Molecular Similarity Analysis Research Articles

Bioavailability and molecular recognition between secondary metabolites of Euphorbia hirta L. and 5-lipoxygenase using fragment molecular orbital method and pair interaction energy decomposition analysis

Is presented the bioavailability analysis for seven secondary metabolites from Euphorbia hirta L. (Euphorbiaceae). The bioactivity score, the molecular similarity analysis for each secondary metabolite, and the molecular docking 5-LOX/metabolite complex are also presented. For the similarity analysis, it is referenced the structure of allosteric LOX-5 inhibitor 3-acetyl-11-keto-beta-boswellic acid (AKBA). The fragment molecular orbital method (FMO) and pair interaction energy decomposition analysis (PIEDA) were applied to evaluate the interaction energy between secondary metabolites and the 5-LOX active site. Log P values of all the secondary metabolites studied were found to be higher than 5, contrary to what was expected with Lipinski’s rules of five. The reference LOX-5 ligand has a high electrostatic potential similarity with most secondary metabolites studied, with values from 0.667 to 0.730. Both AKBA and metabolites ligands were wedged between the two domains of LOX-5, and the main residues contact were LEU-66, ARG-68, VAL-110, HIS-130, ILE-126, GLN, 129, and LYS-133. The PIEDA analysis showed that stabilization in the 5-LOX active site is mainly dominated by hydrophobic interactions, with values up to -35 Kcal/mol. Due to the low presence of terminal-OH groups, electrostatic origin, and charge transfer interaction energies are also important but have values of less than 20 Kcal/mol.

Study anti-viral drugs for their efficiency against multiple SARS CoV-2 drug targets within molecular docking, molecular quantum similarity, and chemical reactivity indices frameworks

The study focused on drug discovery for COVID-19, emphasizing the challenges posed by the pandemic and the importance of understanding the virus’s biology. The research utilized molecular docking and quantum similarity analyses to explore potential ligands for SARS-CoV-2 RNA-dependent RNA polymerase. Docking Results Docking outcomes for various ligands, including Oseltamivir, Prochloraz, Valacyclovir, Baricitinib, Molnupiravir, Penciclovir, Famciclovir, Lamivudine, and Nitazoxanide, were presented. Interactions between ligands and specific residues in the RNA-dependent RNA polymerase were analyzed. Reactivity Descriptors Global parameters, such as electronic chemical potential, chemical hardness, global softness, and global electrophilicity, were computed for the ligands. For the local reactivity descriptors, the Fukui Functions were used. Fukui functions, representing electrophilic and nucleophilic sites, were calculated for selected ligands (Valacyclovir and Penciclovir). Nucleophilic character assignments for specific molecular regions were discussed, providing insights into potential charge-donating interactions. Results and Discussion Challenges in COVID-19 drug discovery, such as virus mutability, rapid evolution, and resource limitations, were summarized. Progress in vaccine development and the need for ongoing research to address variants and breakthrough cases were emphasized. Overlap Operator Analysis Higher MQSM between Lamivudine and Molnupiravir (0.5742) indicates structural and electronic similarity. Lowest MQSM between Oseltamivir and Prochloraz (0.2233) implies structural dissimilarity. Coulomb Operator Analysis Higher MQSM between Lamivudine and Molnupiravir (0.9178) suggests both structural and electronic similarity. Lowest MQSM between Baricitinib and Famciclovir (0.6001) indicates greater structural diversity. Measurements above 0.5 in Table 3 suggest electronic similarity, emphasizing the electronic aspects in molecular analysis. In this sense, it study employed a multi-faceted approach combining molecular docking, quantum similarity analyses, and chemical reactivity assessments to explore potential drug candidates for COVID-19. The findings provide valuable insights into ligand interactions, reactivity patterns, and the challenges associated with drug discovery in the context of the global pandemic.

