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Single-component organic solar cells (SCOSCs) have attracted extensive attention due to their simplified device manufacturing and excellent stability. However, the relationship between morphology and charge carrier mobility in the active layers of SCOSCs is not well understood. In this work, we present a comprehensive investigation on this issue by studying four dyads (fullerenes as acceptor units) used as materials of active layers in small-molecule single-component organic solar cells (SM-SCOSCs), in which dyad 4 created the record of power conversion efficiency (PCE) of SM-SCOSC until now. Utilizing a multiscale theoretical approach, the results identify that the acceptor-acceptor stacking is dominant in amorphous films, significantly improving electron mobility and lowering hole mobility. We also find the importance of achieving a balance between electron and hole mobility to further improve PCE of SM-SCOSC because dyad 4 exhibits a more balanced electron/hole mobility than the other three molecules. These findings indicate the importance of tuning and enhancing donor-donor and acceptor-acceptor stacking simultaneously, offering insights for the design and optimization of future SM-SCOSC.

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In the present work, molecular dynamics simulation is applied to evaluate the drug carrier efficiency of graphene oxide nanoflake (GONF) for loading of Selinexor (SXR) drug as well as the drug delivery by 2D material through the membrane in aqueous solution. In addition, to investigate the adsorption and penetration of drug-nanocarrier complex into the cell membrane, well-tempered metadynamics simulations and steered molecular dynamics (SMD) simulations were performed. Based on the obtained results, it is evident that intermolecular hydrogen bonds (HBs) and π-π interactions play a significant role in expediting the interaction between drug molecules and the graphene oxide (GO) nanosheet, ultimately resulting in the formation of a stable SXR-GO complex. The Lennard-Jones (L-J) energy value for the interaction of SXR with GONF is calculated to be approximately -98.85 kJ/mol. In the SXR-GONF complex system, the dominant interaction between SXR and GONF is attributed to the L-J term, resulting from the formation of a strong π-π interaction between the drug molecules and the substrate surface. Moreover, our simulations show by decreasing the distance of GONF with respect to cell membrane, the interaction energy of GONF-membrane significantly decrease to -1500 kJ/mol resulting in fast diffusion of SXR-GONF complex toward the bilayer surface that is favored opening the way to natural drug nanocapsule.

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In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance the accuracy and interpretability of ML models. Our current study focuses on accurately predicting viscosity in liquid systems using MD descriptors. In this work, we curated a comprehensive dataset of over 4000 small organic molecules' viscosities from scientific literature, publications, and online databases. This dataset enabled us to develop quantitative structure-property relationships (QSPR) consisting of descriptor-based and graph neural network models to predict temperature-dependent viscosities for a wide range of viscosities. The QSPR models reveal that including MD descriptors improves the prediction of experimental viscosities, particularly at the small data set scale of fewer than a thousand data points. Furthermore, feature importance tools reveal that intermolecular interactions captured by MD descriptors are most important for viscosity predictions. Finally, the QSPR models can accurately capture the inverse relationship between viscosity and temperature for six battery-relevant solvents, some of which were not included in the original data set. Our research highlights the effectiveness of incorporating MD descriptors into QSPR models, which leads to improved accuracy for properties that are difficult to predict when using physics-based models alone or when limited data is available.

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Using solid nanoparticles (NPs) as catalysts is the most effective method to achieve catalytic growth of single-walled carbon nanotubes (SWCNTs) with ultrapure chirality. Until now, SWCNTs with a suitable chirality purity have not been prepared in experiments. That is, the evolution of solid NPs during the catalytic growth of SWCNTs is in contradiction with the original concept of a changeless structure. Hence, in this work, the evolution mechanism of solid cobalt NPs during the nucleation process of SWCNTs is analyzed through molecular dynamics. Similar to the experimental observations, the results show that a drastic structural fluctuation of the NPs occurs during the nucleation of SWCNTs. This structural fluctuation is caused by the fact that the elastic strain energy and surface energy of the NPs can be tuned when a carbon gradient exists between the subsurface and interior of the NP. Furthermore, such a carbon gradient can be reduced by changing the carbon feeding rate. This work not only reveals the evolution mechanism of solid catalysts during the nucleation of SWCNTs but also provides prospects for realizing solid catalysts with a changeless structure by tuning the experimental parameters.

