Molecular descriptor analysis of approved drugs using unsupervised learning for drug repurposing

Madugula, Sita Sirisha; John, Lijo; Nagamani, Selvaraman; Gaur, Anamika Singh; Poroikov, Vladimir; Sastry, G. Narahari

doi:10.1016/j.compbiomed.2021.104856

Cited by 19 publications

(5 citation statements)

References 72 publications

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“…k-means and agglomerative clustering follow different approaches, i.e., “top-down” and “bottom-up” approaches, respectively. Clustering has been widely used in the field of medicine and biomedical sciences for applications such as selecting new candidate drugs for lung cancer [ 14 ], molecular descriptor analysis [ 15 ] or clustering the gene profiles of distinct types of cancer [ 16 ].…”

Section: Methodsmentioning

confidence: 99%

The Chemical Space of Terpenes: Insights from Data Science and AI

Hosseini

Pereira

2023

Pharmaceuticals

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Terpenes are a widespread class of natural products with significant chemical and biological diversity, and many of these molecules have already made their way into medicines. In this work, we employ a data science-based approach to identify, compile, and characterize the diversity of terpenes currently known in a systematic way, in a total of 59,833 molecules. We also employed several methods for the purpose of classifying terpene subclasses using their physicochemical descriptors. Light gradient boosting machine, k-nearest neighbours, random forests, Gaussian naïve Bayes and Multilayer perceptron were tested, with the best-performing algorithms yielding accuracy, F1 score, precision and other metrics all over 0.9, thus showing the capabilities of these approaches for the classification of terpene subclasses. These results can be important for the field of phytochemistry and pharmacognosy, as they allow the prediction of the subclass of novel terpene molecules, even when biosynthetic studies are not available.

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Section: Methodsmentioning

confidence: 99%

The Chemical Space of Terpenes: Insights from Data Science and AI

Hosseini

Pereira

2023

Pharmaceuticals

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“…This is especially relevant in drug repurposing, as existing drugs have already undergone significant preclinical and clinical testing, providing a wealth of safety data that can inform the decision-making process. Machine learning and computational approaches have also been employed to aid in SAR analysis [46]. By analyzing large datasets of chemical structures and biological activities, these methods can identify patterns and relationships that might not be immediately apparent through traditional experimentation.…”

Section: Structure-activity Relationship (Sar) Analysismentioning

confidence: 99%

Introduction to Drug Repurposing: Exploring New Applications for Existing Drugs

Ahmad,

Rauf,

Naz

et al. 2024

Drug Development and Safety

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This chapter delves into the concept of drug repurposing, which involves identifying new therapeutic applications for existing drugs. Drug repurposing offers a cost-effective and time-efficient approach to drug discovery by leveraging the knowledge and safety profiles of approved or investigational drugs. The chapter provides an overview of the principles and strategies employed in drug repurposing, including high-throughput screening, repurposing based on mechanistic insights, computational methods, and the increasing role of artificial intelligence in drug repurposing, as this is an emerging trend in the field. It explores successful case studies where repurposed drugs have shown promise in treating different diseases. Furthermore, the chapter discusses the challenges and opportunities associated with drug repurposing, including regulatory considerations and intellectual property issues. Overall, this chapter serves as a valuable resource for researchers and professionals in the field of drug development, emphasizing the potential of repurposing existing drugs to address unmet medical needs.

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“…It helps define molecular descriptors, numerical entities that represent a compound’s physicochemical properties, thereby aiding in predicting its behavior [ 46 , 47 ]. The technique is also proficient in calculating similarities between compound samples, revealing relationships among compounds, and selecting potential drug candidates [ 48 , 49 ]. In addition, k-means clustering is used for clustering compound properties and selecting protein structures based on similarities [ 46 , 48 , 50 ].…”

Section: Ai/ml Algorithms and Bio Big Data Utilized In Drug Discovery...mentioning

confidence: 99%

“…The technique is also proficient in calculating similarities between compound samples, revealing relationships among compounds, and selecting potential drug candidates [ 48 , 49 ]. In addition, k-means clustering is used for clustering compound properties and selecting protein structures based on similarities [ 46 , 48 , 50 ]. Such grouping helps analyze a drug’s effect, and by identifying similar protein conformations, it enhances the performance of ensemble docking [ 51 , 52 ].…”

Section: Ai/ml Algorithms and Bio Big Data Utilized In Drug Discovery...mentioning

confidence: 99%

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Han,

Yoon,

Kim

et al. 2023

Pharmaceuticals

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Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug–target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI’s expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI’s growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

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Molecular descriptor analysis of approved drugs using unsupervised learning for drug repurposing

Cited by 19 publications

References 72 publications

The Chemical Space of Terpenes: Insights from Data Science and AI

The Chemical Space of Terpenes: Insights from Data Science and AI

Introduction to Drug Repurposing: Exploring New Applications for Existing Drugs

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

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