Article
Engineering, Manufacturing
Jonathan W. Pegues, Michael A. Melia, Raymond Puckett, Shaun R. Whetten, Nicolas Argibay, Andrew B. Kustas
Summary: The study presents an efficient method for rapidly evaluating the properties of complex concentrated alloys using powder-based directed energy deposition additive manufacturing. Through in situ alloying, a broad range of compositions in a single metallurgical sample was synthesized, allowing for a quick assessment of the alloy's performance.
ADDITIVE MANUFACTURING
(2021)
Article
Biochemical Research Methods
V Divya, V. L. Pushpa
Summary: A study was conducted to generate and validate various categorical QSAR models for identifying molecules with drug-like properties in human breast cancer cells, ultimately selecting 25 potential lead molecules.
JOURNAL OF MOLECULAR GRAPHICS & MODELLING
(2021)
Article
Biochemical Research Methods
Taj Mohammad, Yash Mathur, Md Imtaiyaz Hassan
Summary: InstaDock is a free and open access GUI program that efficiently performs molecular docking and high-throughput virtual screening. It is the easiest and more interactive interface for molecular docking and high-throughput virtual screening compared to existing GUIs.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Chemistry, Medicinal
Reecan J. Juarez, Yaoyukun Jiang, Matthew Tremblay, Qianzhen Shao, A. James Link, Zhongyue J. Yang
Summary: Lasso peptides are ribosomally synthesized and posttranslationally modified peptides with a slipknot conformation, showing great potential in bioengineering and pharmaceutical applications. To facilitate computational prediction and design of lasso peptides, we developed a software called LassoHTP, which enables automatic structure construction and modeling. LassoHTP has been successfully used to generate de novo structures and simulate conformational ensembles for known lasso peptides.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Chemistry, Medicinal
Reecan J. Juarez, Yaoyukun Jiang, Matthew Tremblay, Qianzhen Shao, A. James Link, Zhongyue J. Yang
Summary: LassoHTP is a software developed for automatic construction and modeling of lasso peptide structures, enabling efficient prediction and design of lasso peptides. The software was used to construct eight known lasso peptide structures and simulate their conformational ensembles. The results show that the LassoHTP-initiated ensembles are similar to those initiated from the PDB structures. LassoHTP provides a computational platform for lasso peptide prediction and design.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Review
Biochemistry & Molecular Biology
Beatriz Suay-Garcia, Jose I. Bueso-Bordils, Antonio Falco, Gerardo M. Anton-Fos, Pedro A. Aleman-Lopez
Summary: Traditionally, drug development involved time-consuming and costly processes, but the application of virtual combinatorial chemistry and virtual screening can greatly accelerate the search and development of drugs, while saving costs.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Environmental Sciences
Heidi F. Hubbard, Caroline L. Ring, Tao Hong, Cara C. Henning, Daniel A. Vallero, Peter P. Egeghy, Michael-Rock Goldsmith
Summary: Ex Priori is a flexible exposure model based on Excel, used to prioritize potential exposures for chemicals with minimal data. It quickly visualizes exposure rankings from consumer product use and considers the impact of consumer use pattern changes on exposure risk.
Review
Biochemistry & Molecular Biology
Clara Blanes-Mira, Pilar Fernandez-Aguado, Jorge De Andres-Lopez, Asia Fernandez-Carvajal, Antonio Ferrer-Montiel, Gregorio Fernandez-Ballester
Summary: The rapid advances in 3D techniques and computational methods have led to the identification of highly active compounds in computer drug design. Molecular docking is widely used in virtual screening to filter potential ligands targeted to proteins. Consensus scoring methods improve virtual screening outcomes by averaging the rank or score of individual molecules obtained from different docking programs.