Computational Integration of TRPV4 Antagonists: 3D QSAR, Molecular Docking, Molecular Dynamics Simulations, ADME/Tox, and Retrosynthesis studies

AbstractTRPV4 antagonists that could be potential drugs for treatment and management of neuropathic pain. This study was conducted to systematically design several amino derivatives of 2,4′‐dimethyl‐[4,5′‐bithiazol]‐2‐yl as antagonists. In silico computational methods, such as 3D quantitative structure‐activity relationship modeling (3D‐QSAR), molecular docking, and pharmacokinetic property assessment (ADMET), were used to discover new compounds with strong antagonistic activity. The best‐performing 3D‐QSAR model was developed using a partial least squares approach and comparative molecular similarity analysis (CoMSIA). The developed model demonstrated an excellent ability to correlate and predict properties (R2 = 0.932, Q2 = 0.620, and SEE = 0.109). It was observed that variations in biological activity were significantly influenced by interactions with steric, electrostatic, and hydrophobic fields. Molecular docking was used to validate the 3D‐QSAR methods and to explain the binding site and interactions between the most active ligands and the receptor. Based on these results, a new series of compounds was predicted. The best‐anchored molecules underwent MD simulation to confirm their dynamic behavior and stability, and they also underwent retrosynthetic analysis to assist in their synthesis.

Molecular structure similarity analysis using Tanimoto coefficient and its correlation analysis with Maltase-glucoamylase inhibitory activity of Nigella sativa’s compounds

Nigella sativa is one of the medicinal plants that are efficacious for treating diabetes mellitus. Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia due to damage of insulin action, insulin production, and/or both. In this study, the molecular structure similarity analysis of the compounds in Nigella sativa to acarbose and the correlation analysis of the similarity with its activity as antidiabetic with the mechanism of maltase glucoamylase (MGAM) inhibition was carried out. Similarity analysis has been done used Tanimoto coefficient. The prediction of MGAM inhibitory activity has been done using molecular docking with molegro virtual docker. The Kaempferol 3-glucosyl-(1-2)-galactosyl-(1-2)-glucoside; (S)-2,3-Epoxysqualene; Quercetin 3-glucosyl-(1-2)-galactosyl-(1-2)-glucoside; Oleic Acid has activity as an inhibitor of MGAM with rerank score -107.8770, -102.1760, -95.7338, -92.4246 respectively and these has Tanimoto score 0.426, 0.319, 0.413, 0.357 respectively. The correlation analysis obtained that there is a significant relationship between the Tanimoto Coefficient and Rerank Score with the opposite relationship because the correlation value is negative. Greater the degree of molecular structure similarity of Nigella sativa’s compounds to acarbose more likely has the similar biological activity as MGAM inhibitory

Study anti-viral drugs for their efficiency against multiple SARS CoV-2 drug targets within molecular docking, molecular quantum similarity, and chemical reactivity indices frameworks

The study focused on drug discovery for COVID-19, emphasizing the challenges posed by the pandemic and the importance of understanding the virus’s biology. The research utilized molecular docking and quantum similarity analyses to explore potential ligands for SARS-CoV-2 RNA-dependent RNA polymerase. Docking Results Docking outcomes for various ligands, including Oseltamivir, Prochloraz, Valacyclovir, Baricitinib, Molnupiravir, Penciclovir, Famciclovir, Lamivudine, and Nitazoxanide, were presented. Interactions between ligands and specific residues in the RNA-dependent RNA polymerase were analyzed. Reactivity Descriptors Global parameters, such as electronic chemical potential, chemical hardness, global softness, and global electrophilicity, were computed for the ligands. For the local reactivity descriptors, the Fukui Functions were used. Fukui functions, representing electrophilic and nucleophilic sites, were calculated for selected ligands (Valacyclovir and Penciclovir). Nucleophilic character assignments for specific molecular regions were discussed, providing insights into potential charge-donating interactions. Results and Discussion Challenges in COVID-19 drug discovery, such as virus mutability, rapid evolution, and resource limitations, were summarized. Progress in vaccine development and the need for ongoing research to address variants and breakthrough cases were emphasized. Overlap Operator Analysis Higher MQSM between Lamivudine and Molnupiravir (0.5742) indicates structural and electronic similarity. Lowest MQSM between Oseltamivir and Prochloraz (0.2233) implies structural dissimilarity. Coulomb Operator Analysis Higher MQSM between Lamivudine and Molnupiravir (0.9178) suggests both structural and electronic similarity. Lowest MQSM between Baricitinib and Famciclovir (0.6001) indicates greater structural diversity. Measurements above 0.5 in Table 3 suggest electronic similarity, emphasizing the electronic aspects in molecular analysis. In this sense, it study employed a multi-faceted approach combining molecular docking, quantum similarity analyses, and chemical reactivity assessments to explore potential drug candidates for COVID-19. The findings provide valuable insights into ligand interactions, reactivity patterns, and the challenges associated with drug discovery in the context of the global pandemic.

