Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents

Nucleic Acids Research, Journal Year: 2021, Volume and Issue: 50(D1), P. D1398 - D1407

Published: Oct. 5, 2021

Drug discovery relies on the knowledge of not only drugs and targets, but also comparative agents targets. These include poor binders non-binders for developing tools, prodrugs improved therapeutics, co-targets therapeutic targets multi-target strategies off-target investigations, collective structure-activity drug-likeness landscapes enhanced drug feature. However, such valuable data are inadequately covered by available databases. In this study, a major update Therapeutic Target Database, previously featured in NAR, was therefore introduced. This includes (a) 34 861 12 683 1308 targets; (b) 534 prodrug-drug pairs 121 (c) 1127 672 regulated 642 approved 624 clinical trial drugs; (d) 427 262 active 1565 (e) profiles drug-like properties 33 598 1102 Moreover, variety additional function provided, which cross-links to target structure PDB AlphaFold, 159 1658 newly emerged drugs, advanced search multi-entry sequences or structures. The database is accessible without login requirement at: https://idrblab.org/ttd/.

Language: Английский

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The emerging role of mass spectrometry-based proteomics in drug discovery

Nature Reviews Drug Discovery, Journal Year: 2022, Volume and Issue: 21(9), P. 637 - 654

Published: March 29, 2022

Language: Английский

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Artificial intelligence in cancer target identification and drug discovery

Signal Transduction and Targeted Therapy, Journal Year: 2022, Volume and Issue: 7(1)

Published: May 10, 2022

Artificial intelligence is an advanced method to identify novel anticancer targets and discover drugs from biology networks because the can effectively preserve quantify interaction between components of cell systems underlying human diseases such as cancer. Here, we review discuss how employ artificial approaches drugs. First, describe scope analysis for target investigations. Second, basic principles theory commonly used network-based machine learning-based algorithms. Finally, showcase applications in cancer identification drug discovery. Taken together, models have provided us with a quantitative framework study relationship network characteristics cancer, thereby leading potential discovery candidates.

Language: Английский

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Utilizing graph machine learning within drug discovery and development

Briefings in Bioinformatics, Journal Year: 2021, Volume and Issue: 22(6)

Published: April 8, 2021

Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, functional relationships between them, integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of topic context drug discovery development. After introducing key terms modelling approaches, move chronologically through development pipeline identify summarize work incorporating: target identification, design small molecules biologics, repurposing. Whilst field still emerging, milestones including repurposed drugs entering in vivo studies, suggest GML will become framework choice biomedical learning.

Language: Английский

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Improving target assessment in biomedical research: the GOT-IT recommendations

Nature Reviews Drug Discovery, Journal Year: 2020, Volume and Issue: 20(1), P. 64 - 81

Published: Nov. 16, 2020

Language: Английский

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Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes

Molecules and Cells, Journal Year: 2021, Volume and Issue: 44(7), P. 433 - 443

Published: July 1, 2021

Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand molecular and clinical features of cancers.A wide range emerging multiview clustering algorithms now provide unprecedented opportunities further classify cancers into subtypes, improve survival prediction therapeutic outcome these key pathophysiological processes through different layers.In this review, we overview concept rationale multi-omics in cancer research.We also introduce recent advances development integration methods for multiple-layered patients.Finally, summarize latest findings large-scale studies various their implications patient subtyping drug development.

Language: Английский

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Artificial intelligence in drug discovery: applications and techniques

Briefings in Bioinformatics, Journal Year: 2021, Volume and Issue: 23(1)

Published: Sept. 21, 2021

Artificial intelligence (AI) has been transforming the practice of drug discovery in past decade. Various AI techniques have used many applications, such as virtual screening and design. In this survey, we first give an overview on discuss related which can be reduced to two major tasks, i.e. molecular property prediction molecule generation. We then present common data resources, representations benchmark platforms. As a part are dissected into model architectures learning paradigms. To reflect technical development over years, surveyed works organized chronologically. expect that survey provides comprehensive review discovery. also provide GitHub repository with collection papers (and codes, if applicable) resource, is regularly updated.

Language: Английский

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Mass spectrometry‐based high‐throughput proteomics and its role in biomedical studies and systems biology

PROTEOMICS, Journal Year: 2022, Volume and Issue: 23(7-8)

Published: Nov. 9, 2022

Abstract There are multiple reasons why the next generation of biological and medical studies require increasing numbers samples. Biological systems dynamic, effect a perturbation depends on genetic background environment. As consequence, many conditions need to be considered reach generalizable conclusions. Moreover, human population clinical only sufficient statistical power if conducted at scale with precise measurement methods. Finally, proteins remain without functional annotations, because they have not been systematically studied under broad range conditions. In this review, we discuss latest technical developments in mass spectrometry (MS)‐based proteomics that facilitate large‐scale by fast efficient chromatography, scanning spectrometers, data‐independent acquisition (DIA), new software. We further highlight recent which demonstrate how high‐throughput (HT) can applied capture diversity, annotate gene functions or generate predictive prognostic models for diseases.

Language: Английский

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BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins

Nucleic Acids Research, Journal Year: 2023, Volume and Issue: 52(D1), P. D1110 - D1120

Published: Oct. 30, 2023

Abstract Traditional Chinese medicine (TCM) is increasingly recognized and utilized worldwide. However, the complex ingredients of TCM their interactions with human body make elucidating molecular mechanisms challenging, which greatly hinders modernization TCM. In 2016, we developed BATMAN-TCM 1.0, an integrated database ingredient–target protein interaction (TTI) for pharmacology research. Here, to address growing need a higher coverage TTI dataset, using omics data screen active or herbs disease treatment, updated version 2.0 (http://bionet.ncpsb.org.cn/batman-tcm/). Using same protocol as collected 17 068 known TTIs by manual curation (with 62.3-fold increase), predicted ∼2.3 million high-confidence TTIs. addition, incorporated three new features into version: (i) it enables simultaneous exploration target ingredient research binding proteins drug discovery; (ii) has significantly expanded coverage; (iii) website was redesigned better user experience speed. We believe that 2.0, discovery repository, will contribute study development drugs diseases.

Language: Английский

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Multi-Omics Integration for the Design of Novel Therapies and the Identification of Novel Biomarkers

Proteomes, Journal Year: 2023, Volume and Issue: 11(4), P. 34 - 34

Published: Oct. 20, 2023

Multi-omics is a cutting-edge approach that combines data from different biomolecular levels, such as DNA, RNA, proteins, metabolites, and epigenetic marks, to obtain holistic view of how living systems work interact. has been used for various purposes in biomedical research, identifying new diseases, discovering drugs, personalizing treatments, optimizing therapies. This review summarizes the latest progress challenges multi-omics designing treatments human focusing on integrate analyze multiple proteome examples use multi-proteomics identify drug targets. We also discussed future directions opportunities developing innovative effective therapies by deciphering complexity.

Language: Английский

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