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CONTEXT: Nanoscrolls are tube-shaped structures formed when a sheet or ribbon of material is rolled into a cylinder, creating a hollow tube with a diameter on the nanoscale, similar to the papyrus. Carbon nanoscrolls have unique properties that make them useful in various applications, such as energy storage, catalysis, and drug delivery. In this study, we employed classical molecular dynamics simulations to investigate the formation and stability of nanoscrolls composed of graphene and hexagonal boron nitride (hBN) nanoribbons. Using a carbon nanotube (CNT) as a template to trigger their collapsing, we found that graphene/graphene, graphene/hBN, and hBN/hBN could form CNT-wrapped nanoscrolls at ultrafast speeds. We also confirmed that these nanoscrolls are thermally stable and discussed the other products formed from the interaction of these complexes and their temperature dependence. Gr/Gr and hBN/Gr nanoscrolls exhibit similar interlayer distances, while hBN/hBN nanoscrolls have wider interlayer distances than the other two composite nanoscrolls. These features suggest that hBN/hBN composite nanoscrolls could more efficiently capture small molecules because of their greater interlayer spacing. METHODS: We conducted molecular dynamics simulations using the Forcite package in the Biovia Materials Studio software, which employs the Universal and Dreiding force fields. We considered an NVT ensemble with a fixed time step of 1.0 fs for a duration of 500 ps. The velocity Verlet algorithm was adopted to integrate the equations of motion of the entire system. We employed the Nosé-Hoover-Langevin thermostat to control the system temperature. The simulations were carried out without periodic boundary conditions, so there was no pressure coupling.

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During the early stage of the COVID-19 pandemic (winter 2020), the northern part of Italy has been significantly affected by viral infection compared to the rest of the country leading the scientific community to hypothesize that airborne particulate matter (PM) could act as a carrier for the SARS-CoV-2. To address this controversial issue, we first verified and demonstrated the presence of SARS-CoV-2 RNA genome on PM2.5 samples, collected in the city of Bologna (Northern Italy) in winter 2021. Then, we employed classical molecular dynamics (MD) simulations to investigate the possible recognition mechanism(s) between a newly modelled PM2.5 fragment and the SARS-CoV-2 Spike protein. The potential molecular interaction highlighted by MD simulations suggests that the glycans covering the upper Spike protein regions would mediate the direct contact with the PM2.5 carbon core surface, while a cloud of organic and inorganic PM2.5 components surround the glycoprotein with a network of non-bonded interactions resulting in up to 4769 total contacts. Moreover, a binding free energy of -207.2 ± 3.9 kcal/mol was calculated for the PM-Spike interface through the MM/GBSA method, and structural analyses also suggested that PM attachment does not alter the protein conformational dynamics. Although the association between the PM and SARS-CoV-2 appears plausible, this simulation does not assess whether these established interactions are sufficiently stable to carry the virus in the atmosphere, or whether the virion retains its infectiousness after the transport. While these key aspects should be verified by further experimental analyses, for the first time, this pioneering study gains insights into the molecular interactions between PM and SARS-CoV-2 Spike protein and will support further research aiming at clarifying the possible relationship between PM abundance and the airborne diffusion of viruses.

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Among various porous solids for gas separation and purification, metal-organic frameworks (MOFs) are promising materials that potentially combine high CO2 uptake and CO2/N2 selectivity. So far, within the hundreds of thousands of MOF structures known today, it remains a challenge to computationally identify the best suited species. First principle-based simulations of CO2 adsorption in MOFs would provide the necessary accuracy; however, they are impractical due to the high computational cost. Classical force field-based simulations would be computationally feasible; however, they do not provide sufficient accuracy. Thus, the entropy contribution that requires both accurate force fields and sufficiently long computing time for sampling is difficult to obtain in simulations. Here, we report quantum-informed machine-learning force fields (QMLFFs) for atomistic simulations of CO2 in MOFs. We demonstrate that the method has a much higher computational efficiency (∼1000×) than the first-principle one while maintaining the quantum-level accuracy. As a proof of concept, we show that the QMLFF-based molecular dynamics simulations of CO2 in Mg-MOF-74 can predict the binding free energy landscape and the diffusion coefficient close to experimental values. The combination of machine learning and atomistic simulation helps achieve more accurate and efficient in silico evaluations of the chemisorption and diffusion of gas molecules in MOFs.

