Molecular Informatics最新文献

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Kinetic solubility: Experimental and machine-learning modeling perspectives. 动力学溶解度:实验和机器学习建模视角。
IF 3.6 4区 医学
Molecular Informatics Pub Date : 2024-02-01 Epub Date: 2024-01-23 DOI: 10.1002/minf.202300216
Shamkhal Baybekov, Pierre Llompart, Gilles Marcou, Patrick Gizzi, Jean-Luc Galzi, Pascal Ramos, Olivier Saurel, Claire Bourban, Claire Minoletti, Alexandre Varnek
{"title":"Kinetic solubility: Experimental and machine-learning modeling perspectives.","authors":"Shamkhal Baybekov, Pierre Llompart, Gilles Marcou, Patrick Gizzi, Jean-Luc Galzi, Pascal Ramos, Olivier Saurel, Claire Bourban, Claire Minoletti, Alexandre Varnek","doi":"10.1002/minf.202300216","DOIUrl":"10.1002/minf.202300216","url":null,"abstract":"<p><p>Kinetic aqueous or buffer solubility is important parameter measuring suitability of compounds for high throughput assays in early drug discovery while thermodynamic solubility is reserved for later stages of drug discovery and development. Kinetic solubility is also considered to have low inter-laboratory reproducibility because of its sensitivity to protocol parameters [1]. Presumably, this is why little efforts have been put to build QSPR models for kinetic in comparison to thermodynamic aqueous solubility. Here, we investigate the reproducibility and modelability of kinetic solubility assays. We first analyzed the relationship between kinetic and thermodynamic solubility data, and then examined the consistency of data from different kinetic assays. In this contribution, we report differences between kinetic and thermodynamic solubility data that are consistent with those reported by others [1, 2] and good agreement between data from different kinetic solubility campaigns in contrast to general expectations. The latter is confirmed by achieving high performing QSPR models trained on merged kinetic solubility datasets. The poor performance of QSPR model trained on thermodynamic solubility when applied to kinetic solubility dataset reinforces the conclusion that kinetic and thermodynamic solubilities do not correlate: one cannot be used as an ersatz for the other. This encourages for building predictive models for kinetic solubility. The kinetic solubility QSPR model developed in this study is freely accessible through the Predictor web service of the Laboratory of Chemoinformatics (https://chematlas.chimie.unistra.fr/cgi-bin/predictor2.cgi).</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300216"},"PeriodicalIF":3.6,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139040261","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}
引用次数: 0
Integrated workflow for the identification of new GABAA R positive allosteric modulators based on the in silico screening with further in vitro validation. Case study using Enamine's stock chemical space. 基于计算机筛选和进一步体外验证的新型GABA阳性变构调节剂鉴定集成工作流程。使用Enamine的库存化学空间进行案例研究。
IF 3.6 4区 医学
Molecular Informatics Pub Date : 2024-02-01 Epub Date: 2024-01-24 DOI: 10.1002/minf.202300156
Maksym Platonov, Oleksandr Maximyuk, Alexey Rayevsky, Olena Iegorova, Vasyl Hurmach, Yuliia Holota, Elijah Bulgakov, Andrii Cherninskyi, Pavel Karpov, Sergey Ryabukhin, Oleg Krishtal, Dmitriy Volochnyuk
{"title":"Integrated workflow for the identification of new GABA<sub>A</sub> R positive allosteric modulators based on the in silico screening with further in vitro validation. Case study using Enamine's stock chemical space.","authors":"Maksym Platonov, Oleksandr Maximyuk, Alexey Rayevsky, Olena Iegorova, Vasyl Hurmach, Yuliia Holota, Elijah Bulgakov, Andrii Cherninskyi, Pavel Karpov, Sergey Ryabukhin, Oleg Krishtal, Dmitriy Volochnyuk","doi":"10.1002/minf.202300156","DOIUrl":"10.1002/minf.202300156","url":null,"abstract":"<p><p>Numerous studies reported an association between GABA<sub>A</sub> R subunit genes and epilepsy, eating disorders, autism spectrum disorders, neurodevelopmental disorders, and bipolar disorders. This study was aimed to find some potential positive allosteric modulators and was performed by combining the in silico approach with further in vitro evaluation