Review
Biochemistry & Molecular Biology
Aditi Dixit, Jeet Kalia
Summary: Metabolites play important roles in cellular processes by interacting with cellular proteins as substrates, co-enzymes, inhibitors, or activators. Traditional biochemical and structural biology-based approaches have limitations in detecting transient and low-affinity protein-metabolite interactions and lack physiological context. Mass spectrometry-based methodologies have overcome these limitations and allowed the discovery of global protein-metabolite cellular interaction networks. This article describes both traditional and modern approaches for discovering protein-metabolite interactions and discusses their impact on our understanding of cellular physiology and drug development.
Article
Biochemistry & Molecular Biology
Jiali Song, Zhenyi Xu, Lei Cao, Meng Wang, Yan Hou, Kang Li
Summary: The study developed a feature-based method, PsePDC-DTIs, to predict drug-target interactions (DTIs) for breast cancer research, achieving good prediction results and exploring new treatment targets for breast cancer.
Article
Pharmacology & Pharmacy
Tianduanyi Wang, Otto I. Pulkkinen, Tero Aittokallio
Summary: Most drug molecules have the ability to modulate multiple target proteins, which can lead to both therapeutic effects and unwanted side effects. Evaluating the selectivity of a compound is an important factor in drug development and repurposing efforts. Traditional methods for characterizing selectivity fall short in quantifying how selective a compound is against a particular target protein. In this study, we propose an optimization-based selectivity scoring method that allows for the identification of potent and selective compounds against given kinase targets. We demonstrate the effectiveness of this method in finding highly selective compounds in computational experiments using a large-scale kinase inhibitor dataset.
FRONTIERS IN PHARMACOLOGY
(2022)
Review
Pharmacology & Pharmacy
Frank W. Pun, Ivan V. Ozerov, Alex Zhavoronkov
Summary: Disease modeling and target identification are crucial in drug discovery, and artificial intelligence is increasingly being used in this field. This article reviews recent advances in AI-driven target discovery and discusses the importance of striking a balance between novelty and confidence in target selection. It also highlights the validation of AI-identified targets through experiments and the potential pathways for further development.
TRENDS IN PHARMACOLOGICAL SCIENCES
(2023)
Review
Pharmacology & Pharmacy
Namrashee V. Mehta, Mariam S. Degani
Summary: Covalent warheads, previously neglected for safety concerns, have gained attention due to their advantages over noncovalent drugs. The approval of several covalent inhibitors in the past decade has accelerated research in this area. Strategies are being developed to improve the potency and mitigate toxicity of covalent warheads.
DRUG DISCOVERY TODAY
(2023)
Review
Biochemistry & Molecular Biology
Harshita Bhargava, Amita Sharma, Prashanth Suravajhala
Summary: The in silico methods have gained popularity in drug discovery in recent years, helping to reduce costs, risks, and time associated with introducing a drug in the market. These methods are supported by big data and computational methods, and their results can be validated through in vitro or in vivo experiments. Predicting interactions between drugs and targets forms the basis for drug discovery, with various approaches such as chemogenomic methods being utilized.
Article
Pharmacology & Pharmacy
Peng Li, Chujie Bai, Lingmin Zhan, Haoran Zhang, Yuanyuan Zhang, Wuxia Zhang, Yingdong Wang, Jinzhong Zhao
Summary: Identifying the biological targets of a compound is crucial for understanding drug mechanisms and developing new drugs. The Connectivity Map concept connects genes, drugs, and diseases based on gene-expression signatures. However, existing methods are inefficient due to the need for reference drugs. In this study, we developed a procedure to extract target-induced gene modules and identified target gene clusters. Additionally, we proposed a gene module pair-based approach to predict novel compound-target interactions, leading to the discovery of new inhibitors for PI3K pathway proteins.
FRONTIERS IN PHARMACOLOGY
(2023)
Article
Biochemical Research Methods
Ruolan Chen, Feng Xia, Bing Hu, Shuting Jin, Xiangrong Liu
Summary: Prediction research on drug-target interactions is of great significance for the development of modern medicine and pharmacology. In this study, we propose a deep collaborative filtering prediction model with multiembeddings (DCFME) that leverages multiple feature information to achieve efficient and improved performance, particularly on sparse datasets.
BRIEFINGS IN BIOINFORMATICS
(2022)
Review
Pharmacology & Pharmacy
Ying-Shan Ren, Hui-Lin Li, Xiu-Hong Piao, Zhi-You Yang, Shu-Mei Wang, Yue-Wei Ge
Summary: DARTS is a novel target discovery approach that is particularly adept at screening small molecule targets without requiring any structural modifications. It can reveal drug-target interactions from cells or tissues and has been applied to uncover drug-action mechanisms.
BIOCHEMICAL PHARMACOLOGY
(2021)
Review
Biochemistry & Molecular Biology
Martina Veit-Acosta, Walter Filgueira de Azevedo Junior
Summary: This study focuses on the application of supervised machine learning modeling to predict the binding affinity of CDK2, showing that a combination of physical modeling and supervised machine learning techniques outperforms classical scoring functions. The results suggest targeted machine learning models are superior in calculating binding affinities, particularly for CDK2.
CURRENT MEDICINAL CHEMISTRY
(2022)
Article
Multidisciplinary Sciences
Paola Ruiz Puentes, Laura Rueda-Gensini, Natalia Valderrama, Isabela Hernandez, Cristina Gonzalez, Laura Daza, Carolina Munoz-Camargo, Juan C. Cruz, Pablo Arbelaez
Summary: Drug Discovery is a challenging research area with high costs and low returns. In this study, a deep learning-based approach called PLA-Net is proposed to predict target-ligand interactions more effectively. By considering the relevant chemical information of ligands and targets and utilizing adversarial augmentations, PLA-Net achieves high precision in interaction prediction.
