Single bond refers to a covalent chemical bond in which two atoms share a single pair of electrons.
This type of bond is commonly found in organic compounds and is essential for the stability and structure of many molecules.
Understanding the properties and behavior of single bonds is crucial for research in fields such as chemistry, materials science, and drug design.
PubCompare.ai's AI-driven platform can help researchers locate and compare the best protocols from literature, preprints, and patents related to single bond optimization, enhancing reproducibility and research accuracy.
This intuitive tool allows users to improve their research outcomes by accessing data-driven comparisons and insights on single bond optimization methodologies.
We have developed and tested two methods for docking of covalently-attached complexes: a grid-based approach and a modification of the flexible sidechain method. The grid-based approach calculates a special map for the site of attachment of the covalent ligand. A Gaussian function is constructed with zero energy at the site of attachment and steep energetic penalties at surrounding areas. The docking analysis is then performed by assigning a special atom type in the ligand for the atom that forms the covalent linkage. The docking simulation places this within the Gaussian well. One caveat is that this does not constrain the geometry of the covalent attachment to reasonable bond angles. To overcome this limitation, we tested the method using two Gaussian grids to define the bond that is formed during covalent linkage. Note, however, that the conformational freedom allowed with a single Gaussian grid may be an advantage if the method is used, for instance, to target ligands to metal coordination sites. We also tested use of the flexible sidechain method for docking of covalent ligands. In this case, a coordinate file is created with the ligand attached to the proper sidechain in the protein, by overlapping ideal coordinates of the ligand onto the proper bond in the protein. This sidechain-ligand structure is then treated as flexible during the docking simulation, searching torsional degrees of freedom to optimize the interaction with the rest of the protein.
Morris G.M., Huey R., Lindstrom W., Sanner M.F., Belew R.K., Goodsell D.S, & Olson A.J. (2009). AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility. Journal of computational chemistry, 30(16), 2785-2791.
To address the transferability of partial atomic charges based on small molecules to the larger head group in lipids, a series of semiempirical QM calculations on different compounds in different environments were undertaken. These calculations used the program DivCon,51 which applies a divide and conquer approach52 ,53 to allow for full AM1 level calculations to be performed on systems with 10,000 or more atoms. Both the Mulliken and CM2 charges54 were obtained and compared for the different compounds and environments to determine the impact of environment on partial atomic charges. DPPC was selected as the target lipid and corresponding small molecules were chosen, i.e., methylacetate (MAS), dimethylphosphate (DMP) and ethyltrimethylammonium (ETMA) (Fig. 1). These molecules were selected as they represent the compounds used for optimization of the non-bond parameters in the C27 FF, with DMP and MAS also being used in the present work. In addition, EGLY and PGLY (Fig. 1) were analyzed to determine the potential utility of larger model compounds. All model compound results were based on gas phase calculations and charges were averaged over multiple conformations as described in the legend of Table S1. For the full lipid, calculations were performed on the DPPC molecules individually in the gas phase and, importantly, on the fully solvated lipid bilayer. The charges for individual lipids used for averaging were based on lipids that were located near the central region of the bilayer to include realistic membrane interactions. The solvated bilayer was obtained from an NPAT MD simulation of 72 DPPC molecules at high hydration and the experimental surface area in periodic boundary conditions using the C27r FF.3 From this simulation, 13 time frames were extracted and individual lipid molecules in the central region of each leaf of the bilayer were identified, yielding a total of 64 lipid conformations. Each of these conformations was subjected to an AM1 calculation isolated in the gas phase and average charges were calculated. To evaluate the impact of the condensed phase environment on the partial atomic charges, AM1 calculations using DivCon on the full bilayer-solvent systems were calculated to obtain charges in a membrane environment. Though these