of its real activity. We started from the GABA<sub>A</sub> R-diazepam complexes and assembled a lipid embedded protein ensemble to refine it via molecular dynamics (MD) simulation. Then we focused on the interaction of α1β2γ2 with some Z-drugs (non-benzodiazepine compounds) using an Induced Fit Docking (IFD) into the relaxed binding site to generate a pharmacophore model. The pharmacophore model was validated with a reference set and applied to decrease the pre-filtered Enamine database before the main docking procedure. Finally, we succeeded in identifying a set of compounds, which met all features of the docking model. The aqueous solubility and stability of these compounds in mouse plasma were assessed. Then they were tested for the biological activity using the rat Purkinje neurons and CHO cells with heterologously expressed human α1β2γ2 GABA<sub>A</sub> receptors. Whole-cell patch clamp recordings were used to reveal the GABA induced currents. Our study represents a convenient and tunable model for the discovery of novel positive allosteric modulators of GABA<sub>A</sub> receptors. A High-throughput virtual screening of the largest available database of chemical compounds resulted in the selection of 23 compounds. Further electrophysiological tests allowed us to determine a set of 3 the most outstanding active compounds. Considering the structural features of leader compounds, the study can develop into the MedChem project soon.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300156"},"PeriodicalIF":3.6,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"107591770","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}
引用次数: 0
Chemical language models for molecular design. 分子设计的化学语言模型。
IF 3.6 4区 医学
Molecular Informatics Pub Date : 2024-01-01 Epub Date: 2023-12-12 DOI: 10.1002/minf.202300288
Jürgen Bajorath
{"title":"Chemical language models for molecular design.","authors":"Jürgen Bajorath","doi":"10.1002/minf.202300288","DOIUrl":"10.1002/minf.202300288","url":null,"abstract":"<p><p>In drug discovery, chemical language models (CLMs) originating from natural language processing offer new opportunities for molecular design. CLMs have been developed using recurrent neural network (RNN) or transformer architectures. For the predictive performance of RNN-based encoder-decoder frameworks and transformers, attention mechanisms play a central role. Among others, emerging application areas for CLMs include constrained generative modeling and the prediction of chemical reactions or drug-target interactions. Since CLMs are applicable to any compound or target data that can be presented in a sequential format and tokenized, mappings of different types of sequences can be learned. For example, active compounds can be predicted from protein sequence motifs. Novel off-the-beat-path applications can also be considered. For example, analogue series from medicinal chemistry can be perceived and represented as chemical sequences and extended with new compounds using CLMs. Herein, methodological features of CLMs and different applications are discussed.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300288"},"PeriodicalIF":3.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138445490","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}
引用次数: 0
A community effort in SARS-CoV-2 drug discovery. 严重急性呼吸系统综合征冠状病毒2型药物发现的社区努力。
IF 2.8 4区 医学
Molecular Informatics Pub Date : 2024-01-01 Epub Date: 2023-11-14 DOI: 10.1002/minf.202300262
Johannes Schimunek, Philipp Seidl, Katarina Elez, Tim Hempel, Tuan Le, Frank Noé, Simon Olsson, Lluís Raich, Robin Winter, Hatice Gokcan, Filipp Gusev, Evgeny M Gutkin, Olexandr Isayev, Maria G Kurnikova, Chamali H Narangoda, Roman Zubatyuk, Ivan P Bosko, Konstantin V Furs, Anna D Karpenko, Yury V Kornoushenko, Mikita Shuldau, Artsemi Yushkevich, Mohammed B Benabderrahmane, Patrick Bousquet-Melou, Ronan Bureau, Beatrice Charton, Bertrand C Cirou, Gérard Gil, William J Allen, Suman Sirimulla, Stanley Watowich, Nick Antonopoulos, Nikolaos Epitropakis, Agamemnon Krasoulis, Vassilis Itsikalis, Stavros Theodorakis, Igor Kozlovskii, Anton Maliutin, Alexander