Article
Biochemistry & Molecular Biology
David Ochoa, Andrew Hercules, Miguel Carmona, Daniel Suveges, Asier Gonzalez-Uriarte, Cinzia Malangone, Alfredo Miranda, Luca Fumis, Denise Carvalho-Silva, Michaela Spitzer, Jarrod Baker, Javier Ferrer, Arwa Raies, Olesya Razuvayevskaya, Adam Faulconbridge, Eirini Petsalaki, Prudence Mutowo, Sandra Machlitt-Northen, Gareth Peat, Elaine McAuley, Chuang Kee Ong, Edward Mountjoy, Maya Ghoussaini, Andrea Pierleoni, Eliseo Papa, Miguel Pignatelli, Gautier Koscielny, Mohd Karim, Jeremy Schwartzentruber, David G. Hulcoop, Ian Dunham, Ellen M. McDonagh
Summary: The Open Targets Platform provides a queryable knowledgebase and user interface for systematic target identification and prioritization for drug discovery, continuously improving evidence for target-disease relationships from various data sources. They have developed a new evidence scoring framework, added evaluation of post-marketing adverse drug reactions and target tractability, and optimized user interface and backend technologies to address the challenge of developing effective and safe drugs.
NUCLEIC ACIDS RESEARCH
(2021)
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)
Article
Biochemical Research Methods
E. Amiri Souri, R. Laddach, S. N. Karagiannis, L. G. Papageorgiou, S. Tsoka
Summary: This article presents a DTI prediction pipeline based on graph embedding and gradient boosted tree classification, which efficiently integrates chemical and genomic spaces and achieves competitive results in predicting new DTIs. By applying the model to validated positive and negative interaction data, many credible novel DTIs were predicted, and some predictions were evaluated using molecular docking.
BMC BIOINFORMATICS
(2022)
Article
Chemistry, Multidisciplinary
Hai-yan Wang, Pian Yu, Xi-sha Chen, Hui Wei, Shi-jie Cao, Meng Zhang, Yi Zhang, Yong-guang Tao, Dong-sheng Cao, Feng Qiu, Yan Cheng
Summary: Physapubenolide (PB), a compound extracted from Physalis minima L., has shown cytotoxic effects on cancer cells by targeting HMGCR. This inhibition leads to decreased proliferation and migration in melanoma cells, as well as increased sensitivity to vemurafenib.
ACTA PHARMACOLOGICA SINICA
(2022)
Review
Biology
Mohammad Reza Keyvanpour, Faraneh Haddadi, Soheila Mehrmolaei
Summary: This paper proposes a quadruple framework called DTIP-TC2A for the analysis of drug-target interactions prediction (DTIP). The framework consists of four main components: categorizing DTIP methods based on technical aspects, classifying DTIP challenges, recommending general criteria for analyzing DTIP methods, and performing a qualitative analysis and comparison between different classes of DTIP approaches. The framework aims to improve the efficiency and selection of DTIP methods by addressing the challenges and providing a systematic evaluation of methods.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2022)
Review
Pharmacology & Pharmacy
Peipei Cheng, Xinting Wang, Qian Liu, Tao Yang, Huiyan Qu, Hua Zhou
Summary: Acute myocardial infarction (AMI) is a severe ischemic disease with high morbidity and mortality worldwide. Maladaptive cardiac remodeling, characterized by abnormalities in cardiac structure and function, occurs following myocardial infarction (MI). Extracellular vesicles (EVs), released by eukaryotic cells, play a significant role in post-infarction cardiac remodeling, serving as mediators of intercellular communication.
FRONTIERS IN PHARMACOLOGY
(2023)
Article
Biochemical Research Methods
Yifan Shang, Xiucai Ye, Yasunori Futamura, Liang Yu, Tetsuya Sakurai
Summary: This study introduces a new computational framework, MccDTI, for predicting potential drug-target interactions using multiview network embedding, which outperforms other methods in prediction accuracy based on experimental results.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Chemistry, Multidisciplinary
Ingoo Lee, Hojung Nam
Summary: Identifying drug-target interactions (DTIs) is crucial for drug discovery. Many deep learning models have been proposed to tackle the challenge of searching all drug-target spaces. However, interpretability in model construction has been neglected, which is closely related to model performance. In this study, we developed a deep learning model called HoTS, which predicts binding regions (BRs) and DTIs by training the model to predict important regions on a protein sequence. The proposed HoTS model demonstrated excellent performance in BR and DTI prediction, even without 3D structure information. The attention given to BRs and the use of transformers were found to be important for accurate prediction.