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HYPOTHESIS: The precipitation and dissolution of aluminum-bearing mineral phases in aqueous systems often proceed via changes in both aluminum coordination number and connectivity, complicating molecular-scale interpretation of the transformation mechanism. Here, the thermally induced transformation of crystalline sodium aluminum salt hydrate, a phase comprised of monomeric octahedrally coordinated aluminate which is of relevance to industrial aluminum processing, has been studied. Because intermediate aluminum coordination states during melting have not previously been detected, it is hypothesized that the transition to lower coordinated aluminum ions occurs within ahighly disordered quasi-two-dimensional phase at the solid-solution interface. EXPERIMENTS AND SIMULATIONS: In situ X-ray diffraction (XRD), Raman and27Al nuclear magnetic resonance (NMR) spectroscopy were used to monitor the melting transition of nonasodium aluminate hydrate (NSA, Na9[Al(OH)6]2·3(OH)·6H2O). A mechanistic interpretation was developed based on complementary classical molecular dynamics (CMD) simulations including enhanced sampling. A reactive forcefield was developed to bridge speciation in the solution and in the solid phase. FINDINGS: In contrast to classical dissolution, aluminum coordination change proceeds through a dynamically stabilized ensemble of intermediate states in a disordered layer at the solid-solution interface. In both melting and dissolution of NSA, octahedral, monomeric aluminum transition through an intermediate of pentahedral coordination. The intermediate dehydroxylates to form tetrahedral aluminate (Al(OH)4-) in the liquid phase. This coordination change is concomitant with a breaking of the ionic aluminate-sodium ionlinkages. The solution phase Al(OH)4- ions subsequently polymerize into polynuclear aluminate ions. However, there are some differences between bulk melting and interfacial dissolution, with the onset of the surface-controlled process occurring at a lower temperature (∼30 °C) and the coordination change taking place more gradually as a function of temperature. This work to determine the local structure and dynamics of aluminum in the disordered layer provides a new basis to understand mechanisms controlling aluminum phase transformations in highly alkaline solutions.

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Surface functionalization of metal nanoparticles (NPs), e.g., using peptides and proteins, has recently attracted a considerable attention in the field of design of therapeutics and diagnostics. The possibility of diverse functionalization allows them to selectively interact with proteins, while the metal core ensures solubility, making them tunable therapeutic agents against diseases due to mis-folding or aggregation. On the other hand, their action is limited by possible self-aggregation, which could be, however, prevented based on the full understanding of their phase diagram as a function of the environmental variables (temperature, ionic strength of the solution, concentration) and intrinsic characteristics (size, charge, amount, and type of functional groups). A common modeling strategy to study the phase behavior is to represent the NPs as spheres interacting via effective potentials implicitly accounting for the solvation effects. Their size put the NPs into the class of colloids, albeit with particularly complex interactions including both attractive and repulsive features, and a consequently complex phase diagram. In this work, we review the studies exploring the phases of these systems starting from those with only attractive or repulsive interactions, displaying a simpler disperse-clustered-aggregated transitions. The phase diagram is here interpreted focusing on the universal aspects, i.e., those dependent on the general feature of the potentials, and available data are organized in a parametric phase diagram. We then consider the potentials with competing attractive short range well and average-long-range repulsive tail, better representing the NPs. Through the proper combination of the attractive only and repulsive only potentials, we are able to interpret the appearance of novel phases, characterized by aggregates with different structural characteristics. We identify the essential parameters that stabilize the disperse phase potentially useful to optimize NP therapeutic activity and indicate how to tune the phase behavior by changing environmental conditions or the NP chemical-physical properties.

