Machine learning-augmented docking. 1. CYP inhibition prediction
Literature Information
Benjamin Weiser, Jérôme Genzling, Mihai Burai-Patrascu, Ophélie Rostaing
A significant portion of the oxidative metabolism carried out by the human body is accomplished by six cytochrome P450 (CYP) enzymes. The binding of small molecules to these enzymes affects drug activity and half-life. Additionally, the inhibition or induction of a CYP isoform by a drug can lead to drug–drug interactions, which in turn can lead to toxicity. To predict CYP inhibition, a variety of computational methods have been used, with docking methods being less accurate than machine learning (ML) methods. However, the latter methods are sensitive to training data and show reduced accuracy on test sets outside of the chemical space represented in the training set. In contrast, docking methods do not have this generalization issue and allow for visual analysis. We hypothesize that combining ML methods with docking can improve CYP inhibition predictions. To test this hypothesis, we pair our in-house docking program FITTED with several ML techniques to investigate the accuracy and transferability of this hybrid methodology, which we term ML-augmented docking. We find that ML-augmented docking can significantly improve the accuracy of docking software while consistently surpassing the performance of ligand-only models. Additionally, we show that ML-augmented docking is more generalizable than machine learning models trained on ligand-only data. The open-source code created for this project can be found at https://github.com/MoitessierLab/ML-augmented-docking-CYP-inhibition.
Related Literature
PICVib: an accurate, fast and simple procedure to investigate selected vibrational modes and evaluate infrared intensities
Marcus V. P. dos Santos, Yaicel G. Proenza, Ricardo L. Longo
DOI: 10.1039/C4CP02279C
Platinum–hydrogen vibrations and low energy electronic excitations of 13-atom Pt nanoclusters
Melanie Keppeler
DOI: 10.1039/C4CP02052A
Systematic study on novel catalytic activity of CO oxidation driven by strong electronic interaction between the monatomic-layered Pt30 cluster disk and the Si substrate
Hisato Yasumatsu, Nobuyuki Fukui
DOI: 10.1039/C4CP02221A
High-density biosynthetic fuels: the intersection of heterogeneous catalysis and metabolic engineering
Benjamin G. Harvey, Heather A. Meylemans, Raina V. Gough, Roxanne L. Quintana, Michael D. Garrison, Thomas J. Bruno
DOI: 10.1039/C3CP55349C
Orientation effects in morphology and electronic properties of anatase TiO2 one-dimensional nanostructures. II. Nanotubes
Dmitri B. Migas, Andrew B. Filonov, Victor E. Borisenko
DOI: 10.1039/C3CP54906B
Ab initio and metadynamics studies on the role of essential functional groups in biomineralization of calcium carbonate and environmental situations
Moumita Saharay, R. James Kirkpatrick
DOI: 10.1039/C4CP03904A
Spectro-microscopic photoemission evidence of charge uncompensated areas in Pb(Zr,Ti)O3(001) layers
Dana Georgeta Popescu, Marius Adrian Huşanu, Lucian Trupinǎ, Luminiţa Hrib, Lucian Pintilie, Alexei Barinov, Silvano Lizzit, Paolo Lacovig, Cristian Mihail Teodorescu
DOI: 10.1039/C4CP04546G
Di- and tri-oxalkyl derivatives of a boron dipyrromethene (BODIPY) rotor dye in lipid bilayers
Marie Olšinová, Piotr Jurkiewicz, Michal Pozník, Radek Šachl, Tereza Prausová, Martin Hof, Václav Kozmík, Filip Teplý, Jiří Svoboda, Marek Cebecauer
DOI: 10.1039/C4CP00888J
Structures and optical properties of two phases of SrMgF4
Alexander P. Yelisseyev, Lei Bai, Zheshuai Lin, Alina A. Goloshumova, Sergei I. Lobanov, Dmitry Y. Naumov
DOI: 10.1039/C4CP04689G
Stress in titania nanoparticles: an atomistic study
Robert Darkins, Maria L. Sushko, Jun Liu, Dorothy M. Duffy
DOI: 10.1039/C3CP54357A
You might also like
What are the main uses of 1H-Indazole-6-carbonitrile (CAS: 141290-59-7)?
1H-Indazole-6-carbonitrile finds applications in pharmaceuticals, where it serve...
How should waste containing Dioctyl (2E)-2-butenedioate (CAS: 2997-85-5) be handled?
Waste containing Dioctyl (2E)-2-butenedioate (CAS: 2997-85-5) should be collecte...
What industries use Sodium [(1,2-benzoxazol-3-ylmethyl)sulfonyl]azanide (CAS: 68291-98-5)?
Sodium [(1,2-benzoxazol-3-ylmethyl)sulfonyl]azanide is primarily used in pharmac...
Are there alternatives to Dimethyl 4-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)-2,6-pyridinedicarboxylate (CAS: 741709-66-0) in synthesis?
Dimethyl 4-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)-2,6-pyridinedicarboxyla...
How should waste containing 2-Fluoro-6-hydrazinopyridine (CAS: 80714-39-2) be handled?
Waste containing 2-Fluoro-6-hydrazinopyridine (CAS: 80714-39-2) should be manage...
What is 6-Formyl-2-pyridinecarboxylic acid (CAS: 499214-11-8)?
6-Formyl-2-pyridinecarboxylic acid is an organic compound with the molecular for...
What is the market or research trend for 3-(3,4-dimethoxyphenyl)-2,5-dimethyl-N-(2-morpholin-4-ylethyl)pyrazolo[1,5-a]pyrimidin-7-amine (CAS: 900874-91-1)?
Research trends for this compound indicate a focus on its potential applications...
How is 9H-Tribenzo[b,d,f]azepine (CAS: 29875-73-8) typically synthesized?
9H-Tribenzo[b,d,f]azepine is typically synthesized via a multi-step process invo...
How is 1-Cyclopropyl-7-ethoxy-6-fluoro-8-methoxy-4-oxo-1,4-dihydro-3-quinolinecarboxylic acid (CAS: 1797982-51-4) typically synthesized?
1-Cyclopropyl-7-ethoxy-6-fluoro-8-methoxy-4-oxo-1,4-dihydro-3-quinolinecarboxyli...
How should waste containing Methyl 3-oxo-1,2,3,4-tetrahydro-6-quinoxalinecarboxylate (CAS: 671820-52-3) be handled?
Waste containing Methyl 3-oxo-1,2,3,4-tetrahydro-6-quinoxalinecarboxylate (CAS: ...












![(1S)-1,5-Anhydro-1-[3-(1-benzothiophen-2-ylmethyl)-4-fluorophenyl]-D-glucitol structure (1S)-1,5-Anhydro-1-[3-(1-benzothiophen-2-ylmethyl)-4-fluorophenyl]-D-glucitol structure](https://static.chemtradehub.com/structs/761/761423-87-4-dbeb.webp)


