Use the journals feature with a free QxMD account.
Use Read by QxMD to access full text via your institution or open access sources.
Read also provides personalized recommendations to keep you up to date in your field.
Existing User
Sign InNew to Read
Sign Up#21
JOURNAL ARTICLE
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 Benabderrahmane, Patrick Bousquet-Melou, Ronan Bureau, Beatrice Charton, Bertrand Cirou, Gérard Gil, William J Allen, Suman Sirimulla, Stanley Watowich, Nick Antonopoulos, Nikolaos Epitropakis, Agamemnon Krasoulis, Vassilis Pitsikalis, 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 Zhang, Emmanuel Bengio, Yoshua Bengio, Moksh Jain, Maksym Korablyov, Cheng-Hao Liu, Marcous Gilles, 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 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 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, Guenter Klambauer, Thomas M Hermans
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...
October 13, 2023: Molecular Informatics
#22
JOURNAL ARTICLE
Alexander Tropsha, Konstantin I Popov, James Wellnitz, Travis Maxfield
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 ( HI t Discovery using D ocking EN riched by GE nerative M odeling) 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...
October 6, 2023: Molecular Informatics
#23
JOURNAL ARTICLE
Bailing Xu, Xiaoyu Wang, Xinyuan Zhang, Jie Zhou, Weiping Wang, Xiaoliang Wang
in silico Kv2.1 is widely expressed in brain, and inhibiting Kv2.1 is a potential strategy to prevent cell death and achieve neuroprotection in ischemic stroke. Herein, an model of Kv2.1 tetramer structure was constructed by employing the AlphaFold-Multimer deep learning method to facilitate the rational discovery of Kv2.1 inhibitors. GaMD was utilized to create an ion transporting trajectory, which was analyzed with HMM to generate multiple representative receptor conformations. The binding site of RY785 and RY796(S) und- er the P-loop was defined with Fpocket program together with the competitive binding electrophysiology assay...
October 4, 2023: Molecular Informatics
#24
JOURNAL ARTICLE
Shamsudin Nasaev, Artem R Mukanov, Ivan I Kuznetsov, Alexander V Veselovsky
Nowadays there are numerous discovered natural RNA variations participating in different cellular processes and artificial RNA, e.g., aptamers, riboswitches. One of the required tasks in the investigation of their functions and mechanism of influence on cells and interaction with targets is the prediction of RNA secondary structures. The classic thermodynamic-based prediction algorithms do not consider the specificity of biological folding and deep learning methods that were designed to resolve this issue suffer from homology-based methods problems...
September 14, 2023: Molecular Informatics
#25
JOURNAL ARTICLE
Hua Feng, Fangyu Wang, Ning Li, Qian Xu, Guanming Zheng, Xuefeng Sun, Man Hu, Xuewu Li, Guangxu Xing, Gaiping Zhang
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...
September 11, 2023: Molecular Informatics
#26
JOURNAL ARTICLE
Annie M Westerlund, Bente Barge, Lewis Mervin, Samuel Genheden
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...
September 7, 2023: Molecular Informatics
#27
JOURNAL ARTICLE
Karen Guerrero-Vázquez, Gabriel Del Rio, Carlos A Brizuela
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...
September 6, 2023: Molecular Informatics
#28
JOURNAL ARTICLE
Sandipan Chakraborty, Chiranjeet Saha
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...
August 17, 2023: Molecular Informatics
#29
JOURNAL ARTICLE
Maryam Hamzeh-Mivehroud, Navid Kaboudi, Nadine Krüger
Background: Methods: 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. In this investigation, we applied ligand-based virtual screening for finding novel molecules based on nirmatrelvir structure...
August 17, 2023: Molecular Informatics
#30
JOURNAL ARTICLE
Gustavo Trossini, Artur Caminero Gomes Soares, Gustavo Henrique Marques Sousa, Raisa Ludmilla Calil
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...
August 7, 2023: Molecular Informatics
#31
JOURNAL ARTICLE
Dimitra-Danai Varsou, Haralambos Sarimveis
In this study we present deimos, a computational methodology for optimal grouping, applied on the read-across prediction of engineered nanomaterials' (ENMs) toxicity-related properties. The method is based on the formulation and the solution of a mixed-integer optimization program (MILP) problem that automatically and simultaneously performs feature selection, defines the grouping boundaries according to the response variable and develops linear regression models in each group. For each group/region, the characteristic centroid is defined in order to allocate untested ENMs to the groups...