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Avena sativa L. is a wholegrain cereal and an important edible crop. Oats possesses high nutritional and health promoting values and contains high levels of bioactive compounds, including a group of phenolic amides, named avenanthramides (Avns), exerting antioxidant, anti-inflammatory, and anticancer activities. Epidermal growth factor receptor (EGFR) represents one of the most known oncogenes and it is frequently up-regulated or mutated in human cancers. The oncogenic effects of EGFR include enhanced cell growth, angiogenesis, and metastasis, and down-regulation or inhibition of EGFR signaling has therapeutic benefit. Front-line EGFR tyrosine kinase inhibitor therapy is the standard therapy for patients with EGFR-mutated lung cancer. However, the clinical effects of EGFR inhibition may be lost after a few months of treatment due to the onset of resistance. Here, we showed the anticancer activity of Avns, focusing on EGFR activation and signaling pathway. Lung cancer cellular models have been used to evaluate the activity of Avns on tumor growth, migration, EMT, and anoikis induced by EGF. In addition, docking and molecular dynamics simulations showed that the Avns bind with high affinity to a region in the vicinity of αC-helix and the DGF motif of EGFR, jeopardizing the target biological function. Altogether, our results reveal a new pharmacological activity of Avns as EGFR tyrosine kinase inhibitors.

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Artemis is an endonuclease responsible for breaking hairpin DNA strands during immune system adaptation and maturation as well as the processing of potentially toxic DNA lesions. Thus, Artemis may be an important target in the development of anticancer therapy, both for the sensitization of radiotherapy and for immunotherapy. Despite its importance, its structure has been resolved only recently, and important questions concerning the arrangement of its active center, the interaction with the DNA substrate, and the catalytic mechanism remain unanswered. In this contribution, by performing extensive molecular dynamic simulations, both classically and at the hybrid quantum mechanics/molecular mechanics level, we evidenced the stable interaction modes of Artemis with a model DNA strand. We also analyzed the catalytic cycle providing the free energy profile and key transition states for the DNA cleavage reaction.

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A mechanistic understanding of the influence of the surface properties of engineered nanomaterials on their interactions with cells is essential for designing materials for applications such as bioimaging and drug delivery as well as for assessing nanomaterial safety. Ligand-coated gold nanoparticles have been widely investigated because their highly tunable surface properties enable investigations into the effect of ligand functionalization on interactions with biological systems. Lipophilic ligands have been linked to adverse biological outcomes through membrane disruption, but the relationship between ligand lipophilicity and membrane interactions is not well understood. Here, we use a library of cationic ligands coated on 2 nm gold nanoparticles to probe the impact of ligand end group lipophilicity on interactions with supported phosphatidylcholine lipid bilayers as a model for cytoplasmic membranes. Nanoparticle adsorption to and desorption from the model membranes were investigated by quartz crystal microbalance with dissipation monitoring. We find that nanoparticle adsorption to model membranes increases with ligand lipophilicity. The effects of ligand structure on gold nanoparticle attachment were further analyzed using atomistic molecular dynamics simulations, which showed that the increase in ligand lipophilicity promotes ligand intercalation into the lipid bilayer. Together, the experimental and simulation results could be described by a two-state model that accounts for the initial attachment and subsequent conversion to a quasi-irreversibly bound state. We find that only nanoparticles coated with the most lipophilic ligands in our nanoparticle library undergo conversion to the quasi-irreversible state. We propose that the initial attachment is governed by interaction between the ligands and phospholipid tail groups, whereas conversion into the quasi-irreversibly bound state reflects ligand intercalation between phospholipid tail groups and eventual lipid extraction from the bilayer. The systematic variation of ligand lipophilicity enabled us to demonstrate that the lipophilicity of cationic ligands correlates with nanoparticle-bilayer adsorption and suggested that changing the nonpolar ligand R group promotes a mechanism of ligand intercalation into the bilayer associated with irreversible adsorption.

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The chemical and physical properties of lanthanide coordination complexes can significantly change with small variations in their molecular structure. Further, in solution, coordination structures (e.g., lanthanide-ligand complexes) are dynamic. Resolving solution structures, computationally or experimentally, is challenging because structures in solution have limited spatial restrictions and are responsive to chemical or physical changes in their surroundings. To determine structures of lanthanide-ligand complexes in solution, a molecular simulation approach is presented in this chapter, which concurrently considers chemical reactions and molecular dynamics. Lanthanide ion, ligand, solvent, and anion molecules are explicitly included to identify, in atomic resolution, lanthanide coordination structures in solution. The computational protocol described is applicable to determining the molecular structure of lanthanide-ligand complexes, particularly with ligands known to bind lanthanides but whose structures have not been resolved, as well as with ligands not previously known to bind lanthanide ions. The approach in this chapter is also relevant to elucidating lanthanide coordination in more intricate structures, such as in the active site of enzymes.