calculations were performed in the absence of periodic boundary conditions used in the MD simulations, the conformations of the lipids in the gas phase and the bilayer/water phase calculations were identical; only the environment of the individual lipids changed. The dihedral portion of the CHARMM FF is mostly from direct fits to vacuum QM-based conformational energies. This approach was not satisfactory for the glycerol torsions β1θ2, and θ4, where solvent effects are expected to be complex such that gas phase potential energy surfaces may be inappropriate.55 (link),56 (link) As described below, these torsions were adjusted slightly to match the experimental deuterium order parameters glycerol region for DPPC following new QM estimates. (Values of SCD reported here are always positive; i.e., the absolute value sign is assumed.) The QM calculations follow the approach used for C27r where the acyl torsions were refined; see references 3 ,57 for details. Figure 1 shows the small molecules used to represent regions of the phospholipid to allow for high-level QM calculations. Second order Möller-Plesset perturbation theory (MP2)58 is used to obtain global minimum energy geometries of these small molecules and a single dihedral constraint is used to obtain torsional energy scans. To obtain conformational energy at the coupled cluster level, CCSD(T),59 with a large basis set, HM-IE (the Hybrid Method for Interaction Energies)60 is used to estimate CCSD(T)/cc-pVQZ, which assumes that the effect of larger basis sets is additive. These conformational energies using HM-IE will be simply referred to as QM in this paper. The following objective function was used initially to obtain all new torsional terms for C36, where Ui is the energy for conformation i. The alkane torsions3 ,4 were also refit to this function to maintain a consistent approach for fitting dihedral parameters (a higher weight was used for the trans and gauche minima in C27r). The alkene torsions adjacent to C=C doubles bonds were fit to conformations of 2-hexene at the RIMP2/cc-pVQZ//MP2/cc-pVDZ level using a Monte Carlo Simulated Annealing approach.61
Klauda J.B., Venable R.M., Freites J.A., O’Connor J.W., Tobias D.J., Mondragon-Ramirez C., Vorobyov I., MacKerell AD J.r, & Pastor R.W. (2010). Update of the CHARMM all-atom additive force field for lipids: Validation on six lipid types. The journal of physical chemistry. B, 114(23), 7830-7843.
Models of single ions interacting with one or more water molecules were built and minimized in vacuo. Because the potential energy of either a single isolated ion or a single isolated water molecule (with the rigid water models used) is zero, the potential energy of the combined system is equivalent to the binding energy. For the cations and the anions with a single water molecule, C2v structures and Cs structures, respectively, represent the global minimum structures. In addition to these basic geometries, various other ion−water structures were built (see Figures 1 and 2). In the figures, the indices include two numbers (e.g., x + y) which indicate the number of water molecules in the first and second hydration shells, respectively. Some of these indices have the letter h next to them, which refers to a halfway or mixed interaction of the water molecules with both the first and the second hydration shells. Unfortunately, because the default minimization algorithms in AMBER are unstable, dated, and limited, the sander program could not accurately minimize the structures with fixed water bond distances (i.e., SHAKE(171 )). Therefore, the energy minimization was performed with a home-built Perl script validated to match the AMBER energies.
Joung I.S, & Cheatham, TE I.I.I. (2008). Determination of Alkali and Halide Monovalent Ion Parameters for Use in Explicitly Solvated Biomolecular Simulations. The Journal of Physical Chemistry. B, 112(30), 9020-9041.
To initiate refinement, a number of major sources of information have to be processed.
(i) Structural model: coordinates, displacement parameters, occupancies, atom types, f′ and f′′ for anomalous scatterers (if present).
(ii) Reflection data: pre-processed observed intensities or amplitudes of structure factors and, optionally, experimental phases.
(iii) Parameters determining the refinement protocol.
(iv) Empirical geometry restraints: bond lengths, bond angles, dihedral angles, chiralities and planarities (Engh & Huber, 1991 ▶ ; Grosse-Kunstleve, Afonine et al., 2004 ▶ ).
(v) Optionally, a restraint library file (CIF) may be provided to define the stereochemistry of entities in the input model (for example, ligands) that do not have corresponding restraints in the library included in the PHENIX distribution.
