Molecular Informatics最新文献

{"title":"Development of novel ligands against SARS-CoV-2 M^pro enzyme: an in silico and in vitro Study.","authors":"Navid Kaboudi, Nadine Krüger, Maryam Hamzeh-Mivehroud","doi":"10.1002/minf.202300120","DOIUrl":"10.1002/minf.202300120","url":null,"abstract":"<p><strong>Background: </strong>Despite tremendous efforts made by scientific community during the outbreak of COVID-19 pandemic, this disease still remains as a public health concern. Although different types of vaccines were globally used to reduce the mortality, emergence of new variants of SARS-CoV-2 is a challenging issue in COVID-19 pharmacotherapy. In this context, target therapy of SARS-CoV-2 by small ligands is a promising strategy.</p><p><strong>Methods: </strong>In this investigation, we applied ligand-based virtual screening for finding novel molecules based on nirmatrelvir structure. Various criteria including drug-likeness, ADME, and toxicity properties were applied for filtering the compounds. The selected candidate molecules were subjected to molecular docking and dynamics simulation for predicting the binding mode and binding free energy, respectively. Then the molecules were experimentally evaluated in terms of antiviral activity against SARS-CoV-2 and toxicity assessment.</p><p><strong>Results: </strong>The results demonstrated that the identified compounds showed inhibitory activity towards SARS-CoV-2 M^pro .</p><p><strong>Conclusion: </strong>In summary, the introduced compounds may provide novel scaffold for further structural modification and optimization with improved anti SARS-CoV-2 M^pro activity.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300120"},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10166837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cell-penetrating peptides predictors: A comparative analysis of methods and datasets.","authors":"Karen Guerrero-Vázquez, Gabriel Del Rio, Carlos A Brizuela","doi":"10.1002/minf.202300104","DOIUrl":"10.1002/minf.202300104","url":null,"abstract":"<p><p>Cell-Penetrating Peptides (CPP) are emerging as an alternative to small-molecule drugs to expand the range of biomolecules that can be targeted for therapeutic purposes. Due to the importance of identifying and designing new CPP, a great variety of predictors have been developed to achieve these goals. To establish a ranking for these predictors, a couple of recent studies compared their performances on specific datasets, yet their conclusions cannot determine if the ranking obtained is due to the model, the set of descriptors or the datasets used to test the predictors. We present a systematic study of the influence of the peptide sequence's similarity of the datasets on the predictors' performance. The analysis reveals that the datasets used for training have a stronger influence on the predictors performance than the model or descriptors employed. We show that datasets with low sequence similarity between the positive and negative examples can be easily separated, and the tested classifiers showed good performance on them. On the other hand, a dataset with high sequence similarity between CPP and non-CPP will be a hard dataset, and it should be the one to be used for assessing the performance of new predictors.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300104"},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10225885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Absorption matters: A closer look at popular oral bioavailability rules for drug approvals.","authors":"Artur Caminero Gomes Soares, Gustavo Henrique Marques Sousa, Raisa Ludmila Calil, Gustavo Henrique Goulart Trossini","doi":"10.1002/minf.202300115","DOIUrl":"10.1002/minf.202300115","url":null,"abstract":"This study examines how two popular drug‐likeness concepts used in early development, Lipinski Rule of Five (Ro5) and Veber's Rules, possibly affected drug profiles of FDA approved drugs since 1997. Our findings suggest that when all criteria are applied, relevant compounds may be excluded, addressing the harmfulness of blindly employing these rules. Of all oral drugs in the period used for this analysis, around 66 % conform to the RO5 and 85 % to Veber's Rules. Molecular Weight and calculated LogP showed low consistent values over time, apart from being the two least followed rules, challenging their relevance. On the other hand, hydrogen bond related rules and the number of rotatable bonds are amongst the most followed criteria and show exceptional consistency over time. Furthermore, our analysis indicates that topological polar surface area and total count of hydrogen bonds cannot be used as interchangeable parameters, contrary to the original proposal. This research enhances the comprehension of drug profiles that were FDA approved in the post‐Lipinski period. Medicinal chemists could utilize these heuristics as a limited guide to direct their exploration of the oral bioavailability chemical space, but they must also steer the wheel to break these rules and explore different regions when necessary.