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The hydrophobicity of monolayer-protected gold nanoparticles is a crucial design parameter that influences self-assembly, preferential binding to proteins and membranes, and other nano-bio interactions. Predicting the effects of monolayer components on nanoparticle hydrophobicity is challenging due to the nonadditive, cooperative perturbations to interfacial water structure that dictate hydrophobicity at the nanoscale. In this work, we quantify nanoparticle hydrophobicity by using atomistic molecular dynamics simulations to calculate local hydration free energies at the nanoparticle-water interface. The simulations reveal that the hydrophobicity of large gold nanoparticles is determined primarily by ligand end group chemistry, as expected. However, for small gold nanoparticles, long alkanethiol ligands interact to form anisotropic bundles that lead to substantial spatial variations in hydrophobicity even for homogeneous monolayer compositions. We further show that nanoparticle hydrophobicity is modulated by changing the ligand structure, ligand chemistry, and gold core size, emphasizing that single-ligand properties alone are insufficient to characterize hydrophobicity. Finally, we illustrate that hydration free energy measurements correlate with the preferential binding of propane as a representative hydrophobic probe molecule. Together, these results show that both physical and chemical properties influence the hydrophobicity of small nanoparticles and must be considered together when predicting gold nanoparticle interactions with biomolecules.

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The specific heat behavior in bulk nanomaterials (NMs) obtained by adding nanoparticles to pure suspending media has attracted a lot interest in recent years. Controversial results about NMs specific heat (cp) have been reported in the literature, where nanoparticles (NPs) of different sizes and materials were suspended in solid and liquid salts at different concentrations and temperatures. However, a unified picture explaining the cp enhancements and diminutions by adding NPs to pure salts is still missing. In this work, we present a general theoretical thermostatic model aimed at describing the cp behavior in two-component ionic bulk nanomaterials containing NPs. The model, designed to work in the dilute regime, divides the NM in three regions: bulk suspending medium (SM), nanoparticles, and interface regions. It includes the effects of temperature, NP size, and NP concentration (mass fraction), allowing us to calculate cp variations with respect to the pure SM and the ideal NM (where NP and SM are assumed to not interact). We then use the model to interpret results of our classical molecular dynamics simulations, which we perform in the solid and liquid phases of NMs representative of three different classes, defined according to the atomic interactions at the interface. The analysis reveals nontrivial and competing effects influencing cp, such as system-dependent atomic rearrangements at the interface, vibrations of the NP as a whole and cp variations coming from the individual NP and SM specific heats. Our study contributes to the interpretation of past controversial results and helps in designing NMs with improved thermal properties, which is highly relevant for industrial applications in thermal energy storage and renewable energy production.

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Nowadays, boron nitride has attracted a great deal of attention due to its physical (chemical) properties, facile synthesis, and experimental characterization, indicating great potential for industrial application. Based on this, we develop here a theoretical study on boron nitride nanoflakes built-up from hexagonal boron nitride nanosheets exhibiting hexagonal, rectangular, and triangular shapes. In order to investigate geometry effects such as those due to the presence of armchair and zigzag edges and distinct shapes, we analyzed their properties from both classical and quantum viewpoints. Using classical molecular dynamics calculations, we show that the nanosheets preserve their structural stability at high temperatures, while DFT calculations demonstrate HOMO-LUMO energy gap variation within the theoretical energy gaps of h-BN in bulk and 2D crystals. Besides that, we have also found that boron nitride nanoflakes structures have spatially symmetrical spin densities.

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Transitions between different conformational states are ubiquitous in proteins, being involved in signaling, catalysis, and other fundamental activities in cells. However, modeling those processes is extremely difficult, due to the need of efficiently exploring a vast conformational space in order to seek for the actual transition path for systems whose complexity is already high in the stable states. Here we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to Calmodulin, based on an empirical force field with a partial structural bias, to explore the transition paths between the apo-closed state and the Ca-bound open state of the protein. We then select representative structures along the trajectory based on a structural clustering algorithm and build a cleaned-up trajectory with them. We finally compare this trajectory with that produced by the online tool MinActionPath, by minimizing the action integral using a harmonic network model, and with that obtained by the PROMPT morphing method, based on an optimal mass transportation-type approach including physical constraints. The comparison is performed both on the structural and energetic level, using the coarse-grained and the atomistic force fields upon reconstruction. Our analysis indicates that this method returns trajectories capable of exploring intermediate states with physical meaning, retaining a very low computational cost, which can allow systematic and extensive exploration of the multi-stable proteins transition pathways.