The user provides the structural model and reflection data. The refinement software then retrieves default parameters and information from a library of empirical geometry restraints, which can be readily customized by the user. The PDB format (Bernstein et al., 1977 ▶ ; Berman et al., 2000 ▶ ) is the most commonly used format for exchanging macromolecular model data and is therefore available as the input format for refinement in PHENIX. The iotbx.pdb library module (Grosse-Kunstleve & Adams, 2010 ▶ ) performs the first stage of the PDB-file interpretation. It robustly constructs an internal hierarchy of models (PDB MODEL keyword), chains, conformers (PDB altLoc identifier), residues and atoms. Common simple formatting problems are corrected on the fly where possible. Currently, phenix.refine can only make use of PDB files containing a single model. The second stage of the PDB interpretation involves matching the structural data with definitions in the CCP4 Monomer Library (Vagin & Murshudov, 2004 ▶ ; Vagin et al., 2004 ▶ ) in order to derive geometry restraints, scattering types and nonbonded energy types. Many common simple formatting and naming problems are considered in this interpretation. The PDB interpretation (iotbx.pdb) has been tested with all files found in the PDB database (http://www.pdb.org/) as of August, 2011 and supports both PDB version 2.3 and version 3.x atom-naming conventions. The vast majority of files can be processed without user intervention. Detailed diagnostic messages help the user to quickly identify idiosyncrasies in the PDB file that cannot be automatically corrected. If the input PDB file contains an item undefined in the CCP4 Monomer Library, a geometry restraint (CIF) file must be provided for that item. This file can be obtained by running phenix.elbow (Moriarty et al., 2009 ▶ ) or phenix.ready_set, which is more comprehensive and automated. The experimental data can be provided in many commonly used formats. Multiple input files can be given simultaneously, e.g. a SCALEPACK file (Otwinowski & Minor, 1997 ▶ ) with observed intensities, a CNS (Brünger et al., 1998 ▶ ) file with Rfree flags (Brünger, 1992 ▶ , 1993 ▶ ) and an MTZ file (Winn et al., 2011 ▶ ) with phase information. A comprehensive procedure aims to extract the data most suitable for refinement without user intervention. A preliminary crystallographic data analysis is performed in order to detect and ignore potential reflection outliers (Read, 1999 ▶ ). If twinning (for a review, see Parsons, 2003 ▶ ; Helliwell, 2008 ▶ ) is suspected, a user can run phenix.xtriage (Zwart et al., 2005 ▶ ) to obtain a twin-law operator to be used by the twin-refinement target in phenix.refine. A number of automatic adjustments to the refinement strategy are considered at this point. These adjustments include automatic choice of refinement target if necessary (based on the number of test reflections, the presence of twinning and the availability of experimental phase information as Hendrickson–Lattman coefficients; Hendrickson & Lattman, 1970 ▶ ), specifying the atomic displacement parameters (isotropic or anisotropic), determining whether or not to add ordered solvent (if the resolution is sufficient), automatic detection or adjustment of user-provided NCS selections, determining the set of atoms that should have their occupancies refined and automatic determination of occupancy constraints for atoms in alternative conformations. When joint refinement is performed using both X-ray and neutron data (Coppens et al., 1981 ▶ ; Wlodawer & Hendrickson, 1981 ▶ , 1982 ▶ ; Adams et al., 2009 ▶ ; Afonine, Mustyakimov et al., 2010 ▶ ), it is important to ensure that the cross-validation reflections are consistent between data sets. This check is performed automatically. If a mismatch is detected, phenix.refine will terminate and offer to generate a new set of flags consistent with both data sets. The large set of configurable refinement parameters is presented to the user in a novel hierarchical organization, libtbx.phil, specifically designed to be user-friendly (Grosse-Kunstleve et al., 2005 ▶ ). This is achieved via a simple syntax with the option to easily override selected parameters from the command line. This parameter-handling framework is completely general and can be reused for other purposes unrelated to refinement. A comprehensive and intuitive graphical user interface (GUI) built around this framework is also available, allowing users of all skill levels to use phenix.refine.
Afonine P.V., Grosse-Kunstleve R.W., Echols N., Headd J.J., Moriarty N.W., Mustyakimov M., Terwilliger T.C., Urzhumtsev A., Zwart P.H, & Adams P.D. (2012). Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallographica Section D: Biological Crystallography, 68(Pt 4), 352-367.
Through our process of iterative reading and discussion of the literature, we worked to nominate definitions that (1) achieve as much consistency as possible with any existing definitions (including multiple definitions we found for a single construct), yet (2) serve to sharpen distinctions between constructs that might be similar. For several of the outcomes, the literature did not offer one clear nominal definition. Table 1 depicts the resultant working taxonomy of implementation outcomes. For each implementation outcome, the table nominates a level of analysis, identifies the theoretical basis to the construct from implementation literature, shows different terms that are used for the construct in the literature, suggests the point or stage within implementation processes at which the outcome may be most salient, and lists the types of existing measures for the construct that our search identified. The implementation outcomes listed in Table 1 are probably only the “more obvious,” and we expect that other concepts may emerge from further analysis of the literature and from the kind of empirical work we call for in our discussion below. Many of the implementation outcomes can be inferred or measured in terms of expressed attitudes and opinions, intentions, or reported or observed behaviors. We now list and discuss our nominated conceptual definitions for each implementation outcome in our proposed taxonomy. We reference similar definitions from the literature, and also comment on marked differences between our definitions and others proposed for the term.
