Molecular Structure Prediction Research Articles

Molecular Prediction and Correlation of the Structure and Function of Universal Stress Protein A (UspA) from Salmonella Typhimurium.

Salmonella Typhimurium (ST) is a zoonotic pathogen that can cause gastroenteritis in humans when they consume contaminated food or water. When exposed to various stressors, both from living organisms (biotic) and the environment (abiotic), Salmonella Typhimurium produces Universal Stress Proteins (USPs). These proteins are gaining recognition for their crucial role in bacterial stress resistance and the ability to enter a prolonged state of growth arrest. Additionally, USPs exhibit diverse structures due to the fusion of the USP domain with different catalytic motifs, enabling them to participate in various reactions and cellular activities during stressful conditions. In this particular study, researchers cloned and analyzed the uspA gene obtained from poultry-derived strains of Salmonella Typhimurium. The gene comprises 435 base pairs, encoding a USP family protein consisting of 144 amino acids. Phylogenetic analysis demonstrated a close relationship between the uspA genes of Salmonella Typhimurium and those found in other bacterial species. Weused molecular dynamics simulations and 3D structure prediction to ensure thatthe USPA protein was stable.Furthermore, we alsocarried out motif search and network analysis of protein-protein interactions. The findings from this study offer valuable insights for the development of inhibitors targeted against Salmonella Typhimurium.

Molecular structure prediction, experimental and theoretical properties, and biological activities of (E)-5-nitro-3-(phenylimino) indolin-2-one- in-vitro against 60 lethal tumour cell lines

In this study an isatin derivative (E)-5-nitro-3-(phenylimino) indolin-2-one (5NPI) has been synthesized and examined utilizing quantum chemical calculations of DFT approach for structural optimization, electronic and vibrational characteristics. Functional groups have been detected using FT-IR spectra and compared with simulated spectra. To model UV–vis absorption and identify electronic characteristics in solvents and gas phase, TD-DFT computations have been performed. The GIAO technique was used to measure chemical shifting in NMR in several different solvents. Theoretical parameters coincide perfectly with experimental data. HOMO and LUMO band gaps reflect chemical activity and have satisfactory charge exchange inside the molecule. RDG analysis, topological and NBO investigations have been used to study weak intermolecular interactions. Reactive regions of the 5NPI compound have been determined using MEP and Fukui functions. NLO behavior has been computed using hyperpolarizability parameters. The anticancer activities of the compound 5NPI were evaluated from the National Cancer Institute (NCI), USA and subjected to in-silico analysis such as drug-likeness, ADMET and molecular docking. The compound 5NPI has been docked with 3RUX, 8ADA, 3TFU and 6TOJ proteins and showed moderate binding affinity as −7.14, −6.42, −6.68 and −8.41 kcal/mol respectively. Compound 5NPI is used as a lead molecule in the treatment of TB along with various tumors.

Analysis of AlphaFold and molecular dynamics structure predictions of mutations in serpins.

Serine protease inhibitors (serpins) include thousands of structurally conserved proteins playing key roles in many organisms. Mutations affecting serpins may disturb their conformation, leading to inactive forms. Unfortunately, conformational consequences of serpin mutations are difficult to predict. In this study, we integrate experimental data of patients with mutations affecting one serpin with the predictions obtained by AlphaFold and molecular dynamics. Five SERPINC1 mutations causing antithrombin deficiency, the strongest congenital thrombophilia were selected from a cohort of 350 unrelated patients based on functional, biochemical, and crystallographic evidence supporting a folding defect. AlphaFold gave an accurate prediction for the wild-type structure. However, it also produced native structures for all variants, regardless of complexity or conformational consequences in vivo. Similarly, molecular dynamics of up to 1000 ns at temperatures causing conformational transitions did not show significant changes in the native structure of wild-type and variants. In conclusion, AlphaFold and molecular dynamics force predictions into the native conformation at conditions with experimental evidence supporting a conformational change to other structures. It is necessary to improve predictive strategies for serpins that consider the conformational sensitivity of these molecules.