Navigating the Chemical Space of ENR Inhibitors: A Comprehensive Analysis.

Antimicrobial resistance is a global health threat that requires innovative strategies against drug-resistant bacteria. Our study focuses on enoyl-acyl carrier protein reductases (ENRs), in particular FabI, FabK, FabV, and InhA, as potential antimicrobial agents. Despite their promising potential, the lack of clinical approvals for inhibitors such as triclosan and isoniazid underscores the challenges in achieving preclinical success. In our study, we curated and analyzed a dataset of 1412 small molecules recognized as ENR inhibitors, investigating different structural variants. Using advanced cheminformatic tools, we mapped the physicochemical landscape and identified specific structural features as key determinants of bioactivity. Furthermore, we investigated whether the compounds conform to Lipinski rules, PAINS, and Brenk filters, which are crucial for the advancement of compounds in development pipelines. Furthermore, we investigated structural diversity using four different representations: Chemotype diversity, molecular similarity, t-SNE visualization, molecular complexity, and cluster analysis. By using advanced bioinformatics tools such as matched molecular pairs (MMP) analysis, machine learning, and SHAP analysis, we were able to improve our understanding of the activity cliques and the precise effects of the functional groups. In summary, this chemoinformatic investigation has unraveled the FAB inhibitors and provided insights into rational antimicrobial design, seamlessly integrating computation into the discovery of new antimicrobial agents.

In Silico Prediction of ADMET/Drug-likeness Properties of Bioactive Phloroglucinols from Hypericum genus.

Dimeric acylphloroglucinols occurring in species from sections Brathys and Trigynobrathys of the genus Hypericum exhibit acylfilicinic acid and acylphloroglucinol moieties linked by a methylene bridge. However, this chemical feature differs from hyperforin, from H. perforatum (Hypericum section), some dimeric acylphloroglucinols, such as uliginosin B, display similar pharmacological activities, namely antidepressant and antinociceptive. However, there is no knowledge about the pharmacokinetic profile and no toxicity studies of these compounds in intact mammals. to perform an in silico evaluation of the similarity, pharmacokinetics and toxicity (ADMET) properties of dimeric acylphloroglucinols from species native to Central and South America. ADMET prediction of eleven elected phloroglucinols followed by the chemical space evaluation of thirty-five dimeric acylphloroglucinols derivatives labeled according to their prenylation/geranylation pattern through principal component analysis (PCA). The similarity analysis was performed using the Tanimoto similarity index. ADMET properties were predicted with the open-source software SwissADME and pkCSM-pharmacokinetics. Several compounds showed good human intestinal absorption. However, they may present difficulties in crossing the blood-brain barrier, probably due to the high tPSA values. The predicted toxicity parameters indicated that most compounds have low toxicity. Most non-prenylated phloroglucinols were disposed into Lipinski's rule limits. Uliginosin B, isouliginosin B and japonica seem to be druglike compounds. The PCA model explained 77.49% of the total variance, and molecular similarity analyses revealed some expected similarities between isomers and different compounds. dimeric acylphloroglucinols may be promising drug candidates and deserve further pharmacological and medicinal chemistry studies.