Article
Biochemistry & Molecular Biology
Rui Zhang, Caili Qiao, Qiuyan Liu, Jingwen He, Yifan Lai, Jing Shang, Hui Zhong
Summary: The study suggests that using zebrafish models in screening for antidepressants can replace rodents, with similarities in behavior and pathology. This could help shorten the drug screening cycle and achieve high-throughput screening.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Chemistry, Multidisciplinary
Matz Liebel, Irene Calderon, Nicolas Pazos-Perez, Niek F. van Hulst, Ramon A. Alvarez-Puebla
Summary: Large-scale SERS particle screening (LSSPS) is a fast multiplexed widefield screening approach that quantifies both Raman and Rayleigh scattering simultaneously, allowing for direct quantification of the fraction of SERS-active particles and unprecedented correlation of SERS activity with particle size.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2022)
Article
Chemistry, Medicinal
Omar Casanova-Alvarez, Aliuska Morales-Helguera, Miguel Angel Cabrera-Perez, Reinaldo Molina-Ruiz, Christophe Molina
Summary: In this study, an automated workflow was developed for predicting antileishmanial activity, utilizing a large, diverse, and highly imbalanced dataset of compounds. The workflow implemented a novel strategy based on balanced training sets and a consensus model using decision trees, resulting in improved predictive accuracy for the test and external sets when compared to other base models like Gaussian-Naive-Bayes and Support-Vector-Machine. The consensus model was found to be effective in prioritizing active compounds with high prediction sensitivity, demonstrating the importance of this approach in QSAR problems.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2021)
Article
Engineering, Multidisciplinary
Siwei Song, Yi Wang, Fang Chen, Mi Yan, Qinghua Zhang
Summary: This study presents a methodology that combines domain knowledge, machine learning algorithms, and experiments to accelerate the discovery of novel energetic materials. The established high-throughput virtual screening system allows for the rapid selection of candidate molecules with promising properties and desirable crystal packing modes from a large molecular space. Experimental results confirm the effectiveness of the proposed methodology.
Article
Biochemistry & Molecular Biology
Xiaojiao Zheng, Chenchen Wang, Na Zhai, Xiaogang Luo, Genyan Liu, Xiulian Ju
Summary: The study investigated a novel series of imidazo[1,2-a]-pyridine derivatives for their binding modes in the GABA(A) receptor binding pocket using various methods. The results provide important information for the development of novel drugs with antipsychotic activities.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Chemistry, Multidisciplinary
Yu-Ting Liu, Xian-Bin Li, Hui Zheng, Nian-Ke Chen, Xue-Peng Wang, Xu-Lin Zhang, Hong-Bo Sun, Shengbai Zhang
Summary: This study reports the largest scale PCM materials searching, starting with 124,515 candidate materials, and eventually screening 158 potential PCM materials using a rational high-throughput screening strategy. Further analyses revealed 52 materials with properties similar to the GST system, some of which show potential for low power consumption and suitability for optical/electrical PCM applications.
ADVANCED FUNCTIONAL MATERIALS
(2021)
Article
Biology
Shagun Krishna, Alexandre Borrel, Ruili Huang, Jinghua Zhao, Menghang Xia, Nicole Kleinstreuer
Summary: Cardiovascular disease is the leading cause of death for most ethnicities in the United States. Inhibition of the hERG channel is associated with cardiotoxicity, and evaluating the effect of environmental chemicals on hERG function is crucial for understanding potential public health risks. Machine learning approaches have been used to predict cardiotoxicity based on hERG inhibition, with the development of statistical models to assess the risk of various drugs and environmental compounds.
Article
Computer Science, Artificial Intelligence
Mona Alshahrani, Maha A. Thafar, Magbubah Essack
Summary: Linked data and bio-ontologies are essential for maintaining data integrity and enhancing search capabilities in biological and biomedical databases, while knowledge graphs have been increasingly employed for information representation. Embedding methods in knowledge graphs can predict entity relationships, improving prediction accuracy in machine learning models and decision support systems.