Machine learning for <i>in silico</i> protein research

<i>In silico</i> protein calculation has been an important research subject for a long time, while its recent combination with machine learning promotes the development greatly in related areas. This review focuses on four major fields of the <i>in silico</i> protein research that combines with machine learning, which are molecular dynamics, structure prediction, property prediction and molecule design. Molecular dynamics depend on the parameters of force field, which is necessary for obtaining accurate results. Machine learning can help researchers to obtain more accurate force field parameters. In molecular dynamics simulation, machine learning can also help to perform the free energy calculation in relatively low cost. Structure prediction is generally used to predict the structure given a protein sequence. Structure prediction is of high complexity and data volume, which is exactly what machine learning is good at. By the help of machine learning, scientists have gained great achievements in three-dimensional structure prediction of proteins. On the other hand, the predicting of protein properties based on its known information is also important to study protein. More challenging, however, is molecule design. Though marching learning has made breakthroughs in drug-like small molecule design and protein design in recent years, there is still plenty of room for exploration. This review focuses on summarizing the above four fields andlooks forward to the application of marching learning to the <i>in silico</i> protein research.

DeepSPInN – deep reinforcement learning for molecular structure prediction from infrared and 13C NMR spectra

DeepSPInI is a deep reinforcement learning method that predicts the molecular structure when given infrared and 13C nuclear magnetic resonance spectra with an accuracy of 91.5%.

Harnessing the power of curvilinear internal coordinates: from molecular structure prediction to vibrational spectroscopy

Different standard VPT2 codes employ Cartesian coordinates for the computation of rotational and vibrational spectroscopic parameters. However, curvilinear internal coordinates offer a number of advantages provided that a general non-redundant set of coordinates can be built and employed in an unsupervised workflow. In the present paper I summarize the main results and perspectives of a general engine employing curvilinear internal coordinates and perturbation theory for the computation of rotational and vibrational spectroscopic parameters of large molecules beyond the conventional rigid rotor/harmonic oscillator model. Some examples concerning biomolecule building blocks are discussed in some detail in order to better analyze the performance of the proposed strategy.

Antihypertensive potential of the stem bark of Canarium schweinfurthii Engl. (Burseraceae) in wistar rats: UPLC-ESI-QToF-MS/MS-based prediction of antihypertensive phytochemicals

Ethnopharmacological relevanceCanarium schweinfurthii, also called ‘’Elemierd’Afrique’’, is used in Cameroonian folk medicine (bark decoction) to treat patients suffering from hypertension.Aim of the study: This study aimed at evaluating the antihypertensive activities of the stem bark of Canarium schweinfurthii and identifying potential compounds present in its extract that may support or oppose its ethnomedicinial use. Materials and methodsStem bark extract of Canarium schweinfurthii was prepared by maceration using 70 % ethanol followed by redissolution in methanol and hyphenated. Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS/MS) analysis for the detection and characterisation of secondary metabolites. Antihypertensive effects were assessed in Wistar rats after induction of hypertension with sodium chloride (NaCl) 18 % at a dose of 0.01mL/gbody weight once a day for four weeks.Hemodynamic parameters were measured weekly by anon-invasive method using the CODA system. ResultsThe ethanolic bark extract of C. schweinfurthii significantly inhibited the increase of blood pressure with a maximum of 23.18 % (systolic pressure, p < 0.0001), 24.77 % (diastolic pressure, p < 0.001) and 22.95 % (mean pressure, p < 0.0001) at a dose of 200 mg/kgbody weight at the 4th week, compared to agroup of Wistar rats that received only NaCl (negative control). Similarly, the extract significantly inhibited the increase in heart rate by 18.84 % (p < 0.001) at 200 mg/kgbody weight at week four. Hematological parameters did not differ significantly between the extract-treated and control groups. The UPLC-MS/MS spectrometric analysis provided evidence for the presence of several C30 terpenoids containing three or five oxygen atoms and exhibiting pentacyclic triterpenoid structures, as well as C29 terpenoids and related compounds containing nitrogen in addition to oxygen, using spectral matching, and in silico molecular formula and structure prediction. Additionally, two features were annotated with high-confidence as lignans, structurally closely related to hinokinin and dehydrocubebin through MS/MS-based in silico structure prediction using CSI: Finger ID in SIRIUS5. The lignans have been previously reported from stem bark of plants belonging to the Burseraceae family. Conclusion: The ethanolic stem bark extract of C. schweinfurthii demonstrated antihypertensive properties on the tested Wistar rats. These results support the ethnopharmacological use of C. schweinfurthii concoctions for the treatment of hypertension and suggest a protective effect against salt damage, hypothetically by the up regulation of antioxidative enzymes and/or lipids, mitigatings membrane peroxidation.