Molecular mechanisms of caramel-like odorant-olfactory receptor interactions based on a computational chemistry approach

Molecular mechanisms of caramel-like odorant-olfactory receptor interactions were investigated based on molecular docking and molecular dynamics simulations. The transmembrane regions TM-3, TM-5 and TM-6 of receptors were main contributors of amino acid residues in the docking. Molecular docking results showed that hydrogen bonding and pi-pi stacking were the key forces for the stabilization of caramel-like odorants. The binding energies were positively correlated with the molecular weight of caramel-like odorants. Residues Asn155 (84%, OR2W1), Asn206 (86%, OR8D1), Ser155 (77%, OR8D1), Asp179 (87%, OR5M3), Val182 (84%, OR2J2) and Tyr260 (94%, OR2J2) with high frequencies played an important role in the complexes formation. Odorants 4-hydroxy-5-methylfuran-3(2H)-one (16#) and methylglyoxal (128#) were screened by molecular field-based similarity analysis, which tended to bind to the receptors OR1G1 and OR52H1 respectively, resulting a caramel-like aroma perception. The obtained results are useful for better understanding the perception of caramel-like odorants and their high-throughput screening.

Identification of dietary compounds that interact with the circadian clock machinery: Molecular docking and structural similarity analysis

The molecular clock is vital for regulating circadian rhythms in various physiological processes, and its dysregulation is associated with multiple diseases. As such, the use of small molecule modulators to regulate the molecular clock presents a promising therapeutic approach. In this study, we generated a homology model of the human circadian locomotor output cycles kaput (CLOCK) protein to evaluate its ligand binding sites. Using molecular docking, we obtained further insights into the binding mode of the control compound CLK8 and explored a selection of dietary compounds. Our investigation of dietary compounds was guided by their potential interactions with the retinoic acid-related orphan receptors RORα/γ, which are involved in circadian regulation. Through the molecular similarity and docking analyses, we identified oleanolic acid demethyl, 3-epi-lupeol, and taraxasterol as potential ROR-interacting compounds. These compounds may exert therapeutic effects through their modulation of RORα/γ activity and subsequently influence the molecular clock. Overall, our study highlights the potential of small molecule modulators in regulating the molecular clock and the importance of exploring dietary compounds as a source of such modulators. Our findings also provide insights into the binding mechanisms of CLK8 and shed light on potential compounds that can interact with RORs to regulate the molecular clock. Future investigations could focus on validating the efficacy of these compounds in modulating the molecular clock and their potential use as therapeutic agents.

Topological characterization of statistically clustered networks for molecular similarity analysis

Statistical clustering technique is extensively employed in molecular similarity analysis where molecules can be clustered on the basis of Euclidean similarity distances derived from rules of similarity measures. Such clusters can be extremely useful in quantitative structure–property relations as any representative within a cluster would have properties similar to other members of the cluster. Consequently, topological techniques for the characterization of such statistical clusters can be useful in many areas including drug research. We compute distance-based topological indices of the statistical cluster networks such as Szeged, Padmakar–Ivan and Mostar indices which provide different measures such as centrality, peripherality, and other properties of the corresponding networks.

Prediction of the Endocrine disruption profile of fluorinated biphenyls and analogues: An in silico study

Fluorinated biphenyls and their analogues (FBAs) are considered new persistent organic pollutants, but their endocrine-disrupting effects are still unknown. To fill this gap, the binding probability of 44 FBAs to different nuclear hormone receptors (NHRs) was predicted using Endocrine Disruptome. And molecular similarity and network toxicology analysis were used to strengthen the docking screening. The docking results showed that FBAs could have high binding potential for various NHRs, such as estrogen receptors β antagonism (ERβ an), liver X receptors α (LXRα), estrogen receptors α (ERα), and liver X receptors β (LXRβ). The similarity analysis found that the degree of overlap of the NHR repertoire was related to the Tanimoto coefficient of FBAs. Network toxicology verified a part of docking screening results and identified endocrine-disrupting pathways worthy of attention. This study found out potential endocrine-disrupting FBAs and their vulnerable, and developed a workflow that would leverage in silico approaches including molecular docking, similarity, and network toxicology for risk prioritization of potential endocrine-disrupting compounds.