August 2023: Molecular Informatics
#32
JOURNAL ARTICLE
Gildardo Rivera, Alonzo González-González, Citlali Vázquez, Rusely Encalada, Emma Saavedra, Lenci K Vázquez-Jiménez, Eyra Ortiz-Pérez, Maria Bolognesi
T. cruzi 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 TR showed that 285 compounds have higher affinity than the natural ligand trypanothione disulfide. Of these compounds, 244 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...
July 25, 2023: Molecular Informatics
#33
JOURNAL ARTICLE
Alexandre Varnek, D Zankov, Timur I Madzhidov, I Baskin
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 log k , pre-exponential factor log A , and activation energy E a . They were benchmarked against single-task (individual and equation-based models) and multi-task models...
July 24, 2023: Molecular Informatics
#34
JOURNAL ARTICLE
Chikashige Yamanaka, Shunya Uki, Kazuma Kaitoh, Michio Iwata, Yoshihiro Yamanishi
Computational de novo drug design is a challenging issue in medicine, and it is desirable to consider all of the relevant information of the biological systems in a disease state. Here, we propose a novel computational method to generate drug candidate molecular structures from patient gene expression profiles via deep learning, which we call DRAGONET. Our model can generate new molecules that are likely to counteract disease-specific gene expression patterns in patients, which is made possible by exploring the latent space constructed by a transformer-based variational autoencoder and integrating the substructures of disease-correlated molecules...
July 20, 2023: Molecular Informatics
#35
JOURNAL ARTICLE
Alex Nyporko, Olga Tsymbalyuk, Ivan Voiteshenko, Sergiy Starosyla, Mykola Protopopov, Volodymyr Bdzhola
The new high selective mAChRs M3 inhibitors with IC 50 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 IC 50 values of 1...
June 9, 2023: Molecular Informatics
#36
JOURNAL ARTICLE
Mutasem Taha, Amenah M Al-Imam, Safa Daoud, Ma'mon M Hatmal
Dual specificity protein kinase threonine/Tyrosine kinase (TTK) is one of the mitotic kinases. High levels of TTK are detected in several types of cancer. Hence, TTK inhibition is considered a promising therapeutic anti-cancer strategy. In this work, we used multiple docked poses of TTK inhibitors to augment training data for machine learning QSAR modeling. Ligand-Receptor Contacts Fingerprints and docking scoring values were used as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to determine critical descriptors for predicting anti-TTK bioactivity and for pharmacophore generation...
May 24, 2023: Molecular Informatics
#37
JOURNAL ARTICLE
Takuya Ehiro
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...
May 22, 2023: Molecular Informatics
#38
JOURNAL ARTICLE
Ramon Miranda-Quintana, Timothy B Dunn, Edgar López López, Taewon Kim, Jose L Medina-Franco
Understanding structure-activity landscapes is essential in drug discovery. Similarly, it has been shown that the presence of activity cliffs in compound data sets can have a substantial impact not only on the design progress but also can influence the predictive ability of machine learning models. With the continued expansion of the chemical space and the currently available large and ultra-large libraries, it is imperative to implement efficient tools to analyze the activity landscape of compound data sets rapidly...
May 18, 2023: Molecular Informatics
#39
JOURNAL ARTICLE
Yun Tang, Yaxin Gu, Yimeng Wang, Zengrui Wu, Weihua Li, Guixia Liu
Drug-induced liver injury (DILI) is one of the major causes of drug withdrawals, acute liver injury and blackbox warnings. Clinical diagnosis of DILI is a huge challenge due to the complex pathogenesis and lack of specific biomarkers. In recent years, machine learning methods have been used for DILI risk assessment, but the model generalization does not perform satisfactorily. In this study, we constructed a large DILI data set and proposed an integration strategy based on hybrid representations for DILI prediction (HR-DILI)...
May 17, 2023: Molecular Informatics
#40
JOURNAL ARTICLE
Aleksander Mendyk, Alicja Gawalska, Natalia Czub, Michał Sapa, Marcin Kołaczkowski, Adam Bucki
The aim of the study was to develop AutoML models to search for novel MTDL chemotypes blocking PDE4B, PDE8A, and TRPA1. Asthma and COPD are characterized by complex pathophysiology associated with chronic inflammation, bronchoconstriction, and bronchial hyperresponsiveness resulting in airway remodeling. A possible comprehensive solution that could fully counteract the pathological processes of both diseases are rationally designed multi-target-directed ligands (MTDLs), combining PDE4B and PDE8A inhibition with TRPA1 blockade...
May 16, 2023: Molecular Informatics
Make the most of Read.
Download the mobile app.
Download the mobile app.
Fetching more papers... 