Taxonomy of implementation outcomes
Implementation outcome
Level of analysis
Theoretical basis
Other terms in literature
Salience by implementation stage
Available measurement
Acceptability
Individual provider
Individual consumer
Rogers: “complexity” and to a certain extent “relative advantage”
Satisfaction with various aspects of the innovation (e.g. content, complexity, comfort, delivery, and credibility)
Early for adoption
Ongoing for penetration
Late for sustainability
Survey
Qualitative or semi-structured interviews
Administrative data
Refused/blank
Adoption
Individual provider
Organization or setting
RE-AIM: “adoption” Rogers: “trialability” (particularly for early adopters)
Uptake; utilization; initial implementation; intention to try
Acceptability is the perception among implementation stakeholders that a given treatment, service, practice, or innovation is agreeable, palatable, or satisfactory. Lack of acceptability has long been noted as a challenge in implementation (Davis 1993 (link)). The referent of the implementation outcome “acceptability” (or the “what” is acceptable) may be a specific intervention, practice, technology, or service within a particular setting of care. Acceptability should be assessed based on the stakeholder’s knowledge of or direct experience with various dimensions of the treatment to be implemented, such as its content, complexity, or comfort. Acceptability is different from the larger construct of service satisfaction, as typically measured through consumer surveys. Acceptability is more specific, referencing a particular treatment or set of treatments, while satisfaction typically references the general service experience, including such features as waiting times, scheduling, and office environment. Acceptability may be measured from the perspective of various stakeholders, such as administrators, payers, providers, and consumers. We presume rated acceptability to be dynamic, changing with experience. Thus ratings of acceptability may be different when taken, for example, pre-implementation and later throughout various stages of implementation. The literature reflects several examples of measuring provider and patient acceptability. Aarons’ Evidence-Based Practice Attitude Scale (EBPAS) captures the acceptability of evidence-based mental health treatments among mental health providers (Aarons 2004 (link)). Aarons and Palinkas (2007 (link)) used semi-structured interviews to assess case managers’ acceptance of evidence-based practices in a child welfare setting. Karlsson and Bendtsen (2005 (link)) measured patients’ acceptance of alcohol screening in an emergency department setting using a 12-item questionnaire. Adoption is defined as the intention, initial decision, or action to try or employ an innovation or evidence-based practice. Adoption also may be referred to as “uptake.” Our definition is consistent with those proposed by Rabin et al. (2008 (link)) and Rye and Kimberly (2007 (link)). Adoption could be measured from the perspective of provider or organization. Haug et al. (2008 (link)) used pre-post items to capture substance abuse providers’ adoption of evidence-based practices, while Henggeler et al. (2008 (link)) report interview techniques to measure therapists’ adoption of contingency management. Appropriateness is the perceived fit, relevance, or compatibility of the innovation or evidence based practice for a given practice setting, provider, or consumer; and/or perceived fit of the innovation to address a particular issue or problem. “Appropriateness” is conceptually similar to “acceptability,” and the literature reflects overlapping and sometimes inconsistent terms when discussing these constructs. We preserve a distinction because a given treatment may be perceived as appropriate but not acceptable, and vice versa. For example, a treatment might be considered a good fit for treating a given condition but its features (for example, rigid protocol) may render it unacceptable to the provider. The construct “appropriateness” is deemed important for its potential to capture some “pushback” to implementation efforts, as is seen when providers feel a new program is a “stretch” from the mission of the health care setting, or is not consistent with providers’ skill set, role, or job expectations. For example, providers may vary in their perceptions of the appropriateness of programs that co-locate mental health services within primary medical, social service, or school settings. Again, a variety of stakeholders will likely have perceptions about a new treatment’s or program’s appropriateness to a particular service setting, mission, providers, and clientele. These perceptions may be function of the organization’s culture or climate (Klein and Sorra 1996 (link)). Bartholomew et al. (2007 (link)) describe a rating scale for capturing appropriateness of training among substance abuse counselors who attended training in dual diagnosis and therapeutic alliance. Cost (incremental or implementation cost) is defined as the cost impact of an implementation effort. Implementation costs vary according to three components. First, because treatments vary widely in their complexity, the costs of delivering