In Silico Identification of Anti-SARS-CoV-2 Medicinal Plants Using Cheminformatics and Machine Learning.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of COVID-19, is spreading rapidly and has caused hundreds of millions of infections and millions of deaths worldwide. Due to the lack of specific vaccines and effective treatments for COVID-19, there is an urgent need to identify effective drugs. Traditional Chinese medicine (TCM) is a valuable resource for identifying novel anti-SARS-CoV-2 drugs based on the important contribution of TCM and its potential benefits in COVID-19 treatment. Herein, we aimed to discover novel anti-SARS-CoV-2 compounds and medicinal plants from TCM by establishing a prediction method of anti-SARS-CoV-2 activity using machine learning methods. We first constructed a benchmark dataset from anti-SARS-CoV-2 bioactivity data collected from the ChEMBL database. Then, we established random forest (RF) and support vector machine (SVM) models that both achieved satisfactory predictive performance with AUC values of 0.90. By using this method, a total of 1011 active anti-SARS-CoV-2 compounds were predicted from the TCMSP database. Among these compounds, six compounds with highly potent activity were confirmed in the anti-SARS-CoV-2 experiments. The molecular fingerprint similarity analysis revealed that only 24 of the 1011 compounds have high similarity to the FDA-approved antiviral drugs, indicating that most of the compounds were structurally novel. Based on the predicted anti-SARS-CoV-2 compounds, we identified 74 anti-SARS-CoV-2 medicinal plants through enrichment analysis. The 74 plants are widely distributed in 68 genera and 43 families, 14 of which belong to antipyretic detoxicate plants. In summary, this study provided several medicinal plants with potential anti-SARS-CoV-2 activity, which offer an attractive starting point and a broader scope to mine for potentially novel anti-SARS-CoV-2 drugs.

Comparative binding affinity analysis of dual CDK2/FLT3 inhibitors

AbstractSelective inhibition of cyclin‐dependent kinase 2 (CDK2) using small molecules is gaining popularity for the treatment of certain types of acute myeloid leukemia (AML). In this study, we used different molecular modeling techniques to investigate the structure–activity relationship (SAR) and binding modalities of dual CDK2/FLT3 inhibitors. The key chemical characteristics of the 3H‐pyrazolo[4,3‐f]quinoline derivatives were highlighted as descriptive colored contours using comparative molecular similarity analysis (CoMSIA). Modifying chemical groups in existing compounds along these contours could improve CDK2 selectivity over FMS‐like tyrosine kinase 3 (FLT3). We determined the ligand affinities for CDK2 by estimating the binding free energy using molecular mechanics generalized Born surface area (MM‐GBSA) and umbrella sampling (US) simulations. Reasonable correlations were found between the computed and experimental binding energies, suggesting that MM‐GBSA and US can be used to reliably predict the binding affinities of new compounds in the more potent CDK2 drug development process.

Withasomniferol C, a new potential SARS-CoV-2 main protease inhibitor from the Withania somnifera plant proposed by in silico approaches.

Exploring potent herbal medicine candidates is a promising strategy for combating a pandemic in the present global health crisis. In Ayurveda (a traditional medicine system in India), Withania somnifera (WS) is one of the most important herbs and it has been used for millennia as Rasayana (a type of juice) for its wide-ranging health benefits. WS phytocompounds display a broad spectrum of biological activities (such as antioxidant, anticancer and antimicrobial) modulate detoxifying enzymes, and enhance immunity. Inspired by the numerous biological actions of WS phytocompounds, the present investigation explored the potential of the WS phytocompounds against the SARS-CoV-2 main protease (3CLpro). We selected 11 specific withanolide compounds, such as withaphysalin, withasomniferol, and withafastuosin, through manual literature curation against 3CLpro. A molecular similarity analysis showed their similarity with compounds that have an established inhibitory activity against the SARS-CoV-2. In silico molecular docking and molecular dynamics simulations elucidated withasomniferol C (WS11) as a potential candidate against SARS-CoV-2 3CLpro. Additionally, the present work also presents a new method of validating docking poses using the AlteQ method.

