Predicting reactivity to drug metabolism: beyond P450s—modelling FMOs and UGTs

Öeren, Mario; Walton, Peter; Hunt, Peter; Ponting, David J.; Segall, Matthew D.

doi:10.1007/s10822-020-00321-1

Cited by 16 publications

(30 citation statements)

References 73 publications

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“…The oxidation works by transferring an oxygen atom from the peroxyflavin to the “soft-nucleophile” of the respective substrate, forming a hydroxyflavin and an oxidized substrate (Figure ). The final parts of the cycle of catalysis are the regeneration of FAD by releasing water and releasing nicotinamide adenine dinucleotide phosphate (NADP + ).…”

Section: Introductionmentioning

confidence: 99%

Predicting Regioselectivity of AO, CYP, FMO, and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning

et al. 2022

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Unexpected metabolism in modification and conjugation phases can lead to the failure of many late-stage drug candidates or even withdrawal of approved drugs. Thus, it is critical to predict the sites of metabolism (SoM) for enzymes, which interact with drug-like molecules, in the early stages of the research. This study presents methods for predicting the isoform-specific metabolism for human AOs, FMOs, and UGTs and general CYP metabolism for preclinical species. The models use semi-empirical quantum mechanical simulations, validated using experimentally obtained data and DFT calculations, to estimate the reactivity of each SoM in the context of the whole molecule. Ligand-based models, trained and tested using high-quality regioselectivity data, combine the reactivity of the potential SoM with the orientation and steric effects of the binding pockets of the different enzyme isoforms. The resulting models achieve κ values of up to 0.94 and AUC of up to 0.92.

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Section: Introductionmentioning

confidence: 99%

Predicting Regioselectivity of AO, CYP, FMO, and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning

et al. 2022

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“…[83,84] The specific conditions necessary for reactions to be amenable for DEL synthesis could be a limiting factor for widespread application of ML reactivity prediction models beyond DELs. Ranking cysteine protease covalent inhibitors Hybrid quantum mechanical/molecular mechanical (QM/ MM) calculations [59] Assessing reversible and irreversible covalent inhibition mechanism Hybrid quantum mechanical/molecular mechanical (QM/ MM) calculations and molecular dynamics simulations [60] Explaining mechanism of covalent inhibition Hybrid quantum mechanical/molecular mechanical (QM/ MM) calculations Site of metabolism prediction [64] Predicting metabolism by FMOs (oxydation) and UGTs (glucoronidation)…”

Section: Multihead Attention Modelmentioning

confidence: 99%

“…The variability of enzymes and isoforms involved, combined with the high plasticity and promiscuity of their catalytic sites, make structure‐based approaches very difficult and not generalizable [63] . Though the majority of recent publications have addressed CYP‐catalysed phase I reactions through ligand‐based approaches, also the combination of phase I reactions and phase II reactions has received considerable attention [64–67] …”

Section: In Vivo Chemical Reactivitymentioning

confidence: 99%

“…[63] Though the majority of recent publications have addressed CYP-catalysed phase I reactions through ligand-based approaches, also the combination of phase I reactions and phase II reactions has received considerable attention. [64][65][66][67] In general, the extent of metabolism at a certain site of a molecule depends on two main factors: 1) its intrinsic, local reactivity 2) its steric accessibility to the reactive oxidised heme groups in the catalytic site. Most published methods are based on a 680-molecule public dataset originally compiled by Zaretzki et al [68] and recently revised by de Bruyn Kops et al [69] We will briefly describe a selection of them in the next paragraphs.…”

Section: Site Of Metabolism Predictionmentioning

confidence: 99%

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Chemical Reactivity Prediction: Current Methods and Different Application Areas

Ertl

Gerebtzoff

Lewis

et al. 2022

Molecular Informatics

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The ability to predict chemical reactivity of a molecule is highly desirable in drug discovery, both ex vivo (synthetic route planning, formulation, stability) and in vivo: metabolic reactions determine pharmacodynamics, pharmacokinetics and potential toxic effects, and early assessment of liabilities is vital to reduce attrition rates in later stages of development. Quantum mechanics offer a precise description of the interactions between electrons and orbitals in the breaking and forming of new bonds. Modern algorithms and faster computers have allowed the study of more complex systems in a punctual and accurate fashion, and answers for chemical questions around stability and reactivity can now be provided. Through machine learning, predictive models can be built out of descriptors derived from quantum mechanics and cheminformatics, even in the absence of experimental data to train on. In this article, current progress on computational reactivity prediction is reviewed: applications to problems in drug design, such as modelling of metabolism and covalent inhibition, are highlighted and unmet challenges are posed.

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“…The HOMOs are the ones who provide electrons, whereas the LUMOs are the ones who accept them. Furthermore, the FMOs aid in the prediction of a molecule's most reactive site [50]. The FMOs in the electronic transitions and their energies difference (Fig.…”

Section: Frontier Molecular Orbital (Fmo) Analysismentioning

confidence: 99%

Phytochemicals as Potential Inhibitors for COVID-19 Revealed by Molecular Docking, Molecular Dynamic Simulation and DFT Studies

Puthanveedu

Muraleedharan

2022

Preprint

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The COVID-19 pandemic outbreak demands the designing of potential drugs as there is no specific treatment available. Thanks to their safety and effectiveness, phytochemicals have been used to treat various diseases, including antiviral therapeutics. Molecular docking is a simple, quick, and effective way to screen a variety of molecules for structure-based drug design. Here, we investigate molecular docking experiments on compounds present in plant species, C.hirsutus and R.rosea and show their potential for the treatment of COVID-19. Almost all the components showed higher binding energies than the built-in ligand, and those with significantly higher binding energies were explored further. Molecular mechanics-based generalized born surface area calculations were used to re-rank the top candidates, rhodionidin and cocsoline, and their stability in association with viral protease was confirmed. Density functional theory was used for detailed investigations of the geometries, electrical properties, and molecule electrostatic potential of rhodionidin and cocsoline. Using the frontier molecular orbitals method, the charge transfer within the molecule was calculated. Chemical reactivity and intermolecular interactions were studied using molecular electrostatic potential maps. These in silico discoveries will simulate the identification of powerful COVID-19 inhibitors, and similar research is likely to make a significant contribution to antiviral drug discovery.

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Predicting reactivity to drug metabolism: beyond P450s—modelling FMOs and UGTs

Cited by 16 publications

References 73 publications

Predicting Regioselectivity of AO, CYP, FMO, and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning

Predicting Regioselectivity of AO, CYP, FMO, and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning

Chemical Reactivity Prediction: Current Methods and Different Application Areas

Phytochemicals as Potential Inhibitors for COVID-19 Revealed by Molecular Docking, Molecular Dynamic Simulation and DFT Studies

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