JOURNAL OF CHEMINFORMATICS
(2022)
Article
Biochemical Research Methods
Bo-Wei Zhao, Xiao-Rui Su, Peng-Wei Hu, Yu-An Huang, Zhu-Hong You, Lun Hu
Summary: In this study, an improved graph representation learning method called iGRLDTI is proposed to address the issue of over-smoothing in drug-target interaction (DTI) prediction. The method captures more discriminative representations of drugs and targets in a latent feature space, and achieves better performance compared to state-of-the-art computational methods on benchmark datasets.
Article
Biochemical Research Methods
Yijie Ding, Jijun Tang, Fei Guo, Quan Zou
Summary: Targeted drugs have shown therapeutic effects in cancer treatment, but detecting drug-target interactions through biochemical experiments is time-consuming. Machine learning has been widely used in large-scale drug screening, but there is a lack of methods for multiple information fusion. Researchers propose a multiple kernel-based triple collaborative matrix factorization method to predict drug-target interactions.
BRIEFINGS IN BIOINFORMATICS
(2022)
Review
Pharmacology & Pharmacy
Shengli Zhang, Jiesheng Wang, Zhenhui Lin, Yunyun Liang
Summary: The article discusses three categories of algorithms applying machine learning techniques in drug-target interactions and compares the advantages and limitations of each method. The three major problems in drug-target interactions prediction are highlighted.
CURRENT PHARMACEUTICAL DESIGN
(2021)
Review
Biochemistry & Molecular Biology
Yusuke Hosoya, Junko Ohkanda
Summary: Intrinsically disordered proteins play crucial roles in cellular processes and offer potential as new drug targets. Medicinal studies have identified low-molecular-weight inhibitors of IDPs, while liquid-liquid phase separations involving intermolecular interactions and posttranslational modifications are also analyzed for their impact on IDPs as drug targets.
Review
Cell Biology
Bradley Tucker, Kaivan Vaidya, Blake J. Cochran, Sanjay Patel
Summary: Periprocedural myocardial injury and myocardial infarction are common complications of percutaneous coronary intervention, associated with increased short- and long-term mortality. Inflammation levels before, during, and after the procedure are linked to higher rates of myonecrosis and future cardiovascular events. In addition to inflammation from underlying coronary disease, PCI itself triggers an acute inflammatory response.
Review
Pharmacology & Pharmacy
Jing Liu, Mengyu Guo, Chunying Chen
Summary: Controllable nano-assembly with stimuli-responsive groups is a powerful strategy for generating theranostic nanosystems in modern medicine. However, the understanding of complex interactions between nanoassemblies and biosystems is still lacking, which limits the development of this field. In this review, the authors propose a 4W1H principle to describe the dynamic biological processes, behavior, and fate of nano-assemblies, and summarize key parameters that govern effective nano-bio interactions. The effects of these parameters on ADMET processes are discussed, and challenges in evaluating nano-bio interactions of assembled nanodrugs are highlighted.
ADVANCED DRUG DELIVERY REVIEWS
(2022)
Article
Chemistry, Multidisciplinary
Heval Atas Guvenilir, Tunca Dogan
Summary: The identification of drug/compound-target interactions (DTIs) is crucial for drug discovery, and computational predictive approaches have been developed for this purpose. Proteochemometric (PCM) modeling is a data-driven paradigm that utilizes protein and compound properties to predict DTIs. This study investigated different computational approaches for protein featurization and evaluated their performance in DTI prediction. The findings suggested that random splitting of datasets should be avoided, learned protein sequence embeddings have high potential in DTI prediction, and PCM models tend to rely heavily on compound features while partially ignoring protein features.