An Integrated Taxonomic Approach Points towards a Single-Species Hypothesis for Santolina (Asteraceae) in Corsica and Sardinia.

Simple SummarySystematics is the branch of biology that studies the relationships among organisms and their evolution, while taxonomy is the science of classification. In this work, a systematic and taxonomic investigation about three plant species of Santolina, commonly known as lavender-cotton, is presented. Two of these species occur exclusively in Corsica and Sardinia, two of the main islands of the Mediterranean Sea, while a third one is a common ornamental plant, known only as cultivated. By integrating several approaches, we find out that the two putative species from Corsica and Sardinia are actually very similar from many points of view. A two-species hypothesis is no longer supported according to our results, so that these plants should be reclassified as a single species. This study demonstrates the importance of integrating different sources of information to produce reliable classifications (i.e. taxonomic hypotheses). In addition, our study is useful to better understand plant evolution in the context of the Mediterranean Basin, one of the world’s biodiversity hotspots.Santolina is a plant genus of dwarf aromatic shrubs that includes about 26 species native to the western Mediterranean Basin. In Corsica and Sardinia, two of the main islands of the Mediterranean, Santolina corsica (tetraploid) and S. insularis (hexaploid) are reported. Along with the cultivated pentaploid S. chamaecyparissus, these species form a group of taxa that is hard to distinguish only by morphology. Molecular (using ITS, trnH-psbA, trnL-trnF, trnQ-rps16, rps15-ycf1, psbM-trnD, and trnS-trnG), cypsela morpho-colorimetric, morphometric, and niche similarity analyses were conducted to investigate the diversity of plants belonging to this species group. Our results confute the current taxonomic hypothesis and suggest considering S. corsica and S. insularis as a single species. Moreover, molecular and morphometric results highlight the strong affinity between S. chamaecyparissus and the Santolina populations endemic to Corsica and Sardinia. Finally, the populations from south-western Sardinia, due to their high differentiation in the studied plastid markers and the different climatic niche with respect to all the other populations, could be considered as an evolutionary significant unit.

Neutral Loss Mass Spectral Data Enhances Molecular Similarity Analysis in METLIN.

Neutral loss (NL) spectral data presents a mirror of MS2 data and is a valuable yet largely untapped resource for molecular discovery and similarity analysis. Tandem mass spectrometry (MS2) data is effective for the identification of known molecules and the putative identification of novel, previously uncharacterized molecules (unknowns). Yet, MS2 data alone is limited in characterizing structurally related molecules. To facilitate unknown identification and complement the METLIN-MS2 fragment ion database for characterizing structurally related molecules, we have created a MS2 to NL converter as a part of the METLIN platform. The converter has been used to transform METLIN's MS2 data into a neutral loss database (METLIN-NL) on over 860 000 individual molecular standards. The platform includes both the MS2 to NL converter and a graphical user interface enabling comparative analyses between MS2 and NL data. Examples of NL spectral data are shown with oxylipin analogues and two structurally related statin molecules to demonstrate NL spectra and their ability to help characterize structural similarity. Mirroring MS2 data to generate NL spectral data offers a unique dimension for chemical and metabolite structure characterization.

Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods.

Bacterial infection is one of the most important factors affecting the human life span. Elderly people are more harmed by bacterial infections due to their deficits in immunity. Because of the lack of new antibiotics in recent years, bacterial resistance has increasingly become a serious problem globally. In this study, an antibacterial compound predictor was constructed using the support vector machines and random forest methods and the data of the active and inactive antibacterial compounds from the ChEMBL database. The results showed that both models have excellent prediction performance (mean accuracy >0.9 and mean AUC >0.9 for the two models). We used the predictor to screen potential antibacterial compounds from FDA-approved drugs in the DrugBank database. The screening results showed that 1087 small-molecule drugs have potential antibacterial activity and 154 of them are FDA-approved antibacterial drugs, which accounts for 76.2% of the approved antibacterial drugs collected in this study. Through molecular fingerprint similarity analysis and common substructure analysis, we screened 8 predicted antibacterial small-molecule compounds with novel structures compared with known antibacterial drugs, and 5 of them are widely used in the treatment of various tumors. This study provides a new insight for predicting antibacterial compounds by using approved drugs, the predicted compounds might be used to treat bacterial infections and extend lifespan.

