Target identification of small molecules: an overview of the current applications in drug discovery

Tabana, Yasser; Babu, Dinesh; Fahlman, Richard; Siraki, Arno G.; Barakat, Khaled

doi:10.1186/s12896-023-00815-4

Cited by 24 publications

(9 citation statements)

References 125 publications

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“…This approach is based on data such as gene expression profiling and genome sequencing to discover potential targets by comparing the differences between diseased and normal tissues. 1 The main genetic methods currently used for drug development are GWAS, WES, and WGS. 36-39…”

Section: Human Genetics Methodologies Used In Drug Developmentmentioning

confidence: 99%

“…Drug targets are the direct binding sites of drugs and biomolecules in the body, including receptors, enzymes, ion channels, transporters, nucleic acids, and other biomolecules. 1 Drug development is a long and expensive process, with a high degree of uncertainty from drug target identification to clinical trials and access to the market. A major challenge to drug development is the high failure rate in clinical development, which increases the cost and slows down the development of new drugs.…”

Section: Introductionmentioning

confidence: 99%

“…The key to modern new drug development lies in finding and identifying drug targets, which has become the focus of intense competition in innovative drug development. 1 Therefore, the discovery of new targets with high clinical translation rates is a prominent support for innovative breakthroughs in drug development.…”

Section: Introductionmentioning

confidence: 99%

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Drug development advances in human genetics‐based targets

Zhang,

Yu,

et al. 2024

MedComm

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Drug development is a long and costly process, with a high degree of uncertainty from the identification of a drug target to its market launch. Targeted drugs supported by human genetic evidence are expected to enter phase II/III clinical trials or be approved for marketing more quickly, speeding up the drug development process. Currently, genetic data and technologies such as genome‐wide association studies (GWAS), whole‐exome sequencing (WES), and whole‐genome sequencing (WGS) have identified and validated many potential molecular targets associated with diseases. This review describes the structure, molecular biology, and drug development of human genetics‐based validated beneficial loss‐of‐function (LOF) mutation targets (target mutations that reduce disease incidence) over the past decade. The feasibility of eight beneficial LOF mutation targets (PCSK9, ANGPTL3, ASGR1, HSD17B13, KHK, CIDEB, GPR75, and INHBE) as targets for drug discovery is mainly emphasized, and their research prospects and challenges are discussed. In conclusion, we expect that this review will inspire more researchers to use human genetics and genomics to support the discovery of novel therapeutic drugs and the direction of clinical development, which will contribute to the development of new drug discovery and drug repurposing.

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Section: Human Genetics Methodologies Used In Drug Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Drug development advances in human genetics‐based targets

Zhang,

Yu,

et al. 2024

MedComm

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“…传统的药物靶标鉴定方法是亲和色谱-蛋白质谱学联用法 [2] . 此外, 目前主要的靶标筛选策略, 无论是基于亲和力的下拉方法与基于活性的蛋白质分析(activity-based protein profiling, ABPP), 还是药物亲和反应的靶点稳定性分析(drug affinity responsive target stability, DARTS)、蛋白质氧化速率稳定性分析(stability of proteins from rates of oxidation, SPROX)及热蛋白质组学分析(thermal proteome profiling, TPP)等无标记的靶标识别方法往往都需要质谱技术对目标蛋白质进行鉴定 [3] . 虽然近年来, 随着蛋白质谱仪器技术的不断革新, 质谱在数据质量及结果可重复性方面取得了较大进展.…”

Section: 天然产物是在漫长的生物进化过程中通过自然筛选优unclassified

应用基于mRNA展示技术的新型药物靶标筛选平台探索地黄苷促进造血的直接作用靶点及机制

Hong,

Du,

Sun

2024

Chin. Sci. Bull.

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“…In antiviral research, traditional methods for classifying the binding activities of small molecules, while effective, often involve labor-intensive and time-consuming experimental procedures. 4 The advent of quantitative high-throughput screening (qHTS) techniques has revolutionized this landscape by generating extensive datasets. However, this abundance of data presents a dual challenge: its complexity and volume often exceed the processing capabilities of conventional analytical methods.…”

Section: Introductionmentioning

confidence: 99%

Binding Activity Classification of Anti-SARS-CoV-2 Molecules using Deep Learning across Multiple Assays

Eren Yamasan,

Korkmaz

2024

Balkan Med J

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Background: The coronavirus disease-2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has urgently necessitated effective therapeutic solutions, with a focus on rapidly identifying and classifying potential small-molecule drugs. Given traditional methods’ labor-intensive and time-consuming nature, deep learning has emerged as an essential tool for efficiently processing and extracting insights from complex biological data. Aims: To utilize deep learning techniques, particularly deep neural networks (DNN) enhanced with the synthetic minority oversampling technique (SMOTE), to enhance the classification of binding activities in anti-SARS-CoV-2 molecules across various bioassays. Methods: We used 11 bioassay datasets covering various SARS-CoV-2 interactions and inhibitory mechanisms. These assays ranged from spike-ACE2 protein-protein interaction to ACE2 enzymatic activity and 3CL enzymatic activity. To address the prevalent class imbalance in these datasets, the SMOTE technique was employed to generate new samples for the minority class. In our model-building approach, we divided the dataset into 80% training and 20% test sets, reserving 10% of the training set for validation. Our approach involved employing a DNN that integrates ReLU and sigmoid activation functions, incorporates batch normalization, and uses Adam optimization. The hyperparameters and architecture of the DNN were optimized through various tests on layers, minibatch sizes, epoch sizes, and learning rates. A 40% dropout rate was incorporated to mitigate overfitting. For model evaluation, we computed performance metrics, such as balanced accuracy (BACC), precision, recall, F1 score, Matthews’ correlation coefficient (MCC), and area under the curve (AUC). Results: The performance of the DNN across 11 bioassay test sets revealed varying outcomes, significantly influenced by the ratios of active-to-inactive compounds. Assays, such as AlphaLISA and CoV-PPE, demonstrated robust performance across various metrics, including BACC, precision, recall, and AUC, when configured with more balanced ratios (1:3 and 1:1, respectively). This suggests the effective identification of active compounds in both cases. In contrast, assays with higher imbalance ratios, such as 3CL (1:38) and cytopathic effect (1:15), demonstrated higher recall but lower precision, highlighting challenges in accurately identifying active compounds among numerous inactive compounds. However, even in these challenging settings, the model achieved favorable BACC and recall scores. Overall, the DNN model generally performed well, as indicated by the BACC, MCC, and AUC values, especially when considering the degree of dataset imbalance in each assay. Conclusion: This study demonstrates the significant impact of deep learning, particularly DNN models enhanced with SMOTE, in improving the identification of active compou...

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Target identification of small molecules: an overview of the current applications in drug discovery

Cited by 24 publications

References 125 publications

Drug development advances in human genetics‐based targets

Drug development advances in human genetics‐based targets

应用基于mRNA展示技术的新型药物靶标筛选平台探索地黄苷促进造血的直接作用靶点及机制

Binding Activity Classification of Anti-SARS-CoV-2 Molecules using Deep Learning across Multiple Assays

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