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The solution-phase stability of the hydronium ion catalyst significantly affects the rates of acid-catalyzed reactions, which are ubiquitously utilized to convert biomass to valuable chemicals. In this work, classical molecular dynamics simulations were performed to quantify the stability of hydronium and chloride ions by measuring their solvation free energies in water, 1,4-dioxane (DIOX), tetrahydrofuran (THF), γ-valerolactone (GVL), N-methyl-2-pyrrolidone (NMP), acetone (ACE), and dimethyl sulfoxide (DMSO). By measuring the free energy for transferring a hydronium ion from pure water to pure organic solvent, we found that the hydronium ion is destabilized in DIOX, THF, and GVL and stabilized in NMP, ACE, and DMSO relative to water. The distinction between these organic solvents can be used to predict the preference of the hydronium ion for specific regions in aqueous mixtures of organic solvents. We then incorporated the stability of the hydronium ion into a correlative model for the acid-catalyzed conversion of 1,2-propanediol to propanal. The revised model is able to predict experimental reaction rates across solvent systems with different organic solvents. These results demonstrate the ability of classical molecular dynamics simulations to screen solvent systems for improved acid-catalyzed reaction performance.

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Monomers and oligomers of the amyloid-ß peptide aggregate to form the fibrils found in the brains of Alzheimer's disease patients. These monomers and oligomers are largely disordered and can interact with transition metal ions, affecting the mechanism and kinetics of amyloid-ß aggregation. Due to the disordered nature of amyloid-ß, its rapid aggregation, as well as solvent and paramagnetic effects, experimental studies face challenges in the characterization of transition metal ions bound to amyloid-ß monomers and oligomers. The details of the coordination chemistry between transition metals and amyloid-ß obtained from experiments remain debated. Furthermore, the impact of transition metal ion binding on the monomeric or oligomeric amyloid-ß structures and dynamics are still poorly understood. Computational chemistry studies can serve as an important complement to experimental studies and can provide additional knowledge on the binding between amyloid-ß and transition metal ions. Many research groups conducted first-principles calculations, ab initio molecular dynamics simulations, quantum mechanics/classical mechanics simulations, and classical molecular dynamics simulations for studying the interplay between transition metal ions and amyloid-ß monomers and oligomers. This review summarizes the current understanding of transition metal interactions with amyloid-ß obtained from computational chemistry studies. We also emphasize the current view of the coordination chemistry between transition metal ions and amyloid-ß. This information represents an important foundation for future metal ion chelator and drug design studies aiming to combat Alzheimer's disease.

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Due to the considerable role of N-cadherin in cancer metastasis, tumor growth, and progression, inhibition of this protein has been highly regarded in recent years. Although ADH-1 has been known as an appropriate inhibitor of N-cadherin in clinical trials, its chemical nature and binding mode with N-cadherin have not been precisely specified yet. Accordingly, in this study, quantum mechanics calculations were used to investigate the chemical nature of ADH-1. These calculations clarify the molecular properties of ADH-1 and determine its reactive sites. Based on the results, the oxygen atoms are suitable for electrophilic reactivity, while the hydrogen atoms that are connected to nitrogen atoms are the favorite sites for nucleophilic reactivity. The higher electronegativity of the oxygen atoms makes them the most reactive portions in this molecule. Molecular docking and molecular dynamics (MD) simulation have also been applied to specify the binding mode of ADH-1 with N-cadherin and determine the important residues of N-cadherin involving in the interaction with ADH-1. Moreover, the verified model by MD simulation has been studied to extract the free energy value and find driving forces. These calculations and molecular electrostatic potential map of ADH-1 indicated that hydrophobic and electrostatic interactions are almost equally involved in the implantation of ADH-1 in the N-cadherin binding site. The presented results not only enable a closer examination of N-cadherin in complex with ADH-1 molecule, but also are very beneficial in designing new inhibitors for N-cadherin and can help to save time and cost in this field.

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