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300115"},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10117941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven approaches for identifying hyperparameters in multi-step retrosynthesis.","authors":"Annie M Westerlund, Bente Barge, Lewis Mervin, Samuel Genheden","doi":"10.1002/minf.202300128","DOIUrl":"10.1002/minf.202300128","url":null,"abstract":"<p><p>The multi-step retrosynthesis problem can be solved by a search algorithm, such as Monte Carlo tree search (MCTS). The performance of multistep retrosynthesis, as measured by a trade-off in search time and route solvability, therefore depends on the hyperparameters of the search algorithm. In this paper, we demonstrated the effect of three MCTS hyperparameters (number of iterations, tree depth, and tree width) on metrics such as Linear integrated speed-accuracy score (LISAS) and Inverse efficiency score which consider both route solvability and search time. This exploration was conducted by employing three data-driven approaches, namely a systematic grid search, Bayesian optimization over an ensemble of molecules to obtain static MCTS hyperparameters, and a machine learning approach to dynamically predict optimal MCTS hyperparameters given an input target molecule. With the obtained results on the internal dataset, we demonstrated that it is possible to identify a hyperparameter set which outperforms the current AiZynthFinder default setting. It appeared optimal across a variety of target input molecules, both on proprietary and public datasets. The settings identified with the in-house dataset reached a solvability of 93 % and median search time of 151 s for the in-house dataset, and a 74 % solvability and 114 s for the ChEMBL dataset. These numbers can be compared to the current default settings which solved 85 % and 73 % during a median time of 110s and 84 s, for in-house and ChEMBL, respectively.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300128"},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10553301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phenothiazine-based virtual screening, molecular docking, and molecular dynamics of new trypanothione reductase inhibitors of Trypanosoma cruzi.","authors":"Gildardo Rivera,&nbsp;Alonzo González-González,&nbsp;Citlali Vázquez,&nbsp;Rusely Encalada,&nbsp;Emma Saavedra,&nbsp;Lenci K Vázquez-Jiménez,&nbsp;Eyra Ortiz-Pérez,&nbsp;Maria Bolognesi","doi":"10.1002/minf.202300069","DOIUrl":"10.1002/minf.202300069","url":null,"abstract":"<p><p>Phenothiazine derivatives can unselectively inhibit the trypanothione-dependent antioxidant system enzyme trypanothione reductase (TR). A virtual screening of 2163 phenothiazine derivatives from the ZINC15 and PubChem databases docked on the active site of T. cruzi TR showed that 285 compounds have higher affinity than the natural ligand trypanothione disulfide. 244 compounds showed higher affinity toward the parasite's enzyme than to its human homolog glutathione reductase. Protein-ligand interaction profiling predicted that the main interactions for the top scored compounds were with residues important for trypanothione disulfide binding: Phe396, Pro398, Leu399, His461, Glu466, and Glu467, particularly His461, which participates in catalysis. Two compounds with the desired profiles, ZINC1033681 (Zn_C687) and ZINC10213096 (Zn_C216), decreased parasite growth by 20 % and 50 %, respectively. They behaved as mixed-type inhibitors of recombinant TR, with Ki values of 59 and 47 μM, respectively. This study provides a further understanding of the potential of phenothiazine derivatives as TR inhibitors.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e2300069"},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10020855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conjugated quantitative structure-property relationship models: Prediction of kinetic characteristics linked by the Arrhenius equation.","authors":"Dmitry Zankov,&nbsp;Timur Madzhidov,&nbsp;Igor Baskin,&nbsp;Alexandre Varnek","doi":"10.1002/minf.202200275","DOIUrl":"10.1002/minf.202200275","url":null,"abstract":"<p><p>Conjugated QSPR models for reactions integrate fundamental chemical laws expressed by mathematical equations with machine learning algorithms. Herein we present a methodology for building conjugated QSPR models integrated with the Arrhenius equation. Conjugated QSPR models