Machine learning and DFT-based combined framework for predicting transmission spectra of quantum-confined bio-molecular nanotube.

The Adenine-based nanotube is theoretically designed, and its transmission spectra are investigated. The quantum-confined Adenine nanotube shows electronic transmission of the carrier at minimum stress. In this paper, the prediction of transmission spectra of the quantum-confined bio-molecular nanotube is investigated and deeply studied. Molecular level structure prediction and their electronic characterization can be possible with ab initio accuracy using a machine learning algorithmic approach. At the molecular level, it is difficult to predict quantum transmission spectra as these results are always hampered by the carrier backscattering effect. However, mostly these predictive models are available for intrinsic semi-conducting materials and other inorganic structures. Machine learning algorithms are designed to predict the electronic properties of the nano-scale structure. This task is even more difficult when quantum-confined molecular arrangements are considered, whose transmission spectra are sensitive to the confinements applied. This paper presents an effective machine learning algorithms framework for predicting transmission spectra of quantum-confined nanotubes from their geometries. In this paper, we consider regression machine learning algorithms to find maximum accuracy with varying configurations and geometries to excerpt their atoms' local environment information. The Hamiltonian components are then used to enable the utilization of the information to predict the electronic structure at any arbitrary sampling point or k-point. The theoretical basics introduced in this process help to capture and incorporate minor changes in quantum confinements into transmission spectra and provide the framework algorithm with more accuracy. This paper shows the ability to predict the accurate algorithmic models of the Adenine nanotube. In this framework, we have considered a tiny data set to achieve a rapid and reliable method for electronic structure determination and also propose the best algorithm for predictive model analysis.

Extracting Structural Information from Physicochemical Property Measurements Using Machine Learning─A New Approach for Structure Elucidation in Non-targeted Analysis.

Non-targeted analysis (NTA) has made critical contributions in the fields of environmental chemistry and environmental health. One critical bottleneck is the lack of available analytical standards for most chemicals in the environment. Our study aims to explore a novel approach that integrates measurements of equilibrium partition ratios between organic solvents and water (KSW) to predictions of molecular structures. These properties can be used as a fingerprint, which with the help of a machine learning algorithm can be converted into a series of functional groups (RDKit fragments), which can be used to search chemical databases. We conducted partitioning experiments using a chemical mixture containing 185 chemicals in 10 different organic solvents and water. Both a liquid chromatography quadrupole time-of-flight mass spectrometer (LC-QTOF MS) and a LC-Orbitrap MS were used to assess the feasibility of the experimental method and the accuracy of the algorithm at predicting the correct functional groups. The two methods showed differences in log KSW with the QTOF method showing a mean absolute error (MAE) of 0.22 and the Orbitrap method 0.33. The differences also culminated into errors in the predictions of RDKit fragments with the MAE for the QTOF method being 0.23 and for the Orbitrap method being 0.31. Our approach presents a new angle in structure elucidation for NTA and showed promise in assisting with compound identification.

Ensemble Reweighting Using Cryo-EM Particle Images.

Cryo-electron microscopy (cryo-EM) has recently become a leading method for obtaining high-resolution structures of biological macromolecules. However, cryo-EM is limited to biomolecular samples with low conformational heterogeneity, where most conformations can be well-sampled at various projection angles. While cryo-EM provides single-molecule data for heterogeneous molecules, most existing reconstruction tools cannot retrieve the ensemble distribution of possible molecular conformations from these data. To overcome these limitations, we build on a previous Bayesian approach and develop an ensemble refinement framework that estimates the ensemble density from a set of cryo-EM particle images by reweighting a prior conformational ensemble, e.g., from molecular dynamics simulations or structure prediction tools. Our work provides a general approach to recovering the equilibrium probability density of the biomolecule directly in conformational space from single-molecule data. To validate the framework, we study the extraction of state populations and free energies for a simple toy model and from synthetic cryo-EM particle images of a simulated protein that explores multiple folded and unfolded conformations.

