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That "... the examples given clearly show the high-quality results based on optimal molecular descriptors ...", as it really is, the comparison of both sets of results here is useful to derive some valid conclusions on the present method employing CWLIMG.The average deviations are lower for our calculations, and it results more meaningful when on takes into account that data taken from ref. 4 is based upon a two variables equation (descriptors p1 and p2, i.e. weighted paths of length one and length two, respectively, Eqs. 7 and 10 in ref.4). Besides, one must take into account that our results for the molecular test set are completely predictive, that is to say, they were no included in the molecular set employed to determine the fitting equation, while the Randic and Basak's results do not make this differentiation (i.e. the whole set of 58 molecules was used to calculate the regression relationships), so that there is not any genuine prediction within their values. In order to justify our claim of having gotten better results, it is instructive to note that, in general, the statistical parameters for the test set are even better than those of Randic and Basak's corresponding values for the whole set of 58 molecules. Another way to recognize the better quality of our predictions is considering the number of predicted bp with a deviation larger than 5°C. In fact, our predicted set of bp registers just 4 cases, while Randic and Basak's data present 10 predictions with a deviation larger than 5°C.We have tried other alternative ways to choose the members of the training and test sets, but final results are practically the same. IV - ConclusionsThe results presented in this paper clearly show the very good outcomes arising from the use of the CWLIMG which, on one hand uses just only one molecular descriptor and on the other hand give correlations with significative reduced deviations with regard to other similar approaches. It seems to be a very good prospect in resorting to molecular descriptors having an intrinsic flexibility, as it is the case of the present one, because they yield quite satisfactory predictions.In addition, it is not necessary to employ higher order polynomial relationships in order to improve linear equations or/and to be dependent upon the choice of the training set to get the most suitable fitting equation.Present results agree with those published before on the use of CWLIMG /14-18/ and they further illuminate the appropriateness of using this molecular descriptor within the realm of QSAR/QSPR theory.Perhaps, before establishing more definitive conclusions about the goodness degree of this sort of flexible molecular descriptor it should be necessary and convenient to study other molecular sets and/or other physical chemistry properties and biological activities. At present, research along these lines are under development in our laboratories and results will be published elsewhere in the near future. ReferencesTrinajstic, N. Chemical Graph Theory, 2nd revised edition; CRC Press: Boca Raton, Floirda, 1992; Chapter 3. [Google Scholar]Turro, N. J. Angew. Chem. Int.

Defines a “degree” of an atom as the number of adjacent non-hydrogen atoms • Bond connectivity value is the reciprocal of the square root of the product of the degree of the two atoms in the bond. • Branching index is the sum of the bond connectivities over all bonds in the molecule. • Chi indexes – introduces valence values to encode sigma, pi, and lone pair electronsKappa Shape Indexes • Characterize aspects of molecular shape • Compare the molecule with the “extreme shapes” possible for that number of atoms • Range from linear molecules to completely connected graph2D Fingerprints • Two types: • One based on a fragment dictionary • Each bit position corresponds to a specific substructure fragment • Fragments that occur infrequently may be more useful • Another based on hashed methods • Not dependent on a pre-defined dictionary • Any fragment can be encoded • Originally designed for substructure searching, not for molecular descriptorsAtom-Pair Descriptors • Encode all pairs of atoms in a molecule • Include the length of the shortest bond-by-bond path between them • Elemental type plus the number of non-hydrogen atoms and the number of π-bonding electronsBCUT Descriptors • Designed to encode atomic properties that govern intermolecular interactions • Used in diversity analysis • Encode atomic charge, atomic polarizability, and atomic hydrogen bonding abilityDESCRIPTORS BASED ON 3D REPRESENTATIONS • Require the generation of 3D conformations • Can be computationally time consuming with large data sets • Usually must take into account conformational flexibility • 3D fragment screens encode spatial relationships between atoms, ring centroids, and planesPharmacophore Keys & Other 3D Descriptors • Based on atoms or substructures thought to be relevant for receptor binding • Typically include hydrogen bond donors and acceptors, charged centers, aromatic ring centers and hydrophobic centers • Others: 3D topographical indexes, geometric atom pairs, quantum mechanical calculations for HUMO and LUMODATA VERIFICATION AND MANIPULATION • Data spread and distribution • Coefficient of variation (standard deviation divided by the mean) • Scaling (standardization): making sure that each descriptor has an equal chance of contributing to the overall analysis • Correlations • Reducing the dimensionality of a data set: Principal Components Analysis

