Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?

Zhavoronkov, Alex; Vanhaelen, Quentin; Oprea, Tudor I.

doi:10.1002/cpt.1795

Cited by 108 publications

(73 citation statements)

References 25 publications

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“…Such techniques can automatically extract high-dimensional abstract information without the need for manual feature design and learn nonlinear mappings between molecular structures and their biological and pharmacological properties. Deep generative models can utilize large datasets for training and perform in silico design of de novo molecular structures with predefined properties 27 . The first model of this type, a molecular generator using an adversarial auto-encoder (AAE) to generate molecular fingerprints, was released in early 2017 28 .…”

Section: Generative Chemistry Approachesmentioning

confidence: 99%

Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches

Zhavoronkov

Aladinskiy²,

Zhebrak³

et al. 2020

Preprint

Self Cite

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<div> <div> <p>The emergence of the 2019 novel coronavirus (COVID-19), for which there is no vaccine or any known effective treatment created a sense of urgency for novel drug discovery approaches. One of the most important COVID-19 protein targets is the 3C-like protease for which the crystal structure is known. Most of the immediate efforts are focused on drug repurposing of known clinically-approved drugs and virtual screening for the molecules available from chemical libraries that may not work well. For example, the IC50 of lopinavir, an HIV protease inhibitor, against the 3C-like protease is approximately 50 micromolar, which is far from ideal. In an attempt to address this challenge, on January 28th, 2020 Insilico Medicine decided to utilize a part of its generative chemistry pipeline to design novel drug-like inhibitors of COVID-19 and started generation on January 30th. It utilized three of its previously validated generative chemistry approaches: crystal-derived pocked-based generator, homology modelling-based generation, and ligand-based generation. Novel druglike compounds generated using these approaches were published at <a href="http://www.insilico.com/ncov-sprint/">www.insilico.com/ncov-sprint/</a>. Several molecules will be synthesized and tested using the internal resources; however, the team is seeking collaborations to synthesize, test, and, if needed, optimize the published molecules. <br></p> </div> </div>

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Section: Generative Chemistry Approachesmentioning

confidence: 99%

Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches

Zhavoronkov

Aladinskiy²,

Zhebrak³

et al. 2020

Preprint

Self Cite

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“…It has been suggested that one of the major challenges and emerging problems in drug design and discovery using artificial intelligence and machine learning technologies is the lack of big data in the various phases of research and development for candidate molecular compounds, such as pharmacokinetic and pharmacodynamic analysis [119]. In general, this fact also holds true for the GAN architecture because the meaningful datasets would be required to train various GAN-based frameworks at the first place.…”

Section: Limitationsmentioning

confidence: 99%

“…In general, this fact also holds true for the GAN architecture because the meaningful datasets would be required to train various GAN-based frameworks at the first place. Another potential challenge and emerging problem in drug design and discovery using artificial intelligence and machine learning technologies, which also holds true for the GAN architecture, is that overall datasets from failed clinical trials (that is, true negative data) are unavailable for the research community [119].…”

Section: Limitationsmentioning

confidence: 99%

Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design

Lin

Lane

2020

Molecules

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A growing body of evidence now suggests that artificial intelligence and machine learning techniques can serve as an indispensable foundation for the process of drug design and discovery. In light of latest advancements in computing technologies, deep learning algorithms are being created during the development of clinically useful drugs for treatment of a number of diseases. In this review, we focus on the latest developments for three particular arenas in drug design and discovery research using deep learning approaches, such as generative adversarial network (GAN) frameworks. Firstly, we review drug design and discovery studies that leverage various GAN techniques to assess one main application such as molecular de novo design in drug design and discovery. In addition, we describe various GAN models to fulfill the dimension reduction task of single-cell data in the preclinical stage of the drug development pipeline. Furthermore, we depict several studies in de novo peptide and protein design using GAN frameworks. Moreover, we outline the limitations in regard to the previous drug design and discovery studies using GAN models. Finally, we present a discussion of directions and challenges for future research.

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“…23 In our view, there are many exciting opportunities for machine learning to help clinical pharmacologists and drug discovery and development. 24,25 Often the biggest advances occur when different disciplines intersect, and pharmacometrics should be fertile ground to benefit from machine learning-indeed there is already quite a literature on this topic. In this issue we add to the discussion with examples of how machine learning can help pharmacometricians, 26 precision medicine and precision dosing, 25,27 identification of useful drug combinations, 28 and pharmacovigilance.…”

Section: Editorialmentioning

confidence: 99%

Data Science in Clinical Pharmacology and Drug Development for Improving Health Outcomes in Patients

Peck¹,

Shah

Vamvakas

et al. 2020

Clin Pharma and Therapeutics

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Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?

Cited by 108 publications

References 25 publications

Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches

Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches

Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design

Data Science in Clinical Pharmacology and Drug Development for Improving Health Outcomes in Patients

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