Medvedev, Petr Popov, Mark Zaretckii, Hamid Eghbal-Zadeh, Christina Halmich, Sepp Hochreiter, Andreas Mayr, Peter Ruch, Michael Widrich, Francois Berenger, Ashutosh Kumar, Yoshihiro Yamanishi, Kam Y J Zhang, Emmanuel Bengio, Yoshua Bengio, Moksh J Jain, Maksym Korablyov, Cheng-Hao Liu, Gilles Marcou, Enrico Glaab, Kelly Barnsley, Suhasini M Iyengar, Mary Jo Ondrechen, V Joachim Haupt, Florian Kaiser, Michael Schroeder, Luisa Pugliese, Simone Albani, Christina Athanasiou, Andrea Beccari, Paolo Carloni, Giulia D'Arrigo, Eleonora Gianquinto, Jonas Goßen, Anton Hanke, Benjamin P Joseph, Daria B Kokh, Sandra Kovachka, Candida Manelfi, Goutam Mukherjee, Abraham Muñiz-Chicharro, Francesco Musiani, Ariane Nunes-Alves, Giulia Paiardi, Giulia Rossetti, S Kashif Sadiq, Francesca Spyrakis, Carmine Talarico, Alexandros Tsengenes, Rebecca C Wade, Conner Copeland, Jeremiah Gaiser, Daniel R Olson, Amitava Roy, Vishwesh Venkatraman, Travis J Wheeler, Haribabu Arthanari, Klara Blaschitz, Marco Cespugli, Vedat Durmaz, Konstantin Fackeldey, Patrick D Fischer, Christoph Gorgulla, Christian Gruber, Karl Gruber, Michael Hetmann, Jamie E Kinney, Krishna M Padmanabha Das, Shreya Pandita, Amit Singh, Georg Steinkellner, Guilhem Tesseyre, Gerhard Wagner, Zi-Fu Wang, Ryan J Yust, Dmitry S Druzhilovskiy, Dmitry A Filimonov, Pavel V Pogodin, Vladimir Poroikov, Anastassia V Rudik, Leonid A Stolbov, Alexander V Veselovsky, Maria De Rosa, Giada De Simone, Maria R Gulotta, Jessica Lombino, Nedra Mekni, Ugo Perricone, Arturo Casini, Amanda Embree, D Benjamin Gordon, David Lei, Katelin Pratt, Christopher A Voigt, Kuang-Yu Chen, Yves Jacob, Tim Krischuns, Pierre Lafaye, Agnès Zettor, M Luis Rodríguez, Kris M White, Daren Fearon, Frank Von Delft, Martin A Walsh, Dragos Horvath, Charles L Brooks, Babak Falsafi, Bryan Ford, Adolfo García-Sastre, Sang Yup Lee, Nadia Naffakh, Alexandre Varnek, Günter Klambauer, Thomas M Hermans
{"title":"A community effort in SARS-CoV-2 drug discovery.","authors":"Johannes Schimunek, Philipp Seidl, Katarina Elez, Tim Hempel, Tuan Le, Frank Noé, Simon Olsson, Lluís Raich, Robin Winter, Hatice Gokcan, Filipp Gusev, Evgeny M Gutkin, Olexandr Isayev, Maria G Kurnikova, Chamali H Narangoda, Roman Zubatyuk, Ivan P Bosko, Konstantin V Furs, Anna D Karpenko, Yury V Kornoushenko, Mikita Shuldau, Artsemi Yushkevich, Mohammed B Benabderrahmane, Patrick Bousquet-Melou, Ronan Bureau, Beatrice Charton, Bertrand C Cirou, Gérard Gil, William J Allen, Suman Sirimulla, Stanley Watowich, Nick Antonopoulos, Nikolaos Epitropakis, Agamemnon Krasoulis, Vassilis Itsikalis, Stavros Theodorakis, Igor Kozlovskii, Anton Maliutin, Alexander Medvedev, Petr Popov, Mark Zaretckii, Hamid Eghbal-Zadeh, Christina Halmich, Sepp Hochreiter, Andreas Mayr, Peter Ruch, Michael Widrich, Francois Berenger, Ashutosh Kumar, Yoshihiro Yamanishi, Kam Y J Zhang, Emmanuel Bengio, Yoshua Bengio, Moksh J Jain, Maksym Korablyov, Cheng-Hao Liu, Gilles Marcou, Enrico Glaab, Kelly Barnsley, Suhasini M Iyengar, Mary Jo Ondrechen, V Joachim Haupt, Florian Kaiser, Michael Schroeder, Luisa Pugliese, Simone Albani, Christina Athanasiou, Andrea Beccari, Paolo Carloni, Giulia D'Arrigo, Eleonora Gianquinto, Jonas Goßen, Anton Hanke, Benjamin P Joseph, Daria B Kokh, Sandra Kovachka, Candida Manelfi, Goutam Mukherjee, Abraham Muñiz-Chicharro, Francesco Musiani, Ariane Nunes-Alves, Giulia Paiardi, Giulia Rossetti, S Kashif Sadiq, Francesca Spyrakis, Carmine Talarico, Alexandros Tsengenes, Rebecca C Wade, Conner Copeland, Jeremiah Gaiser, Daniel R Olson, Amitava Roy, Vishwesh Venkatraman, Travis J Wheeler, Haribabu Arthanari, Klara Blaschitz, Marco Cespugli, Vedat Durmaz, Konstantin Fackeldey, Patrick D Fischer, Christoph Gorgulla, Christian Gruber, Karl Gruber, Michael Hetmann, Jamie E Kinney, Krishna M Padmanabha Das, Shreya Pandita, Amit Singh, Georg Steinkellner, Guilhem Tesseyre, Gerhard Wagner, Zi-Fu Wang, Ryan J Yust, Dmitry S Druzhilovskiy, Dmitry A Filimonov, Pavel V Pogodin, Vladimir Poroikov, Anastassia V Rudik, Leonid A Stolbov, Alexander V Veselovsky, Maria De Rosa, Giada De Simone, Maria R Gulotta, Jessica Lombino, Nedra Mekni, Ugo