Occurrences in molecular datasets. A structural pattern refers to a specific arrangement of atoms and bonds within a molecule, identifiable and characterizable as a substructure. These patterns represent recurring molecular features, such as functional groups or distinct atom connectivities. Structural patterns play a pivotal role in cheminformatics, particularly in structure-activity relationship (SAR) studies, pattern recognition, and the prediction of molecular properties and behavior.In alvaDesc, structural patterns are defined using the SMARTS syntax.Other FeaturesOne of the most innovative features of alvaDesc is its capability to handle both full-connected and non-full-connected molecular structures, such as salts and ionic liquids. All of the molecular descriptor calculation algorithms provide different theoretical approaches for the calculation of molecular descriptors on such structures.Different tools are provided to carry out a first exploration of your molecular dataset:Molecule structure verification using PubChem and Google Patents servicesMolecule structure visualisation and filteringPrincipal Component Analysis (PCA), t-SNE and correlation analysisDue to its capability of calculating large numbers of molecular descriptors, alvaDesc provides variable reduction tools, including the fast V-WSP method (variable reduction method adapted from space-filling designs).VideoA short video introduction:PlatformsThe software is 64bit and it’s available for Windows, Linux and macOS. It is provided both as an easy to use command line tool and as an intuitive graphical interface.With the release of alvaDesc 3.0, experience significantly enhanced calculation speeds on M Series processors. Learn more about the benchmarking results and performance improvements here.Performance comparison of descriptor calculation times for 2D (4,215 descriptors) and all descriptors (5,799 descriptors, including 3D) across three configurations: Intel Mac (x86_64), Apple Silicon via Rosetta 2, and native Apple Silicon. Native Apple Silicon builds significantly reduce computation time, demonstrating the benefits of optimized support for M Series processors.How to CiteIf you reference alvaDesc in an academic paper or publication, you can find the correct citation for your version by:Running alvaDescGUI and selecting “About alvaDesc” from the menuUsing the command alvaDescCLI –citeAdditionally, please consider citing the following papers:Mauri, A. (2020). alvaDesc: A Tool to Calculate and Analyze Molecular Descriptors and Fingerprints. In K. Roy (Ed.), Ecotoxicological QSARs (pp. 801–820). Humana Press Inc. A., & Bertola, M. (2022). Alvascience: A New Software Suite for the QSAR Workflow Applied to the Blood–Brain Barrier Permeability. International Journal of Molecular Sciences, 23(21), 12882. perfect tool to prepare your molecular dataset for alvaDesc is alvaMoleculeCreate QSAR/QSPR models with alvaModel starting from an alvaDesc projectA tutorial showing how to build a QSAR model using

His co-workers have implemented molecular docking using AutoDock 4.2 Suite with Lamarckian genetic algorithm (LGA) as shown in Fig. 16 and the predicted binding energy was found to be − 2.32 kcal/mol, that indicates a stable absorption because of potential interactions on the surface.Fig. 16Docked complex structure of UiO-66(Zr) metal organic framework (MOF) and chrysene (CRY), a toxic and hazardous polycyclic aromatic hydrocarbon (PAH) pollutant using AutoDock 4.2 [115]. UiO-66(Zr) MOF (receptor) and CRY (ligand) are shown in stick and ball-and-stick models, respectively. UiO-66(Zr) is marked in white, violet, and red color. Four benzene rings of CRY are marked in blue and light gray color [115]. (Color figure online)Full size imageThe calculate energy due to different types of interaction potentials, such as van der Waals, hydrogen bonding and desolvation was − 3.1 kcal/mol and electrostatic was 0.79 kcal/mol. CRY was found to show electrostatic interaction with Zr4+ ion of the MOF.To understand the cause of instant isotopic exchange reaction in silver nanoparticles cluster, Chakraborty et al. have carried out molecular docking study between two [Ag25(DMBT)18]− (DBMT for 2,4-dimethylbenzenethiol, which acts as a protecting ligand) clusters using AutoDock 4.2 with Lamarckian genetic algorithm [100, 116]. [Ag25(DMBT)18]− has been used both as receptor and ligand in the docking process. The binding energy was found to be − 23.7 kcal/mol. The docking result with least binding energy is shown in Fig. 17.Fig. 17Docked complex structure of two [Ag25(DMBT)18]− clusters using AutoDock 4.2 [100, 115]. Complex is shown in ball-and-stick model. Silver (Ag) and sulfur (S) atoms are shown in gray and yellow, respectively. C–H… π interactions are viewed in green dotted lines. Hydrogen (H) atoms and benzene rings associated with these interactions are viewed in red and blue, respectively. Other benzene rings not associated with these interactions are shown in green [116]. (Color figure online)Full size imageThe fluorescent cobalt oxide (CoO) umbelliferone nanoconjugate having anti-cancer activity can be used both as a drug and carrier. Ali et al. have conducted molecular docking studies applying the protein docking program, HEX 8.0.0 using Spherical Polar Fourier Correlations technique to find the binding interactions of the