were used to predict kinetic characteristics of cycloaddition reactions related by the Arrhenius equation: rate constant <math> <semantics><mrow><mi>l</mi> <mi>o</mi> <mi>g</mi> <mi>k</mi></mrow> <annotation>${{rm l}{rm o}{rm g}k}$</annotation> </semantics> </math> , pre-exponential factor <math> <semantics><mrow><mi>l</mi> <mi>o</mi> <mi>g</mi> <mi>A</mi></mrow> <annotation>${{rm l}{rm o}{rm g}A}$</annotation> </semantics> </math> , and activation energy <math> <semantics><msub><mi>E</mi> <mi>a</mi></msub> <annotation>${{E}_{{rm a}}}$</annotation> </semantics> </math> . They were benchmarked against single-task (individual and equation-based models) and multi-task models. In individual models, all characteristics were modeled separately, while in multi-task models <math> <semantics><mrow><mi>l</mi> <mi>o</mi> <mi>g</mi> <mi>k</mi></mrow> <annotation>${{rm l}{rm o}{rm g}k}$</annotation> </semantics> </math> , <math> <semantics><mrow><mi>l</mi> <mi>o</mi> <mi>g</mi> <mi>A</mi></mrow> <annotation>${{rm l}{rm o}{rm g}A}$</annotation> </semantics> </math> and <math> <semantics><msub><mi>E</mi> <mi>a</mi></msub> <annotation>${{E}_{{rm a}}}$</annotation> </semantics> </math> were treated cooperatively. An equation-based model assessed <math> <semantics><mrow><mi>l</mi> <mi>o</mi> <mi>g</mi> <mi>k</mi></mrow> <annotation>${{rm l}{rm o}{rm g}k}$</annotation> </semantics> </math> using the Arrhenius equation and <math> <semantics><mrow><mi>l</mi> <mi>o</mi> <mi>g</mi> <mi>A</mi></mrow> <annotation>${{rm l}{rm o}{rm g}A}$</annotation> </semantics> </math> and <math> <semantics><msub><mi>E</mi> <mi>a</mi></msub> <annotation>${{E}_{{rm a}}}$</annotation> </semantics> </math> values predicted by individual models. It has been demonstrated that the conjugated QSPR models can accurately predict the reaction rate constants at extreme temperatures, at which reaction rate constants hardly can be measured experimentally. Also, in the case of small training sets conjugated models are more robust than related single-task approaches.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e2200275"},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10029968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-tier computational screening framework to effectively search the mutational space of SARS-CoV-2 receptor binding motif to identify mutants with enhanced ACE2 binding abilities.","authors":"Sandipan Chakraborty,&nbsp;Chiranjeet Saha","doi":"10.1002/minf.202300055","DOIUrl":"10.1002/minf.202300055","url":null,"abstract":"<p><p>SARS-CoV-2 gained crucial mutations at the receptor binding domain (RBD) that often changed the course of the pandemic leading to new waves with increased case fatality. Variants are observed with enhanced transmission and immune invasion abilities. Thus, predicting future variants with enhanced transmission ability is a problem of utmost research interest. Here, we have developed a multi-tier exhaustive SARS-CoV-2 mutation screening platform combining MM/GBSA, extensive molecular dynamics simulations, and steered molecular dynamics to identify RBD mutants with enhanced ACE2 binding capability. We have identified four RBM mutations (F490K, S494K, G504F, and the P499L) with significantly higher ACE2 binding abilities than wild-type RBD. Compared to wild-type RBD, they all form stable complexes with more hydrogen bonds and salt-bridge interactions with ACE2. Our simulation data suggest that these mutations allosterically alter the packing of the RBM interface of the RBD-ACE2 complex. As a result, the rupture force required to break the RBD-ACE2 contacts is significantly higher for these mutants.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e2300055"},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10491472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature importance-based interpretation of UMAP-visualized polymer space.","authors":"Takuya Ehiro","doi":"10.1002/minf.202300061","DOIUrl":"10.1002/minf.202300061","url":null,"abstract":"<p><p>Dimensionality reduction (DR) techniques are used for various purposes such as exploratory data analysis. A commonly employed linear DR technique is principal component analysis (PCA), which is one of the most popular methods for DR. Owing to its linear nature, PCA enables the determination of axes in a low-dimensional space and the calculation of corresponding loading vectors. However, PCA cannot necessarily extract important features of non-linearly distributed data. This study presents a technique aimed at aiding the interpretation of data reduced through non-linear DR methods. In the proposed method, non-linear dimensionally reduced data was clustered via a density-based clustering method. Thereafter, the obtained cluster