Perricone, Arturo Casini, Amanda Embree, D Benjamin Gordon, David Lei, Katelin Pratt, Christopher A Voigt, Kuang-Yu Chen, Yves Jacob, Tim Krischuns, Pierre Lafaye, Agnès Zettor, M Luis Rodríguez, Kris M White, Daren Fearon, Frank Von Delft, Martin A Walsh, Dragos Horvath, Charles L Brooks, Babak Falsafi, Bryan Ford, Adolfo García-Sastre, Sang Yup Lee, Nadia Naffakh, Alexandre Varnek, Günter Klambauer, Thomas M Hermans","doi":"10.1002/minf.202300262","DOIUrl":"10.1002/minf.202300262","url":null,"abstract":"<p><p>The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the \"Billion molecules against COVID-19 challenge\", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300262"},"PeriodicalIF":2.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11299051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41205605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
CIPSI: An open chemical intellectual property service for medicinal chemists. CIPSI:为药物化学家提供开放的化学知识产权服务。
IF 3.6 4区 医学
Molecular Informatics Pub Date : 2024-01-01 Epub Date: 2023-12-12 DOI: 10.1002/minf.202300221
Maria Martinez-Sevillano, Maria J Falaguera, Jordi Mestres
{"title":"CIPSI: An open chemical intellectual property service for medicinal chemists.","authors":"Maria Martinez-Sevillano, Maria J Falaguera, Jordi Mestres","doi":"10.1002/minf.202300221","DOIUrl":"10.1002/minf.202300221","url":null,"abstract":"<p><p>The availability of patent chemical data offers public access to a chemical space that is not well covered by other sources collecting small molecules from scholarly literature. However, open applications to facilitate the search and analysis of biologically-relevant molecular structures present in patents are still largely missing. We have developed CIPSI, an open Chemical Intellectual Property Service @ IMIM to assist medicinal chemists in searching and analysing molecules in SureChEMBL patents. The current version contains 6,240,500 molecules from 236,689 pharmacological patents, of which 5,949,214 are confidently assigned to core chemical structures reminiscent of the Markush structure in the patent claim. The platform includes some graphical tools to facilitate comparative patent analyses between drugs, chemical substructures, and company assignees. CIPSI is available at https://cipsi.org.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300221"},"PeriodicalIF":3.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138445491","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}
引用次数: 0
GUIDEMOL: A Python graphical user interface for molecular descriptors based on RDKit. GUIDEMOL:基于RDKit的分子描述符的Python图形用户界面。
IF 3.6 4区 医学
Molecular Informatics Pub Date : 2024-01-01 Epub Date: 2023-11-20 DOI: 10.1002/minf.202300190
Joao Aires-de-Sousa
{"title":"GUIDEMOL: A Python graphical user interface for molecular descriptors based on RDKit.","authors":"Joao Aires-de-Sousa","doi":"10.1002/minf.202300190","DOIUrl":"10.1002/minf.202300190","url":null,"abstract":"<p><p>GUIDEMOL is a Python computer program based on the RDKit software to process molecular structures and calculate molecular descriptors with a graphical user interface using the tkinter package. It can calculate descriptors already implemented in RDKit as well as grid representations of 3D molecular structures using the electrostatic potential or voxels. The GUIDEMOL app provides easy access to RDKit tools for chemoinformatics users with no programming skills and can be adapted to calculate other descriptors or to trigger other procedures. A command line interface (CLI) is also provided for the calculation of grid representations. The source code is available at https://github.com/jairesdesousa/guidemol.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300190"},"PeriodicalIF":3.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54230132","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}
引用次数: 0
HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries. 使用扩展生成建模(HIDDEN-GEM)丰富的对接发现命中率:一种用于加速超大型化学库虚拟筛选的新计算工作流。
IF 3.6 4区 医学
Molecular Informatics Pub Date : 2024-01-01 Epub Date: 2023-12-19 DOI: 10.1002/minf.202300207