DFT Method Used for Prediction of Molecular and Electronic Structures of Mn(VI) Macrocyclic Complexes with Porhyrazine/Phthalocyanine and Two Oxo Ligands.

By using the data of the DFT quantum chemical calculation in the OPBE/TZVP and B3PW91/TZVP levels, the possibility of the existence of a manganese(VI) heteroligand complex containing porphyrazine or its tetra[benzo] derivative (phthalocyanine) and two oxygen (O2-) ligands, which is still unknown for this element, is shown. The parameters of the molecular structure, multiplicity of the ground state, NBO analysis data and standard thermodynamic parameters (enthalpy ΔH0f, entropy S0f and Gibbs's energy ΔG0f of formation) of each of these metal macrocyclic compounds are presented. Additionally, it is noted that, based on the totality of structural data obtained by the above versions of the DFT method, the existence of a similar complex of manganese with di[benzo] derivative of porhyrazine and two oxygen (O2-) ligands seems doubtful.

Quantum-Chemical Prediction of Molecular and Electronic Structure of Carbon-Nitrogen Chemical Compound with Unusual Ratio Atoms: C(N20).

Using various versions of quantum-chemical calculation, namely four versions of density functional theory (DFT), (DFT B3PW91/TZVP, DFT M06/TZVP, DFT B3PW91/Def2TZVP, and DFT M06/Def2TZVP) and two versions of the MP method (MP2/TZVP and MP3/TZVP), the existence possibility of the carbon-nitrogen-containing compound having an unusual M: nitrogen ratio of 1:20, unknown for these elements at present, was shown. Structural parameters data are presented; it was noted that, as may be expected, CN4 grouping has practically a tetrahedral structure, and the chemical bond lengths formed by nitrogen atoms and a carbon atom in the frameworks of each of the calculation methods indicated above are equal to each other. Thermodynamical parameters, NBO analysis data, and HOMO/LUMO images for this compound are also presented. A good agreement between the calculated data obtained using the above three quantum-chemical methods was noticed, too.

An equivariant generative framework for molecular graph-structure Co-design.

Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine learning-based generative models have emerged as promising approaches for de novo molecule design. However, further refinement of methodology is highly desired as most existing methods lack unified modeling of 2D topology and 3D geometry information and fail to effectively learn the structure-property relationship for molecule design. Here we present MolCode, a roto-translation equivariant generative framework for molecular graph-structure Co-design. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Extensive experimental results show that MolCode outperforms previous methods on a series of challenging tasks including de novo molecule design, targeted molecule discovery, and structure-based drug design. Particularly, MolCode not only consistently generates valid (99.95% validity) and diverse (98.75% uniqueness) molecular graphs/structures with desirable properties, but also generates drug-like molecules with high affinity to target proteins (61.8% high affinity ratio), which demonstrates MolCode's potential applications in material design and drug discovery. Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation.

Computational and Functional Insights of Protein Misfolding in Neurodegeneration.

Protein folding is the process by which a polypeptide chain self-assembles into the correct three-dimensional structure, so that it ends up in the biologically active, native state. Under conditions of proteotoxic stress, mutations, or cellular aging, proteins can begin to aggregate into non-native structures such as ordered amyloid fibrils and plaques. Many neurodegenerative diseases involve the misfolding and aggregation of specific proteins into abnormal, toxic species. Experimental approaches including crystallography and AFM (atomic force microscopy)-based force spectroscopy are used to exploit the folding and structural characterization of protein molecules. At the same time, computational techniques through molecular dynamics, fold recognition, and structure prediction are widely applied in this direction. Benchmarking analysis for combining and comparing computational methodologies with functional studies can decisively unravel robust interactions between the side groups of the amino acid sequence and monitor alterations in intrinsic protein dynamics with high precision as well as adequately determine potent conformations of the folded patterns formed in the polypeptide structure.