them will also vary. Second, the costs of implementation will vary depending upon the complexity of the particular implementation strategy used. Finally, because treatments are delivered in settings of varying complexity and overheads (ranging from a solo practitioner’s office to a tertiary care facility), the overall costs of delivery will vary by the setting. The true cost of implementing a treatment, therefore, depends upon the costs of the particular intervention, the implementation strategy used, and the location of service delivery. Much of the work to date has focused on quantifying intervention costs, e.g., identifying the components of a community-based heart health program and attaching costs to these components (Ronckers et al. 2006 (link)). These cost estimations are combined with patient outcomes and used in cost-effectiveness studies (McHugh et al. 2007 (link)). A review of literature on guideline implementation in professions allied to medicine notes that few studies report anything about the costs of guideline implementation (Callum et al. 2010 (link)). Implementing processes that do not require ongoing supervision or consultation, such as computerized medical record systems, may carry lower costs than implementing new psychosocial treatments. Direct measures of implementation cost are essential for studies comparing the costs of implementing alternative treatments and of various implementation strategies. Feasibility is defined as the extent to which a new treatment, or an innovation, can be successfully used or carried out within a given agency or setting (Karsh 2004 (link)). Typically, the concept of feasibility is invoked retrospectively as a potential explanation of an initiative’s success or failure, as reflected in poor recruitment, retention, or participation rates. While feasibility is related to appropriateness, the two constructs are conceptually distinct. For example, a program may be appropriate for a service setting—in that it is compatible with the setting’s mission or service mandate, but may not be feasible due to resource or training requirements. Hides et al. (2007 (link)) tapped aspects of feasibility of using a screening tool for co-occurring mental health and substance use disorders. Fidelity is defined as the degree to which an intervention was implemented as it was prescribed in the original protocol or as it was intended by the program developers (Dusenbury et al. 2003 (link); Rabin et al. 2008 (link)). Fidelity has been measured more often than the other implementation outcomes, typically by comparing the original evidence-based intervention and the disseminated/implemented intervention in terms of (1) adherence to the program protocol, (2) dose or amount of program delivered, and (3) quality of program delivery. Fidelity has been the overriding concern of treatment researchers who strive to move their treatments from the clinical lab (efficacy studies) to real-world delivery systems. The literature identifies five implementation fidelity dimensions including adherence, quality of delivery, program component differentiation, exposure to the intervention, and participant responsiveness or involvement (Mihalic 2004 ; Dane and Schneider 1998 (link)). Adherence, or the extent to which the therapy occurred as intended, is frequently examined in psychotherapy process and outcomes research and is distinguished from other potentially pertinent implementation factors such as provider skill or competence (Hogue et al. 1996 ). Fidelity is measured through self-report, ratings, and direct observation and coding of audio- and videotapes of actual encounters, or provider-client/patient interaction. Achieving and measuring fidelity in usual care is beset by a number of challenges (Proctor et al. 2009 (link); Mihalic 2004 ; Schoenwald et al. 2005 (link)). The foremost challenge may be measuring implementation fidelity quickly and efficiently (Hayes 1998 ). Schoenwald and colleagues (2005 (link)) have developed three 26–45-item measures of adherence at the therapist, supervisor and consultant level of implementation (available from the MST Institute www.mstinstitute.org). Ratings are obtained at regular intervals, enabling examination of the provider, clinical supervisor, and consultant. Other examples from the mental health literature include Bond et al. (2008 (link)) 15-item Supported Employment Fidelity Scale (SE Fidelity Scale) and Hogue et al. (2008 (link)) Therapist Behavior Rating Scale-Competence (TBRS-C), an observational measure of fidelity in evidence based practices for adolescent substance abuse treatment. Penetration is defined as the integration of a practice within a service setting and its subsystems. This definition is similar to (Stiles et al. 2002 (link)) notion of service penetration and to Rabin et al.s’ (2008 (link)) notion of niche saturation. Studying services for persons with severe mental illness, Stiles et al. (2002 (link)) apply the concept of service penetration to service recipients (the number of eligible persons who use a service, divided by the total number of persons eligible for the service). Penetration also can be calculated in terms of the number of providers who deliver a