Artificial intelligence and big data facilitated targeted drug discovery

Liu, Benquan; He, Huiqin; Luo, Hongyi; Zhang, Tingting; Jiang, Jingwei

doi:10.1136/svn-2019-000290

Cited by 51 publications

(29 citation statements)

References 45 publications

(45 reference statements)

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“…As such, the molecular graph representation has widespread use in molecular dynamics applications within drug discovery, such as docking, protein folding, and free energy perturbation calculations. These applications have been assisted by recent developments in AI [133,134].…”

Section: Graph Representations For Small Moleculesmentioning

confidence: 99%

Molecular representations in AI-driven drug discovery: a review and practical guide

et al. 2020

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The technological advances of the past century, marked by the computer revolution and the advent of high-throughput screening technologies in drug discovery, opened the path to the computational analysis and visualization of bioactive molecules. For this purpose, it became necessary to represent molecules in a syntax that would be readable by computers and understandable by scientists of various fields. A large number of chemical representations have been developed over the years, their numerosity being due to the fast development of computers and the complexity of producing a representation that encompasses all structural and chemical characteristics. We present here some of the most popular electronic molecular and macromolecular representations used in drug discovery, many of which are based on graph representations. Furthermore, we describe applications of these representations in AI-driven drug discovery. Our aim is to provide a brief guide on structural representations that are essential to the practice of AI in drug discovery. This review serves as a guide for researchers who have little experience with the handling of chemical representations and plan to work on applications at the interface of these fields.

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Section: Graph Representations For Small Moleculesmentioning

confidence: 99%

Molecular representations in AI-driven drug discovery: a review and practical guide

et al. 2020

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“…The ability of new data analytics to synergize with classical approaches and prior hypotheses to produce novel hypotheses and models has proven itself to be useful in applications of repositioning, target discovery, small molecule discovery, synthesis, etc. [ 29 , 30 , 31 ]. The information generated within the medical and multi-omic fields is multidimensional.…”

Section: ML Algorithms Used In Drug Discoverymentioning

confidence: 99%

“…Using ML methods, like generalized linear models through NB, the issues of analysis and interpretation of multidimensional data may be unburdened. Other ML techniques and models commonly used in these areas of analysis include regression, clustering, regularization, neural networks (NNs), decision trees, dimensionality reduction, ensemble methods, rule-based methods, and instance-based methods [ 31 , 32 ].…”

Section: ML Algorithms Used In Drug Discoverymentioning

confidence: 99%

Machine Learning Methods in Drug Discovery

et al. 2020

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The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

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“…In contrast to the conventional approach, AI, particularly ML, uses virtual screening of big data to predict therapeutic targets and identify suitable drug candidates for the disease variant [13]. ML is capable of analysing vast amounts of information from areas such as gene mapping, pharmacokinetics, solubility profiles and receptor affinities to predict properties of novel agents with their target counterparts [4].…”

Section: Stage 1: Research and Developmentmentioning

confidence: 99%

The Integration of Artificial Intelligence in Drug Discovery and Development

Archer

Germain

2021

International Journal of Digital Health

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The application of Artificial Intelligence (AI) has risen astronomically within modern society, but its influence within the pharmaceutical and healthcare sectors is set to be hugely significant. Artificial intelligence can be defined as the idea and development of computers that are able to perform tasks traditionally thought of as requiring human intelligence and subsequent learning. In this article, we will bring to light some of the diverse roles and select limitations of AI, from drug discovery and development, to marketing and purposing drugs for clinical use. The pathway of drug discovery is well documented to be time and financially intensive. With the digitsation of data and advancements in technology combined with the cumbersome process of drug discovery and subsequent development, it creates a perfect environment for integration of AI. AI can be implemented in all stages of drug discovery from helping to identify potential targets and molecules through to aiding the trial design and finally it has uses with ongoing monitoring of post market products. We consider this to be pivotal time in using AI to revolutionise the discovery process, allowing us to move away from traditional drug discovery with high value drugs and towards an enhanced number of discoveries with shorter lead time. It should be appreciated that there will be challenges still posed by this integration; data acquisition and specifically high-quality data, interpretation of the data and whether this can translate into real world results.

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Artificial intelligence and big data facilitated targeted drug discovery

Cited by 51 publications

References 45 publications

Molecular representations in AI-driven drug discovery: a review and practical guide

Molecular representations in AI-driven drug discovery: a review and practical guide

Machine Learning Methods in Drug Discovery

The Integration of Artificial Intelligence in Drug Discovery and Development

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