Molecular Docking, Multicomponent One-pot Synthesis of Pyrimidine Derivatives as Anti-mycobacterial Agents

Background: Molecular docking study is used significantly in the drug discovery process for predicting the interaction between drug and receptor. This technique has been used commonly to identify the binding affinity and orientation of drug molecules at the binding site of the target. The main objectives of docking studies include accurate modeling of molecular structure and precise prediction of the biological activity of the drug molecules. Methods: Based on this concept, a series of 2-amino-6-(substituted phenyl)-4-oxo-4,5-dihydropyrimidine-5-carbonitrile derivatives have been designed and synthesized via multi-component reaction. The synthetic protocol involves the one-pot, three-component reaction be-tween equimolar quantities of substituted benzaldehydes, ethyl cyanoacetate, and guanidine in an ethanolic sodium hydroxide solution. Results: The characterization of the titled compounds was carried out by assessing infrared spectroscopy (IR), proton nuclear magnetic resonance (1H-NMR), and mass spectrometry (MS) data. The synthesized compounds were screened for their in vitro anti-mycobacterial activity by using the luciferase reporter phage (LRP) assay method. Conclusion: The determination of anti-mycobacterial activity was carried out in terms of the percent reduction in the relative light unit (RLU). The test compounds displayed significant ac-tivity against Mycobacterium strain H37Rv in comparison to isoniazid as a standard drug.

PREDICTION OF DIPROPYL SULFONE PROPERTIES BY DENSITY FUNCTIONAL THEORY METHODS: CONFORMATIONAL ANALYSIS AND SIMULATED IR SPECTRUM

In this work, the conformational and vibrational analysis of dipropyl sulfone in its isolated gaseous state with identification of all stable conformers, their energy and degeneracy, relative population determined by Boltzmann distribution, as well as IR spectra have been performed by density functional theory (DFT) methods. Several DFT methods and basis sets were tested. It was demonstrated that the various local and hybrid DFT functionals such as well-known B3LYP, regardless of the size of basis sets, completely fail in the prediction of correct molecular structures, let alone the IR spectra. It was found that only long-range corrected hybrid density functionals, combined with decently sized basis sets are capable to predict correct values of dihedral angles between non-bonded atomic groups: the most important coordinates in conformational analysis. Thus, wB97XD/6-311++G(2df,2pd) method/basis set combination appears to be the best method for the titled system both in terms of geometry and IR spectra prediction. A detailed analysis of the potential energy surface revealed the existence of 28 distinct conformers with various populations at 298 K, which have significant impact in the simulated IR spectra. The linear scaling equation (LSE) fitting methodology was successfully adopted for the calibration of wavenumbers and achievement of the best match between theoretical and experimental absorption regions of functional groups in sulfones. Moreover, in the construction of the simulated IR spectra, the Lorentzian broadening of each calculated mode with different full widths at half maximum was considered to obtain extinction coefficients, thus more realistic ε(ν) dependency, that is directly comparable with experimental spectra. The authenticity of the results obtained have been verified by comparison with existing experimental literature data on sulfones.

Comprehensive Structural and Functional Characterization of a Seed γ-thionin as a Potent Bioactive Molecule Against Fungal Pathogens and Insect Pests.

Fungi and insect pests ruin stored crop grain, which results in millions of dollars of damage, presenting an ongoing challenge for farmers in addition to diminishing the safety of stored food. A wide-range defensive system against pathogens is needed to reduce or even eliminate the dependence of the crop yield upon the use of pesticides. Plant defensins (γ-thionins) are antimicrobial peptides (AMPs) that are a component of the host defense system. They are known to interact with cell membranes to exhibit antifungal and insecticidal activity. They exhibit a broad range of activities against fungi and insects and are effective at low concentrations. Thionins act on membranes, greatly reducing the development of pathogen resistance. The aim of this study is to investigate a bioactive molecule that acts against fungal pathogens and stored grain insect pests. γ-thionin protein was extracted from Brassica oleracea L. var. capitata f. alba (white cabbage) seed powder in phosphate buffer (100 mM, pH 7.0) and was identified by MALDI-TOF/TOF. The crude extract was subjected to 70% ammonium sulfate saturation followed by gel filtration chromatography. The disc diffusion assay along with a microtiter bioassay was used to determine the antifungal activity of the protein against phytopathogenic fungi. The insecticidal efficacy was evaluated by feeding insect pests with food contaminated with the purified protein. Additionally, an in silico molecular structure prediction study of the protein was performed using Auto Dock Vina for molecular docking of the protein with either fungal membrane moieties or α-amylase from Tenebrio molitor L. MD simulations of protein-ligand complexes were conducted using Schrodinger's Desmond module. γ-Thionin (BoT) was purified from white cabbage seeds and showed 100% homology with thionin (Brassica oleracea L. var. viridis) and 80% homology with defensin-like protein 1 (Raphanus sativus L.), respectively. BoT significantly inhibited the mycelial growth of Aspergillus niger van Tieghem and Aspergillus flavus Link at a concentration of 2 μM. Similarly, 0.12 μM BoT treatment resulted in significant mortality of Tribolium castaneum Herbst and Sitophilus oryzae L. Molecular docking and MD simulation of BoT confirmed the strong binding affinity with fungal membrane moieties (phosphatidylinositol 4,5-bisphosphate and phosphatidic acid), which causes disruption of the cell membrane and leakage of the cellular contents, leading to cell death. BoT blocked the active site of α-amylase, and as a result of the inactivation of this gut enzyme, the digestive systems of insects were disturbed, resulting in their deaths. This study revealed that γ-thionin is a good antifungal and insecticidal agent that could be used as an alternate to fungicides and insecticides.