given service or treatment, divided by the total number of providers trained in or expected to deliver the service. From a service system perspective, the construct is also similar to “reach” in the RE-AIM framework (Glasgow 2007b ). We found infrequent use of the term penetration in the implementation literature; though studies seemed to tap into this construct with terms such a given treatment’s level of institutionalization. Sustainability is defined as the extent to which a newly implemented treatment is maintained or institutionalized within a service setting’s ongoing, stable operations. The literature reflects quite varied uses of the term “sustainability,” but our proposed definition incorporates aspects of those offered by Johnson et al. (2004 (link)), Turner and Sanders (2006 (link)), Glasgow et al. (1999 (link)), Goodman et al. (1993 (link)), and Rabin et al. (2008 (link)). Rabin et al. (2008 (link)) emphasizes the integration of a given program within an organization’s culture through policies and practices, and distinguishes three stages that determine institutionalization: (1) passage (a single event such as transition from temporary to permanent funding), (2) cycle or routine (i.e., repetitive reinforcement of the importance of the evidence-based intervention through including it into organizational or community procedures and behaviors, such as the annual budget and evaluation criteria), and (3) niche saturation (the extent to which an evidence-based intervention is integrated into all subsystems of an organization). Thus the outcomes of “penetration” and “sustainability” may be related conceptually and empirically, in that higher penetration may contribute to long-term sustainability. Such relationships require empirical test, as we elaborate below. Indeed Steckler et al. (1992 (link)) emphasize sustainability in terms of attaining long-term viability, as the final stage of the diffusion process during which innovations settle into organizations. To date, the term sustainability appears more frequently in conceptual papers than actual empirical articles measuring sustainability of innovations. As we discuss below, the literature often uses the same term (niche saturation, for example) to reference multiple implementation outcomes, underscoring the need for conceptual clarity as we seek to advance in this paper.
Proctor E., Silmere H., Raghavan R., Hovmand P., Aarons G., Bunger A., Griffey R, & Hensley M. (2010). Outcomes for Implementation Research: Conceptual Distinctions, Measurement Challenges, and Research Agenda. Administration and Policy in Mental Health, 38(2), 65-76.
Characterization of the MEPs executed for the two main relaxation coordinates involved in rPSB photo-isomerization. First, bond length relaxation coordinate has been studied by single point scans (with frozen bond lengths) at CASPT2/6–31G(d)/MM level of theory, between the FC and the ABL (alternated bond length) geometries. The initial coordinates of the intermediate structures acquired by linear interpolation of those single-double bonds alternate during the photo-isomerization process. The geometry of ABL point was obtained by optimization of the first (S1) excited state. Second, characterization of the MEPs associated with the isomerization around the central C13–C14 bond was performed, starting from the ABL structure, with gradual rotation of the C12–C13=C14–C15 dihedral angle by 14 degrees (with the atom numbering given at Fig. 3). The optimization scan involved constraints for central dihedral at CASSCF/6–31G(d)/MM, followed by the energy correction at CASPT2/6–31G(d)/MM level of theory.
Malakar P., Gholami S., Aarabi M., Rivalta I., Sheves M., Garavelli M, & Ruhman S. (2024). Retinal photoisomerization versus counterion protonation in light and dark-adapted bacteriorhodopsin and its primary photoproduct. Nature Communications, 15, 2136.
The functions involved in the , which represents a clash, and involved in , which represents a covalent bond, are calculated based on physico-chemical parameters: bond length, bond angle, dihedral angle of the covalent bond, and inter-atomic distance between each atom. The interaction energies can be calculated with a force field [27 (link),28 (link),29 (link)]. However, it is almost impossible to obtain placements pairs that have precisely appropriate bond lengths, bond angles, and dihedral angles for , owing to the limited resolution of fragment placements in this study. Therefore, distortion energy of a covalent bond and inter-molecular repulsion should be calculated tolerantly for errors. Thus, we dare to employ binary functions for and , representing whether the condition is applicable or not. Interaction energies and , based on the Universal Force Field (UFF) [27 (link)], are calculated using RDKit (version 2023.09.5). Note that includes distortion energy from the added covalent bond and non-bond interaction energy between atoms within the single molecule structure composed of the two fragments and the added covalent bond. The distortion energy originates from the bond length, bond angle, and dihedral. To accept some distortions and clashes, the energy tolerance level is set to a high value of 500 kcal/mol.