PEERJ COMPUTER SCIENCE
(2021)
Article
Biotechnology & Applied Microbiology
Hayedeh Behzad, Hajime Ohyanagi, Badr Alharbi, Martin Ibarra, Mohammed Alarawi, Yoshimoto Saito, Carlos M. Duarte, Vladimir Bajic, Katsuhiko Mineta, Takashi Gojobori
Summary: Increased dust emissions due to climate change and desertification could have negative impacts on marine ecosystems. A study on the Red Sea found that sandstorms led to changes in the microbiota, with a decrease in autotrophic bacteria and an increase in heterotrophic bacteria and Archaea, which recovered within a month.
Review
Cell Biology
Julijana Stanimirovic, Jelena Radovanovic, Katarina Banjac, Milan Obradovic, Magbubah Essack, Sonja Zafirovic, Zoran Gluvic, Takashi Gojobori, Esma R. Isenovic
Summary: Chronic subclinical inflammation may be a triggering factor in the development of type 2 diabetes mellitus (T2DM), and C-reactive protein (CRP) plays a key role in this process. Understanding the mechanism of CRP in T2DM is important for prevention and diagnosis.
MEDIATORS OF INFLAMMATION
(2022)
Article
Multidisciplinary Sciences
Elisabeth Prince, Jennifer Cruickshank, Wail Ba-Alawi, Kelsey Hodgson, Jillian Haight, Chantal Tobin, Andrew Wakeman, Alona Avoulov, Valentina Topolskaia, Mitchell J. Elliott, Alison P. McGuigan, Hal K. Berman, Benjamin Haibe-Kains, David W. Cescon, Eugenia Kumacheva
Summary: The researchers have developed a mimetic nanofibrilar hydrogel that supports tumor organoid growth with reduced batch variability and cell contamination.
NATURE COMMUNICATIONS
(2022)
Article
Multidisciplinary Sciences
Mona Alshahrani, Abdullah Almansour, Asma Alkhaldi, Maha A. Thafar, Mahmut Uludag, Magbubah Essack, Robert Hoehndorf
Summary: This article introduces a machine learning method that predicts drug targets and indications by combining information from knowledge graphs and published literature. By integrating different types of information, the ranking of targets and indications can be improved.
Article
Multidisciplinary Sciences
Vladan P. Bajic, Adil Salhi, Katja Lakota, Aleksandar Radovanovic, Rozaimi Razali, Lada Zivkovic, Biljana Spremo-Potparevic, Mahmut Uludag, Faroug Tifratene, Olaa Motwalli, Benoit Marchand, Vladimir B. Bajic, Takashi Gojobori, Esma R. Isenovic, Magbubah Essack
Summary: This study developed a knowledgebase dedicated to human amyloid-related diseases, obtaining relevant information through text and data mining. The knowledgebase provides a systematic way to understand amyloidosis processes and related diseases, and allows information exploration through different options.
Article
Biochemistry & Molecular Biology
Somayah Albaradei, Abdurhman Albaradei, Asim Alsaedi, Mahmut Uludag, Maha A. Thafar, Takashi Gojobori, Magbubah Essack, Xin Gao
Summary: This study proposes a computational framework that uses deep learning architecture to predict primary cancer samples and metastasized cancer samples based on gene expression profiles. Through techniques such as autoencoders and Deep LIFT, key genes are identified and a model is trained for prediction, achieving good performance.
FRONTIERS IN MOLECULAR BIOSCIENCES
(2022)
Article
Endocrinology & Metabolism
Hind Alamro, Vladan Bajic, Mirjana T. Macvanin, Esma R. Isenovic, Takashi Gojobori, Magbubah Essack, Xin Gao
Summary: MicroRNAs (miRNAs) play critical regulatory roles in gene expression in both healthy and diseased states, and have tremendous potential as a tool for improving the diagnosis of Type 2 Diabetes Mellitus (T2D) and its comorbidities. In this study, novel hub miRNAs potentially involved in T2D were computationally identified using two strategies: ranking miRNAs based on the number of T2D differentially expressed genes (DEGs) they target, and predicting and ranking miRNA using the common DEGs between T2D and Alzheimer's disease (AD). Classifier models were built using the DEGs targeted by each miRNA as features. The results showed that specific T2D DEGs targeted by certain miRNAs were capable of distinguishing T2D samples from controls, indicating their potential role in T2D progression.
