one of the most significant challenges in modern computational chemistry, serving as a gateway to understanding complex phenomena ranging from photochemistry and electron transfer to surface scattering and biological exciton transport. A key difficulty lies in bridging high-level electronic structure theory for ground and excited states with accurate quantum dynamics theory. Although on-the-fly semiclassical approaches are increasingly viable, most quantum dynamics simulations still rely on pre-constructed potential energy surfaces, or in the nonadiabatic context, diabatic potential energy matrices (DPEMs). This perspective paper addresses the theoretical foundations, construction methodologies, and emerging frontiers of DPEMs. We examine the mathematical framework of the adiabatic-to-diabatic transformation, addressing the inherent topological challenges imposed by the geometric phase and the curl condition. We further analyze the transformative impact of machine learning, detailing how machine learning algorithms, such as permutation invariant polynomial neural networks and deep learning architectures, are reshaping the construction of global, high-dimensional DPEMs. Finally, we explore the disruptive potential of quantum computing, discussing how quantum algorithms are automating the direct simulation of nonadiabatic dynamics. In emerging quantum-centric workflows, DPEMs will continue to provide the critical bridge which enables the mapping of realistic, time-dependent molecular Hamiltonians onto quantum hardware. Full article

Persimmon polyphenols (PP) are natural polyphenols with high reactivity and strong deodorization potential; however, their practical application in odor control is limited by their poor solubility. In this study, natural deep eutectic solvents (NADESs) were employed for the green extraction of PP, and the capabilities of extracts on the removal of ammonia (NH₃) and hydrogen sulfide (H₂S) were investigated. In addition, the underlying mechanisms were explored by integrating spectroscopic analysis, molecular dynamics simulations, and quantum chemical calculations. The results showed that chloride-citric acid (CC-CA) was the optimal system in both PP extraction and sustained NH₃ removal, while the betaine-urea (B-U) system was more effective for H₂S removal. NH₃ removal was governed by acid-base neutralization, with the resulting ammonium species being further stabilized within the PP-regulated NADES hydrogen-bond network. In contrast, H₂S interacted with the solvent network not only through acid-base neutralization but also via Van der Waals forces and hydrophobic contacts. Our data supported that NADESs enhanced the deodorization performance of PP through cooperative microenvironment regulation rather than irreversible chemical conversion. This work highlighted that NADESs could not only function as highly efficient extraction media for polyphenols, but also active platforms for enhancing selective gas-capture capability for polyphenols. Furthermore, it provided a new strategy for the rational design of green, persimmon-derived deodorants. Full article

Deciphering the coupling mechanisms between post-harvest precursor shaping and roasting thermochemistry is pivotal for precise coffee flavor modulation. This study aimed to investigate the regulation mechanisms of in vitro biomimetic fermentation (BF) on the precursor-roasting reaction network. Integrated multi-omics characterization and sensory evaluation reveal that the BF protocol achieves targeted substrate enrichment, notably amplifying free amino acids—particularly leucine and phenylalanine—by 1.89-fold while accumulating lactate and succinate buffering salt systems. This reconfiguration constructs a matrix with superior thermal buffering capacity (ΔpH 0.17), which optimizes the thermal reaction kinetic window during roasting. Consequently, BF drives a 3.08-fold surge in esterification flux, significantly increasing the abundance of key fruity markers such as ethyl acetate and ethyl isovalerate. It also enhances the diversity of Maillard products, specifically elevating nutty-associated alkylpyrazines (e.g., 2,3,5-trimethylpyrazine). Concurrently, BF improves the thermal stability of bioactive compounds, including 5-caffeoylquinic acid (5-CQA) and trigonelline. Multi-scale molecular dynamics and quantum chemical calculations elucidate that BF-derived organic acid–salt complexes exert a ‘pseudo-catalytic effect,’ lowering activation free energy barriers for critical aroma-generating reactions by approximately 8.5 kcal/mol. This study demonstrates high sensory predictability (predictive model R² = 0.98) and provides a quantitative theoretical framework to advance coffee processing from empirical observation to rational flavor design. Full article