Konstantin I Popov, James Wellnitz, Travis Maxfield, Alexander Tropsha
{"title":"HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries.","authors":"Konstantin I Popov, James Wellnitz, Travis Maxfield, Alexander Tropsha","doi":"10.1002/minf.202300207","DOIUrl":"10.1002/minf.202300207","url":null,"abstract":"<p><p>Recent rapid expansion of make-on-demand, purchasable, chemical libraries comprising dozens of billions or even trillions of molecules has challenged the efficient application of traditional structure-based virtual screening methods that rely on molecular docking. We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow. For each target, HIDDEN GEM nominates a small number of top-scoring virtual hits prioritized from ultra-large chemical libraries. We have benchmarked HIDDEN GEM by conducting virtual screening campaigns for 16 diverse protein targets using Enamine REAL Space library comprising 37 billion molecules. We show that HIDDEN GEM yields the highest enrichment factors as compared to state of the art accelerated virtual screening methods, while requiring the least computational resources. HIDDEN GEM can be executed with any docking software and employed by users with limited computational resources.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300207"},"PeriodicalIF":3.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11156482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41139125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Targeting of essential mycobacterial replication enzyme DnaG primase revealed Mitoxantrone and Vapreotide as novel mycobacterial growth inhibitors. 以分枝杆菌的基本复制酶 DnaG primase 为靶标,发现米托蒽醌和伐普瑞泰是新型的分枝杆菌生长抑制剂。
IF 3.6 4区 医学
Molecular Informatics Pub Date : 2023-12-20 DOI: 10.1002/minf.202300284
Sonam Grover, Waseem Ali, Salma Jamal, Rishabh Gangwar, Faraz Ahmed, Rahul Sharma, Meetu Agarwal, Javaid Ahmad Sheikh, Abhinav Grover
{"title":"Targeting of essential mycobacterial replication enzyme DnaG primase revealed Mitoxantrone and Vapreotide as novel mycobacterial growth inhibitors.","authors":"Sonam Grover, Waseem Ali, Salma Jamal, Rishabh Gangwar, Faraz Ahmed, Rahul Sharma, Meetu Agarwal, Javaid Ahmad Sheikh, Abhinav Grover","doi":"10.1002/minf.202300284","DOIUrl":"https://doi.org/10.1002/minf.202300284","url":null,"abstract":"Tuberculosis (TB) is the second leading cause of mortality after COVID-19, with a global death toll of 1.6 million in 2021. The escalating situation of drug-resistant forms of TB has threatened the current TB management strategies. New therapeutics with novel mechanisms of action are urgently required to address the current global TB crisis. The essential mycobacterial primase DnaG with no structural homology to homo sapiens presents itself as a good candidate for drug targeting. In the present study, Mitoxantrone and Vapreotide, two FDA-approved drugs, were identified as potential anti-mycobacterial agents. Both Mitoxantrone and Vapreotide exhibit a strong Minimum Inhibitory Concentration (MIC) of ≤25µg/ml against both the virulent (M.tb-H37Rv) and avirulent (M.tb-H37Ra) strains of M.tb. Extending the validations further revealed the inhibitory potential drugs in ex-vivo conditions. Leveraging the computational high-throughput multi-level docking procedures from the pool of ~2700 FDA-approved compounds, Mitoxantrone and Vapreotide were screened out as potential inhibitors of DnaG. Extensive 200ns long all-atoms molecular dynamic simulation of DnaGDrugs complexes revealed that both drugs bind strongly and stabilize the DnaG during simulations. Reduced solvent exposure and confined motions of the active centre of DnaG upon complexation with drugs indicated that both drugs led to the closure of the active site of DnaG. From this study's findings, we propose Mitoxantrone and Vapreotide as potential anti-mycobacterial agents, with their novel mechanism of action against mycobacterial DnaG.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"55 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138825802","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}
引用次数: 0