FRONTIERS IN ENDOCRINOLOGY
(2023)
Review
Endocrinology & Metabolism
Mirjana T. Macvanin, Zoran Gluvic, Sonja Zafirovic, Xin Gao, Magbubah Essack, Esma R. Isenovic
Summary: Oxidative stress is the result of an imbalance between pro-oxidative and antioxidative mechanisms in cells, which can be systemic or organ-specific. The thyroid gland exhibits both oxidative and antioxidative processes, and protective nutritional antioxidants can help resolve redox imbalance and prevent chronic thyroid diseases.
FRONTIERS IN ENDOCRINOLOGY
(2023)
Article
Multidisciplinary Sciences
Hind Alamro, Maha A. Thafar, Somayah Albaradei, Takashi Gojobori, Magbubah Essack, Xin Gao
Summary: Despite being the most common cause of dementia and impaired cognitive function, an effective treatment for Alzheimer's disease (AD) remains elusive. This study developed a computational method that combines multiple hub gene ranking methods, feature selection methods, and machine learning to identify biomarkers and targets for AD. The results showed that feature selection methods outperformed hub gene sets in prediction performance, and a small number of genes were able to accurately distinguish AD samples from healthy controls.
SCIENTIFIC REPORTS
(2023)
Article
Genetics & Heredity
Maha A. Thafar, Somayah Albaradei, Mahmut Uludag, Mona Alshahrani, Takashi Gojobori, Magbubah Essack, Xin Gao
Summary: Late-stage drug development failures are often due to ineffective targets. Computational approaches can help identify proper targets by analyzing disease-related biological functions and protein data. OncoRTT, a deep learning method, was developed to predict novel therapeutic targets using features of known effective targets. It achieved high prediction performances for multiple cancer types and outperformed the state-of-the-art method in most cases. Validation evidence from the Open Targets Platform and a case study in lung cancer further supported its effectiveness.
FRONTIERS IN GENETICS
(2023)
Review
Endocrinology & Metabolism
Mirjana Macvanin, Zoran Gluvic, Jelena Radovanovic, Magbubah Essack, Xin Gao, Esma R. Isenovic
Summary: Cardiovascular disorders are increasingly common and pose a significant global health issue. Recent research emphasizes the importance of insulin-like growth factor 1 (IGF-1) in maintaining cardiovascular health.
FRONTIERS IN ENDOCRINOLOGY
(2023)
Review
Endocrinology & Metabolism
Mirjana T. Macvanin, Zoran Gluvic, Jelena Radovanovic, Magbubah Essack, Xin Gao, Esma R. Isenovic
Summary: Diabetic cardiomyopathy (DCMP) is a leading cause of morbidity and mortality in diabetic patients, and non-coding RNAs such as miRNAs and lncRNAs have been found to play a protective role in regulating processes related to DCMP. This paper reviews the literature on the roles of miRNAs and lncRNAs in DCMP in diabetes and demonstrates their potential for future DCMP treatment in diabetic patients.
FRONTIERS IN ENDOCRINOLOGY
(2023)
Article
Multidisciplinary Sciences
Maha A. Thafar, Mona Alshahrani, Somayah Albaradei, Takashi Gojobori, Magbubah Essack, Xin Gao
Summary: Drug-target interaction (DTI) prediction is crucial for drug repositioning and virtual drug screening. Most methods require 3D structural information of targets, while a few non-structure-based methods have been proposed. In this study, we propose a novel regression-based method, Affinity2Vec, which formulates the task as a graph-based problem and combines computational techniques to predict drug-target binding affinity.
SCIENTIFIC REPORTS
(2022)