JOURNAL OF CHEMINFORMATICS
(2023)
Article
Chemistry, Medicinal
O. A. Bocharova, N. S. Ionov, I. Kazeev, V. E. Shevchenko, E. Bocharov, R. Karpova, O. P. Sheychenko, A. A. Aksyonov, S. Chulkova, V. G. Kucheryanu, A. Revishchin, G. Pavlova, V. S. Kosorukov, D. A. Filimonov, A. A. Lagunin, V. B. Matveev, N. Pyatigorskaya, I. S. Stilidi, V. V. Poroikov
Summary: Many human diseases are multifactorial and cannot be efficiently treated with drugs targeting a single factor. Medicinal plant extracts containing multiple components are widely used in traditional medicines for multifactorial disorders. In silico evaluation of the pharmacological potential of these extracts can help identify promising directions for further testing and potential additive or synergistic effects.
MOLECULAR INFORMATICS
(2023)
Article
Biochemistry & Molecular Biology
Olga Tarasova, Nadezhda Biziukova, Andrey Shemshura, Dmitry Filimonov, Dmitry Kireev, Anastasia Pokrovskaya, Vladimir V. V. Poroikov
Summary: Viruses can cause infections that impact human life for different durations. HIV, the most common cause of HIV infection, can affect the immune system and lead to complications and decreased quality of life. Our study aims to identify molecular mechanisms involved in viral infection progression, using HIV as a case study. We identified human proteins and genes involved in HIV infection progression and analyzed their impact on associated biological processes in clinical studies.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Biochemistry & Molecular Biology
Anton D. Zadorozhny, Anastasia V. Rudik, Dmitry A. Filimonov, Alexey A. Lagunin
Summary: NGS technologies are being used in newborn screening to identify missense variants associated with hereditary diseases. Bioinformatic tools are currently used for variant interpretation but there is no widely accepted pathogenicity predictor. This study developed SSPR models based on protein fragment representation and compared them with traditional bioinformatic tools. The SSPR models achieved higher accuracy for certain proteins and are available in the online resource SAV-Pred.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Biochemistry & Molecular Biology
Alexey A. Lagunin, Anastasia V. Rudik, Pavel V. Pogodin, Polina I. Savosina, Olga A. Tarasova, Alexander V. Dmitriev, Sergey M. Ivanov, Nadezhda Y. Biziukova, Dmitry S. Druzhilovskiy, Dmitry A. Filimonov, Vladimir V. Poroikov
Summary: CLC-Pred 2.0 is a web application that uses structural formulas to predict cytotoxicity against various tumor and normal cell lines. It provides qualitative predictions and molecular mechanisms of actions, offering a significant improvement compared to the previous version.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Review
Biochemistry & Molecular Biology
Yuriy M. Kositsyn, Murilo S. de Abreu, Tatiana O. Kolesnikova, Alexey A. Lagunin, Vladimir V. Poroikov, Hasmik S. Harutyunyan, Konstantin B. Yenkoyan, Allan V. Kalueff
Summary: Depression and schizophrenia are common and debilitating neuropsychiatric disorders, for which current pharmacotherapies have limited efficacy and significant side effects. This highlights the need for novel drug targets. Recent advancements in translational research, research tools, and approaches have provided potential avenues for innovative drug discovery. This article provides a comprehensive overview of current treatments and outlines potential molecular targets for these disorders, while also addressing translational challenges and unanswered questions to encourage further research.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Chemistry, Medicinal
Nikita Ionov, Dmitry Filimonov, Dmitry Filimonov, Vladimir Poroikov
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Chemistry, Medicinal
I. V. Kazeev, N. S. Ionov, V. E. Shevchenko, E. V. Bocharov, R. V. Karpova, A. A. Aksenov, O. P. Sheichenko, V. G. Kucheryanu, V. S. Kosorukov, D. A. Filmonov, A. A. Lagunin, V. V. Poroikov, N. V. Pyatigorskaya, O. A. Bocharova
Summary: An herbal formulation called multiphytoadaptogen (MPA) has been studied for preventive oncology in preclinical and clinical trials. High performance liquid chromatography-mass spectrometry (HPLC-MS/MS) was used to identify secondary metabolites in the extract from Oplopanax elatus Nakai and in the MPA pharmaceutical composition. The compounds found in both MPA and the extract of O. elatus were similar to ginsenosides from Panax ginseng and pentacyclic triterpene saponins from Aralia mandshurica. In silico analysis of the biological activity of these compounds confirmed their potential in preventive oncology, consistent with previous studies on MPA.