Car-Following Dynamics, Characteristics, and Model Based on Interaction Potential Function

To model the car-following behavior more accurately, we carried out the molecular similarity analysis between the vehicles on the road and the inert gas system, comparing vehicles with microscopic particles in long and narrow pipes. The complex car-following interaction behavior is simplified into a dynamic process of the follower car that is constantly seeking to maintain the required safety distance from the leading vehicle. Through mathematical derivation of the Lennard–Jones potential function suitable for thermodynamic analysis of inert gas systems, the influence of each variable on the potential energy is clarified, and the existing problems of the existing molecular car-following model are analyzed, referring to the general Lennard–Jones potential function to build the vehicle interaction potential function. Considering the impact of the road wall potential generated by the lane boundary, a car-following model based on Lennard–Jones interaction potential is presented. The simulation test results show that compared with the existing molecular car-following model and IDM model, the average absolute error and root mean square error of the vehicle acceleration results obtained by this model and the actual data are lower, which proves that the vehicle is based on the Lennard–Jones interaction potential. The vehicle-following model based on Lennard–Jones interaction potential has a better fitting effect on the real vehicle-following behavior.

In silico Modeling of Curcumin Based Sulfonamides Inhibitors of the Human trans-Membrane Carbonic Anhydrase Isozyme, hCA IX by CoMSIA

Carbonic anhydrases, hCAs IX and XII are applied as the markers of progression of the disease in many oxygen deficient tumours and their specially manoeuvred inhibition is directly related to containing the growth of both primary tumours and tumour growth of secondary nature. Ligand-based quantitative structure-activity relationship (QSAR) studies were carried out on curcumin related, sulphonamide derivatives as inhibitors of human trans-membrane carbonic anhydrase isozyme, hCA IX by comparative molecular field similarity analysis (CoMSIA) implemented through the SYBYL package. The capacity of the model to predict coveted compound was evaluated using test set of three compounds. The best model created was found to be of choice as it showed a r2 value of 0.811 and a cross validated coefficient q2 value of 0.617 in tripos CoMSIA hydrophobic region. Results of the present study indicated that hydrophobic region factors play an important role in carbonic anhydrase hCA IX inhibition for compounds.

Building 2D classification models and 3D CoMSIA models on small-molecule inhibitors of both wild-type and T790M/L858R double-mutant EGFR.

Epidermal growth factor receptor (EGFR) has received widespread attention because it is an important target for anticancer drug design. Mutations in the EGFR, especially the T790M/L858R double mutation, have made cancer treatment more difficult. We herein built the structure-activity relationship models of small-molecule inhibitors on wild-type and T790M/L858R double-mutant EGFR with a whole dataset of 379 compounds. For 2D classification models, we used ECFP4 fingerprints to build support vector machine and random forest models and used SMILES to build self-attention recurrent neural network models. Each of all six models resulted in an accuracy of above 0.87 and the Matthews correlation coefficient value of above 0.76 on the test set, respectively. We concluded that inhibitors containing anilinoquinoline and methoxy or fluoro phenyl are highly active against wild EGFR. Substructures such as anilinopyrimidine, acrylamide, amino phenyl, methoxy phenyl, and thienopyrimidinyl amide appeared more in highly active inhibitors against double-mutant EGFR. We also used self-organizing map to cluster the inhibitors into six subsets based on ECFP4 fingerprints and analyzed the activity characteristics of different scaffolds in each subset. Among them, three datasets, which are based on pteridin, anilinopyrimidine, and anilinoquinoline scaffold, were selected to build 3D comparative molecular similarity analysis models individually. Models with the leave-one-out coefficient of determination (q2) above 0.65 were selected, and five descriptor types (steric, electrostatic, hydrophobic, donor, and acceptor) were used to study the effects of side chains of inhibitors on the activity against wild-type and mutant-type EGFR.