Organic mixed ionic–electronic conductors (OMIECs) are pivotal for bioelectronics; however, the microscopic origins of doping-dependent charge transport remain elusive. In this study, we established a multi-scale computational framework to quantify the distinct intramolecular electronic coupling ($H_{ab}$) distributions in systems with 25% and 75% doping levels. Our protocol employs automated quantum chemical calculations to exhaustively identify intrinsic local minima, ensuring thermodynamically stable initial conformations. Subsequent Molecular Dynamics (MD) simulations characterize the equilibration timescales and counter-ion dispersion behaviors. The simulation results reveal that the 75% doped system exhibits significantly stronger counter-ion confinement and a distinct electrostatic landscape compared to the 25% system. Finally, hybrid QM/MM calculations integrated with Constrained Density Functional Theory (CDFT) were utilized to evaluate $H_{ab}$ within these specific environments. The computed coupling distributions show a clear correlation with local electrostatic fluctuations induced by differing counter-ion arrangements. These findings indicate that doping-induced environmental disorder is a critical factor modulating intramolecular transport efficiency, providing a theoretical basis for optimizing OMIEC performance through electrostatic engineering. Full article

The emergence of drug-resistant strains of Plasmodium falciparum continues to challenge global malaria control efforts, underscoring the urgent need for novel therapeutic strategies. In this study, we present an integrative computational framework that combines ensemble machine learning, molecular docking, and molecular dynamics simulations to predict and characterize the antimalarial activity of compounds from the Malaria Box database. Initially, topographical and quantum mechanical descriptors were used to construct regression models for predicting pEC₅₀ values, but due to the limited predictive performance in the global regression, a classification strategy was adopted, categorizing compounds into “active” and “very active” classes. The best ensemble classifier achieved robust performance (Acc₁₀-fold = 0.738, Acc_ext = 0.675), with good sensitivity and specificity over individual models. Subsequent regression modeling within each class yielded high predictive accuracy, with ensemble models reaching Q²10-fold values of 0.810 and 0.793 for the very active and active classes, respectively. To explore potential mechanisms of action, molecular docking was performed against P. falciparum Cytochrome B, revealing strong binding affinities for most compounds, particularly those forming π–π stacking and hydrogen bonds with Glu272. Molecular dynamics simulations over 200 ns confirmed the stability of several ligand–protein complexes, including unexpected behavior from compound M31, which demonstrated stable binding despite poor docking scores, suggesting a possible competitive inhibition mechanism. Binding free energy calculations further validated these findings, highlighting several promising candidates for future experimental evaluation. This integrative approach offers a powerful platform for accelerating antimalarial drug discovery by combining predictive modeling with mechanistic insights. Full article

Mitragyna speciosa, commonly referred to as kratom, is known for its active compound, mitragynine, which is classified as a new psychoactive substance. The availability of mitragynine standards remains a challenge, highlighting the need for effective and efficient methods for isolating this compound from kratom. This study aimed to computationally design a molecularly imprinted polymer (MIP) for the selective isolation of mitragynine. Computational studies were conducted using the B3LYP def2TZVP method with DFT-D4 dispersion, and the results were verified by a laboratory experiment and a molecular dynamics study. The study revealed that methacrylic acid was the optimal monomer for MIP interactions in methanol. Laboratory experiments, employing the association constant and Job plot methods, confirmed that methanol was the ideal solvent for the pre-polymerization complex, with an equilibrium template-to-monomer ratio of 1:3. Radial distribution function analysis from molecular dynamics simulations further supported that the 1:3 template-to-monomer ratio was optimal, aligning with experimental findings. This study’s findings suggest that computational analysis may be employed for the rational design of improved MIPs and for further laboratory investigation into the selective isolation of mitragynine from plants. Full article