Similarity searching for anticandidal agents employing a repurposing approach 采用再利用方法进行抗念珠菌药剂的相似性搜索
IF 3.6 4区 医学
Molecular Informatics Pub Date : 2023-12-14 DOI: 10.1002/minf.202300206
Jaime Pérez-Villanueva, Karen Rodríguez-Villar, Francisco Cortés-Benítez, Juan Francisco Palacios-Espinosa
{"title":"Similarity searching for anticandidal agents employing a repurposing approach","authors":"Jaime Pérez-Villanueva, Karen Rodríguez-Villar, Francisco Cortés-Benítez, Juan Francisco Palacios-Espinosa","doi":"10.1002/minf.202300206","DOIUrl":"https://doi.org/10.1002/minf.202300206","url":null,"abstract":"Fungal infections caused by &lt;i&gt;Candida&lt;/i&gt; are still a public health concern. Particularly, the resistance to traditional chemotherapeutic agents is a major issue that requires efforts to develop new therapies. One of the most interesting approaches to finding new active compounds is drug repurposing aided by computational methods. In this work, two databases containing anticandidal agents and drugs were studied employing cheminformatics and compared by similarity methods. The results showed 36 drugs with high similarities to some candicidals. From these drugs, trimetozin, osalmid and metochalcone were evaluated against &lt;i&gt;C. albicans&lt;/i&gt; (18804), &lt;i&gt;C. glabrata&lt;/i&gt; (90030), and miconazole-resistant strain &lt;i&gt;C. glabrata&lt;/i&gt; (32554). Osalmid and metochalcone were the best, with activity in the micromolar range. These findings represent an opportunity to continue with the research on the potential antifungal application of osalmid and metochalcone as well as the design of structurally related derivatives.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"5 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138691835","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}
引用次数: 0
Use of tree-based machine learning methods to screen affinitive peptides based on docking data. 使用基于树的机器学习方法筛选基于对接数据的亲和肽。
IF 3.6 4区 医学
Molecular Informatics Pub Date : 2023-12-01 Epub Date: 2023-11-09 DOI: 10.1002/minf.202300143
Hua Feng, Fangyu Wang, Ning Li, Qian Xu, Guanming Zheng, Xuefeng Sun, Man Hu, Xuewu Li, Guangxu Xing, Gaiping Zhang
{"title":"Use of tree-based machine learning methods to screen affinitive peptides based on docking data.","authors":"Hua Feng, Fangyu Wang, Ning Li, Qian Xu, Guanming Zheng, Xuefeng Sun, Man Hu, Xuewu Li, Guangxu Xing, Gaiping Zhang","doi":"10.1002/minf.202300143","DOIUrl":"10.1002/minf.202300143","url":null,"abstract":"<p><p>Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four different tree-based algorithms, including Classification and regression trees (CART), C5.0 decision tree (C50), Bagged CART (BAG) and Random Forest (RF), were employed to explore the relationship between experimental peptide affinities and virtual docking data, and the performance of each model was also compared in parallel. All four algorithms showed better performances on dataset pre-scaled, -centered and -PCA than other pre-processed dataset. After model re-built and hyperparameter optimization, the optimal C50 model (C50O) showed the best performances in terms of Accuracy, Kappa, Sensitivity, Specificity, F1, MCC and AUC when validated on test data and an unknown PEDV datasets evaluation (Accuracy=80.4 %). BAG and RFO (the optimal RF), as two best models during training process, did not performed as expecting during in testing and unknown dataset validations. Furthermore, the high correlation of the predictions of RFO and BAG to C50O implied the high stability and robustness of their prediction. Whereas although the good performance on unknown dataset, the poor performance in test data validation and correlation analysis indicated CARTO could not be used for future data prediction. To accurately evaluate the peptide affinity, the current study firstly gave a tree-model competition on affinitive peptide prediction by using virtual docking data, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300143"},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10212086","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}
引用次数: 0
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