SCIENTIFIC REPORTS
(2022)
Review
Chemistry, Multidisciplinary
Viacheslav Bolnykh, Giulia Rossetti, Ursula Rothlisberger, Paolo Carloni
Summary: This review discusses the utilization of innovative HPC algorithms and hardware in current molecular simulations and docking codes, emphasizing on the technical aspects that may not be familiar to computational pharmacologists. With the advancement of exascale computing, HPC is expected to become standardly applied in drug design campaigns and pharmacological applications, boosting accuracy and predictive power.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
(2021)
Article
Construction & Building Technology
Shu Zheng, Yi Wang, Zhiqiang (John) Zhai, Yu Xue, Lin Duanmu
Summary: This study investigated the effects of surrounding buildings on wind flow characteristics around a target building using CFD simulations. An empirical formula was developed to determine appropriate simulation domains for different building densities and heights. The results confirmed that surrounding building factors significantly impact flow simulations, and the developed guide can effectively predict wind flow conditions with acceptable accuracy.
BUILDING AND ENVIRONMENT
(2021)
Article
Pharmacology & Pharmacy
Dandan Xia, Baoling Liu, Xiaowei Xu, Ya Ding, Qiuling Zheng
Summary: Drug target discovery is essential for drug innovation, as it elucidates the mechanism of drug action. The Fe3O4 nanoparticle-based approach developed in this study shows potential for drug-target screening in biological matrices by reducing endogenous interference and providing active sites for ligand-protein interactions.
JOURNAL OF PHARMACEUTICAL ANALYSIS
(2021)
Review
Pharmacology & Pharmacy
Amelia Palermo
Summary: Metabolomics enables the analysis of metabolites and lipids in biological systems, and can guide the discovery of lead compounds from natural sources. In combination with other omics, metabolomics can elucidate compound toxicity and mode of action. This article discusses the workflows, limitations, and future opportunities of metabolomics and big data in streamlining pharmaceutical discovery and development.
DRUG DISCOVERY TODAY
(2023)
Review
Chemistry, Medicinal
Andrea Nuzzo, James R. Brown
CHEMICAL RESEARCH IN TOXICOLOGY
(2020)
Article
Biochemical Research Methods
Andrea Nuzzo, Salvatore Puccio, Claudio Martina, Biancamaria Pietrangeli, Gonzalo A. Martinez, Lorenzo Bertin, Maurizio Mancini, Fabio Fava, Giulio Zanaroli
Article
Ecology
Andrea Nuzzo, Aditi Satpute, Ute Albrecht, Sarah L. Strauss
Letter
Biotechnology & Applied Microbiology
Gavin M. Douglas, Vincent J. Maffei, Jesse R. Zaneveld, Svetlana N. Yurgel, James R. Brown, Christopher M. Taylor, Curtis Huttenhower, Morgan G. I. Langille
NATURE BIOTECHNOLOGY
(2020)
Editorial Material
Biochemistry & Molecular Biology
Andrea Nuzzo, James R. Brown
TRENDS IN MOLECULAR MEDICINE
(2020)
Article
Medicine, General & Internal
Andrea Nuzzo, Can Ozan Tan, Ramesh Raskar, Daniel C. DeSimone, Suraj Kapa, Rajiv Gupta
MAYO CLINIC PROCEEDINGS
(2020)
Article
Forestry
Antonio Castellano-Hinojosa, Bo Meyering, Andrea Nuzzo, Sarah L. Strauss, Ute Albrecht
Summary: Plant biostimulants did not improve citrus health and productivity in an HLB-endemic environment. Only fulvic acids showed significant increases in root and leaf macro- and micronutrient concentrations, which were linked to changes in specific bacterial and fungal taxa in the citrus rhizosphere. Long-term and higher application rates of fulvic acids may be needed to have measurable effects on tree health and productivity in HLB-endemic conditions. Different management practices and soil and environmental conditions can influence the efficacy of biostimulants on root and tree health.
TREES-STRUCTURE AND FUNCTION
(2021)
Article
Medicine, Research & Experimental
Keith A. Houck, Katie Paul Friedman, Madison Feshuk, Grace Patlewicz, Marci Smeltz, M. Scott Clifton, Barbara A. Wetmore, Sharlene Velichko, Antal Berenyi, Ellen L. Berg
Summary: A diverse set of 147 PFAS was screened in 12 human primary cell systems, measuring 148 biomarkers to investigate the mechanistic effects of data-poor PFAS on human model systems. The study focused on immunosuppressive activity, comparing PFAS responses to immunosuppressants. The findings suggest diverse mechanisms of action for PFAS and provide new hypotheses for their bioactivity.
ALTEX-ALTERNATIVES TO ANIMAL EXPERIMENTATION
(2023)
Article
Toxicology
Liam Simms, Elizabeth Mason, Ellen L. Berg, Fan Yu, Kathryn Rudd, Lukasz Czekala, Edgar Trelles Sticken, Oleg Brinster, Roman Wieczorek, Matthew Stevenson, Tanvir Walele
Summary: Electronic vapor products are considered a lower risk alternative to conventional cigarettes, with research showing that NGP aerosols have weaker activity compared to traditional cigarettes. Traditional cigarettes exhibit significant activity and toxicity associated biomarker signatures, while NGPs show no toxicity signatures.
CURRENT RESEARCH IN TOXICOLOGY
(2021)