Cytochrome P450 Substrate Recognition and Binding

Leach, Andrew G.; Kidley, Nathan

doi:10.1002/9783527673261.ch05

Cited by 4 publications

(1 citation statement)

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“…They are a family of heme‐containing enzymes ubiquitously found in animals, plants, fungi, and bacteria where at least 57 CYP isoforms have been documented in humans. The different CYP isoforms exhibit varying pocket sizes, shapes, binding surfaces, and flexibility, giving them different substrate specificity profiles and directing metabolism toward different parts of small molecules (Leach & Kidley, ; Mustafa, Yu, & Wade, ; Testa, ). Some CYP isoforms have remarkable ligand promiscuity driven in part by the size and plasticity of their binding sites where significant flexibility and conformational change has been revealed with molecular dynamics simulations (Mustafa et al., ).…”

Section: Introductionmentioning

confidence: 99%

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

2019

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In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, including CYP substrate and inhibitor predictors, site of metabolism predictors (i.e., metabolically labile sites within potential substrates) and metabolite structure predictors. We summarize the different approaches taken by these models, such as rule‐based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields, and docking. We highlight the scope and limitations of each method and discuss future implications for the field of metabolism prediction in drug discovery.

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Section: Introductionmentioning

confidence: 99%

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

2019

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Prediction of human cytochromeP450inhibition using bio‐selectivity induced deep neural network

Park¹,

Brahma

Shin³

et al. 2021

Bulletin Korean Chem Soc

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Since the successful debut of AlphaGo, deep learning (DL) techniques have been applied to almost all areas of data sciences and are achieving remarkable milestones. For example, in predicting cytochrome P450 (CYP450) inhibition, DL and other machine learning techniques applied were proven significantly useful. However, currently, most models are focused on how much they can improve compared to previously published methods by using different methodologies and larger data sets without considering bio‐selectivity. This study provides a multitask classification model for five important isoforms of CYP450. Our model uses the most extensive data set of over 150 000 molecules ever used. To consider the stereoisomerism of bio‐selectivity, new descriptors have been developed. We illustrated the discriminating power of the new descriptors with some examples that demonstrated the improvement in the model's performance using three‐dimensional descriptors, which account for the stereoselectivity of drug–protein/enzymes that two‐dimensional models cannot achieve.

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Predicting drug metabolism: experiment and/or computation?

Kirchmair

Göller

Lang

et al. 2015

Nat Rev Drug Discov

388

334

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Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.

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Cytochrome P450 Substrate Recognition and Binding

Cited by 4 publications

References 70 publications

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

Prediction of human cytochromeP450inhibition using bio‐selectivity induced deep neural network

Predicting drug metabolism: experiment and/or computation?

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