labels were classified by random forest (RF) classifiers. Further, feature importance (FI) of RF classifiers and Spearman's rank correlation coefficients between predictive probabilities to obtained clusters and original feature values were utilized for characterizing the visualized dimensionally reduced data. The results revealed that the proposed method can provide the interpretable FI-based images of the handwritten digits dataset. Moreover, the proposed method was also applied to the polymer dataset. The study found that incorporating signed FI was advantageous in achieving a meaningful interpretation. Furthermore, Gaussian process regression was utilized to produce intuitive FI-based heatmaps on a 2-dimensional space for greater ease of understanding. Additionally, to enhance the interpretability of the obtained clusters, a feature selection technique called Boruta was applied. The Boruta feature selection method worked effectively to interpret the obtained clusters with limited and commonly important features. Additionally, the study suggested that computing FI solely from substructure-based descriptors could further enhance the interpretability of the results. Finally, the automation of the proposed method was investigated, and through maximizing the target score based on the quality of both the DR and clustering, indicative results were automatically obtained for both the handwritten digits and polymer datasets.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"42 8-9","pages":"e2300061"},"PeriodicalIF":3.6,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10257338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computer-aided design of muscarinic acetylcholine receptor M3 inhibitors: Promising compounds among trifluoromethyl containing hexahydropyrimidinones/thiones.","authors":"Alex Nyporko,&nbsp;Olga Tsymbalyuk,&nbsp;Ivan Voiteshenko,&nbsp;Sergiy Starosyla,&nbsp;Mykola Protopopov,&nbsp;Volodymyr Bdzhola","doi":"10.1002/minf.202300006","DOIUrl":"10.1002/minf.202300006","url":null,"abstract":"<p><p>The new high selective mAChRs M3 inhibitors with IC₅₀ in nanomolecular ranges, which can be the prototypes for effective COPD and asthma treatment drugs, were discovered with computational approaches among trifluoromethyl containing hexahydropyrimidinones/thiones. Compounds [6-(4-ethoxy-3-methoxy-phenyl)-4-hydroxy-2-thioxo-4-(trifluoromethyl)hexahydropyrimidin-5-yl]-phenyl-methanone (THPT-1) and 5-benzoyl-6-(3,4-dimethoxyphenyl)-4-hydroxy-4-(trifluoromethyl)hexahydropyrimidin-2-one (THPO-4) have been proved to be a highly effective (with IC₅₀ values of 1.62 ⋅ 10^-7  M and 3.09 ⋅ 10^-9  M, respectively) at the same concentrations significantly competitive inhibit the signal conduction through mAChR3 in comparison with ipratropium bromide, without significant effect on mAChR2, nicotinic cholinergic and adrenergic receptors.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"42 8-9","pages":"e2300006"},"PeriodicalIF":3.6,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10227176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Inhibitors of androgen receptor's DNA binding domain identified using an ultra-large virtual screening.","authors":"Mariia Radaeva,&nbsp;Helene Morin,&nbsp;Mohit Pandey,&nbsp;Fuqiang Ban,&nbsp;Maria Guo,&nbsp;Eric LeBlanc,&nbsp;Nada Lallous,&nbsp;Artem Cherkasov","doi":"10.1002/minf.202300026","DOIUrl":"10.1002/minf.202300026","url":null,"abstract":"<p><p>Androgen receptor (AR) inhibition remains the primary strategy to combat the progression of prostate cancer (PC). However, all clinically used AR inhibitors target the ligand-binding domain (LBD), which is highly susceptible to truncations through splicing or mutations that confer drug resistance. Thus, there exists an urgent need for AR inhibitors with novel modes of action. We thus launched a virtual screening of an ultra-large chemical library to find novel inhibitors of the AR DNA-binding domain (DBD) at two sites: protein-DNA interface (P-box) and dimerization site (D-box). The compounds selected through vigorous computational filtering were then experimentally validated. We identified several novel chemotypes that effectively suppress transcriptional activity of AR and its splice variant V7. The identified compounds represent previously unexplored chemical scaffolds with a mechanism of action that evades the conventional drug resistance manifested through LBD mutations. Additionally, we describe the binding features required to inhibit AR DBD at both P-box and D-box target sites.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"42 8-9","pages":"e2300026"},"PeriodicalIF":3.6,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10218099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}