Yanagisawa K., Fujie T., Takabatake K, & Akiyama Y. (2024). QUBO Problem Formulation of Fragment-Based Protein–Ligand Flexible Docking. Entropy, 26(5), 397.
There are four types of bonded interactions in this model: bond, angle, dihedral, and improper. The bonded parameters in Gao’s paper are simplified MARTINI bonded interactions, where the equilibrium distances/angles are preserved but different force constants are simplified into one single force constant. In this work, the force constants are directly converted from MARTINI for better accuracies. The constraint interactions in MARTINI are converted to bond interactions with a large force constant of 30,000 kJ/(nm2·mol). All force constants are converted into DPD units. The bond, angle, dihedral, and improper interactions are simulated with bond style set to “harmonic”, angle style set to “cosine/squared”, dihedral style set to “charmm”, and improper style set to “harmonic” in LAMMPS, respectively.
Wang Y, & Hernandez R. (2024). Construction of Multiscale Dissipative Particle Dynamics (DPD) Models from Other Coarse-Grained Models. ACS Omega, 9(15), 17667-17680.
All desired molecules and enumerated substructures are loaded into RDKit as mol objects and sanitized. For each substructure, the frequency it occurs in the drug structure is computed and recorded. In plotting the chord diagram, the substructures are arrayed clockwise by increasing bond edit distance from the amine–acid starting materials. The more bonds that must be changed to convert the starting materials into the product, the higher is the reaction label number displayed around the periphery of the circle (Fig. S10). It should be noted that the number of bond edits from a given substrate pair does not necessarily correlate with synthetic ease. For example, the Diels-Alder reaction features six bond edits in the transformation matrix but experimentally occurs as a single concerted mechanistic step. In contrast a direct C–N bond cross-coupling transformation has only a single bond edit but experimentally requires several elementary steps in the form of a catalytic cycle including oxidative addition, reductive elimination and deprotonation80 (link). For this exercise, we chose to preserve the aromaticity of the enumerated and drug structures. When a molecule is loaded into RDKit, the user can choose to encode aromatic systems as conjugated single and double bonds76 (link). While we initially chose to remove aromaticity markers, we found a high proportion of substructure matches to lie along aromatic rings, which point to a disconnection fragmenting those rings. To remove these energetically undesirable matches, aromaticity of all molecular structures was subsequently preserved (Fig. S11). Code for substructure search is contained in 002_substructure_search.ipynb. The notebook takes as input smiles_min_dist_natoms.csv, appends the search result as an additional column in the spreadsheet, and saves the output as smiles_mindist_dbank.csv.
Zhang R., Mahjour B., Outlaw A., McGrath A., Hopper T., Kelley B., Walters W.P, & Cernak T. (2024). Exploring the combinatorial explosion of amine–acid reaction space via graph editing. Communications Chemistry, 7, 22.
Adper Single Bond 2 is a dental adhesive system developed by 3M. It is designed to bond dental restorative materials to tooth structures. The product features a simple, two-step application process.
Single Bond Universal is a dental adhesive product manufactured by 3M. It is designed to provide a strong, durable bond between dental restorative materials and tooth structures. The product functions as a universal adhesive, offering compatibility with a variety of materials used in dental procedures.
Sourced in Germany, United States, Switzerland, United Kingdom, Italy, Spain, Canada, Australia
Topspin 3.2 is a software package developed by Bruker for the acquisition, processing, and analysis of nuclear magnetic resonance (NMR) data. It provides a comprehensive suite of tools for the management and interpretation of NMR spectra.
Sourced in United States, Germany, United Kingdom, Switzerland, Israel, France, Australia
The Bond RX is a compact and versatile laboratory equipment designed for a wide range of applications. It offers reliable performance and consistent results, making it a valuable tool for researchers and scientists.
Sourced in Germany, United States, Switzerland, United Kingdom, France, Japan, Brazil
The Avance III is a high-performance NMR spectrometer by Bruker. It is designed for advanced nuclear magnetic resonance applications, providing reliable and accurate data acquisition and analysis capabilities.