PHARMACEUTICAL CHEMISTRY JOURNAL
(2023)
Article
Chemistry, Multidisciplinary
A. S. Kolodnitsky, N. S. Ionov, A. V. Rudik, A. A. Lagunin, D. A. Filimonov, V. V. Poroikov
Summary: The human gut microbiota (HGM) is a complex population of microorganisms that greatly impact human health, particularly in terms of xenobiotics metabolism. Many orally administered pharmaceuticals come into contact with the HGM, which can metabolize them. Therefore, it is important to evaluate the HGM's effect on pharmaceutical fate in the body. In this study, we collected information on over 600 compounds from more than eighty publications and used this data to develop three SAR models for predicting HGM-mediated drug metabolism, including compound metabolism, responsible bacterial genera, and biotransformation reactions. These models were utilized to create the freely available web application MDM-Pred (http://www.way2drug.com/mdm-pred/).
SAR AND QSAR IN ENVIRONMENTAL RESEARCH
(2023)
Article
Neurosciences
Adelya Albertovna Galiakberova, Olga Igorevna Brovkina, Nikolay Vitalyevich Kondratyev, Alexander Sergeevich Artyuhov, Ekaterina Dmitrievna Momotyuk, Olga Nikolaevna Kulmukhametova, Alexey Aleksandrovich Lagunin, Boris Vladimirovich Shilov, Anton Dmitrievich Zadorozhny, Igor Sergeevitch Zakharov, Larisa Sergeevna Okorokova, Vera Evgenievna Golimbet, Erdem Bairovich Dashinimaev
Summary: Culturing human neural stem cells (NSCs) derived from induced pluripotent stem cells (iPSC) is a promising area of research, but the stability of these cells during long-term culturing remains a challenge. This study investigated the spontaneous differentiation profile of iPSC-derived NSCs during long-term cultivation. Results showed that different NSC lines generate significantly different spectrums of differentiated neural cells, which can also change significantly during long-term cultivation. These findings have important implications for the development of optimal NSCs culturing protocols and highlight the need for further investigation into the factors influencing the stability of these cells in vitro.
FRONTIERS IN MOLECULAR NEUROSCIENCE
(2023)
Article
Chemistry, Medicinal
Anton S. Kolodnitsky, Nikita S. Ionov, Anastasia V. Rudik, Dmitry A. Filimonov, Vladimir. V. Poroikov
Summary: The metagenome of bacteria colonizing the human intestine is much larger than the host genome and encodes enzymes that expand the metabolic pathways for xenobiotics. The resulting metabolites can have different activities and affect drug efficacy and side effects. Understanding the biotransformation of small drug-like compounds by the gut microbiota is important for drug development. In vitro studies are challenging and computationally predicting metabolite structures is limited due to lack of information. This study aims to create a database of drug-like compound metabolism by the gut microbiota.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Biology
Svetlana I. Zhuravleva, Anton D. Zadorozhny, Boris V. Shilov, Alexey A. Lagunin
Summary: Drug resistance is a significant complication in cancer patients, often caused by amino acid substitutions in drug target proteins. This study developed a new approach using structural formulas to predict amino acid substitutions leading to drug resistance. The models showed high accuracy in predicting drug resistance to tyrosine-protein kinase ABL1 inhibitors. Molecular modeling and docking validated the predicted resistant amino acid substitutions.
Article
Virology
Anastasiia Iu. Paremskaia, Anastassia V. Rudik, Dmitry A. Filimonov, Alexey A. Lagunin, Vladimir V. Poroikov, Olga A. Tarasova
Summary: Predicting viral drug resistance is crucial in medical field. This study proposes two machine learning methods to predict HIV drug resistance using computational approaches, which can enhance therapy effectiveness and reduce costs.
Article
Chemistry, Multidisciplinary
Anastasia V. Rudik, Alexander V. Dmitriev, Alexey A. Lagunin, Dmitry A. Filimonov, Vladimir V. Poroikov
Summary: In this study, we developed MetaTox 2.0, which can predict the metabolites of drugs and take into account their biological activity. The tool is based on the PASS algorithm and a training set, allowing estimation of the biological activity profile of a compound. Additionally, MetaTox 2.0 enables the search for substances similar to known drug metabolic networks.