Analysis of a thermostable lipase from CTG-clade yeast: Molecular expression, characterization and structure prediction

Analysis of a thermostable lipase from CTG-clade yeast: Molecular expression, characterization and structure prediction

Molecular Phylogeny, DNA Barcoding, and ITS2 Secondary Structure Predictions in the Medicinally Important Eryngium Genotypes of East Coast Region of India.

Commercial interest in the culinary herb, Eryngium foetidum L., has increased worldwide due to its typical pungency, similar to coriander or cilantro, with immense pharmaceutical components. The molecular delimitation and taxonomic classification of this lesser-known medicinal plant are restricted to conventional phenotyping and DNA-based marker evaluation, which hinders accurate identification, genetic conservation, and safe utilization. This study focused on species discrimination using DNA sequencing with chloroplast–plastid genes (matK, Kim matK, and rbcL) and the nuclear ITS2 gene in two Eryngium genotypes collected from the east coast region of India. The results revealed that matK discriminated between two genotypes, however, Kim matK, rbcL, and ITS2 identified these genotypes as E. foetidum. The ribosomal nuclear ITS2 region exhibited significant inter- and intra-specific divergence, depicted in the DNA barcodes and the secondary structures derived based on the minimum free energy. Although the efficiency of matK genes is better in species discrimination, ITS2 demonstrated polyphyletic phylogeny, and could be used as a reliable marker for genetic divergence studies understanding the mechanisms of RNA molecules. The results of this study provide insights into the scientific basis of species identification, genetic conservation, and safe utilization of this important medicinal plant species.

HATS5m as an Example of GETAWAY Molecular Descriptor in Assessing the Similarity/Diversity of the Structural Features of 4-Thiazolidinone.

Among the various methods for drug design, the approach using molecular descriptors for quantitative structure–activity relationships (QSAR) bears promise for the prediction of innovative molecular structures with bespoke pharmacological activity. Despite the growing number of successful potential applications, the QSAR models often remain hard to interpret. The difficulty arises from the use of advanced chemometric or machine learning methods on the one hand, and the complexity of molecular descriptors on the other hand. Thus, there is a need to interpret molecular descriptors for identifying the features of molecules crucial for desirable activity. For example, the development of structure–activity modeling of different molecule endpoints confirmed the usefulness of H-GETAWAY (H-GEometry, Topology, and Atom-Weights AssemblY) descriptors in molecular sciences. However, compared with other 3D molecular descriptors, H-GETAWAY interpretation is much more complicated. The present study provides insights into the interpretation of the HATS5m descriptor (H-GETAWAY) concerning the molecular structures of the 4-thiazolidinone derivatives with antitrypanosomal activity. According to the published study, an increase in antitrypanosomal activity is associated with both a decrease and an increase in HATS5m (leverage-weighted autocorrelation with lag 5, weighted by atomic masses) values. The substructure-based method explored how the changes in molecular features affect the HATS5m value. Based on this approach, we proposed substituents that translate into low and high HATS5m. The detailed interpretation of H-GETAWAY descriptors requires the consideration of three elements: weighting scheme, leverages, and the Dirac delta function. Particular attention should be paid to the impact of chemical compounds’ size and shape and the leverage values of individual atoms.