Sourced in United States, Germany, India, Japan, Brazil
Filtek Z350 XT is a light-cured, radiopaque, resin-based composite material designed for use in anterior and posterior restorations. It is formulated to provide high esthetics, excellent handling characteristics, and long-lasting durability.
Single Bond Universal Adhesive is a dental bonding agent developed by 3M. It is designed for use in direct and indirect restorative procedures, providing a reliable bond to multiple dental substrates, including enamel, dentin, and various restorative materials.
Adper Single Bond is a light-cured, single-component dental adhesive system designed for bonding light-cured restorative materials to enamel and dentin. It provides a strong, durable bond to tooth structure.
Single Bond 2 is a dental adhesive product developed by 3M for use in dental procedures. It is a light-cured, one-component dental adhesive system that is designed to provide a strong, durable bond between tooth structures and restorative materials. The core function of Single Bond 2 is to create a reliable bond between the tooth and the restoration.
Maestro is a computational modeling software developed by Schrödinger. It is designed to assist researchers in visualizing and analyzing molecular structures and interactions. The core function of Maestro is to provide a comprehensive platform for molecular modeling and simulation.
A single bond is a covalent chemical bond in which two atoms share a single pair of electrons. This type of bond is commonly found in organic compounds and is essential for the stability and structure of many molecules. Some key properties of single bonds include their relative strength, directionality, and ability to form stable molecular structures.
Single bonds differ from other types of chemical bonds, such as double bonds and triple bonds, in the number of shared electron pairs. Double bonds involve the sharing of two electron pairs, while triple bonds involve the sharing of three electron pairs. These differences in electron sharing lead to distinct properties, such as bond strength, molecular geometry, and reactivity.
Single bonds are ubiquitous in organic chemistry and play a crucial role in the structure and properties of many materials. They are essential for the formation of stable hydrocarbon chains, as well as the construction of complex organic molecules used in pharmaceuticals, polymers, and other materials. Understanding the behavior of single bonds is also important for research in fields like catalysis, materials engineering, and nanoscale device fabrication.
PubCompare.ai's AI-driven platform can assist researchers in locating and comparing the best protocols from literature, preprints, and patents related to single bond optimization. The tool allows users to improve their research outcomes by accessing data-driven comparisons and insights on single bond optimization methodologies. By helping researchers screen protocol literature more efficiently and leveraging AI to pinpoint critical insights, PubCompare.ai can enable researchers to identify the most effective protocols for their specific research goals, enhancing reproducibility and research accuracy.
Yes, there are a few key challenges and considerations when working with single bonds. One common issue is ensuring the proper orientation and alignment of single bonds within larger molecular structures, as this can impact the overall stability and reactivity of the compound. Additionally, optimizing the strength and directionality of single bonds can be crucial for achieving desired physical and chemical properties in materials and devices. Researchers may also need to carefully consider the potential for steric hindrance or other spatial effects when designing molecules with multiple single bonds.
More about "Single bond"
Covalent Chemical Bonds | Organic Compounds | Materials Science | Drug Design | Bonding Properties | Bond Optimization | Reproducibility | Research Accuracy | PubCompare.ai | Adper Single Bond 2 | Single Bond Universal | Topspin 3.2 | Bond RX | Avance III | Filtek Z350 XT | Single Bond Universal Adhesive | Adper Single Bond | Single Bond 2 | Maestro Single bonds refer to the covalent chemical bonds where two atoms share a single pair of electrons.
This type of bond is essential for the stability and structure of many organic compounds, making it a crucial topic in fields like chemistry, materials science, and drug design.
Understanding the properties and behavior of single bonds is vital for researchers to optimize these bonds and enhance their research outcomes.
PubCompare.ai's AI-driven platform provides an innovative approach to helping researchers locate and compare the best protocols from literature, preprints, and patents related to single bond optimization.
This intuitive tool allows users to access data-driven comparisons and insights, improving reproducibility and research accuracy.
Products like Adper Single Bond 2, Single Bond Universal, Topspin 3.2, Bond RX, Avance III, Filtek Z350 XT, Single Bond Universal Adhesive, Adper Single Bond, Single Bond 2, and Maestro are examples of tools and materials that can be used in single bond research and optimization.
By leveraging the power of AI and data-driven insights, PubCompare.ai empowers researchers to enhance their understanding of single bonds and optimize their research processes, leading to more reliable and impactful discoveries.
