A DFT Investigation on Two Proposed Anticancer Platinum(IV) Drugs

Aghmasheh, Simin; Abedi, Anita

doi:10.1134/s0036024420070031

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…Another remarkable usage area of Pt compounds is pharmaceutics, where water‐soluble Pt complexes with simple organic ligands gave rise to the new generation of anticancer drugs [18–24] . Investigation on biological activity and mechanism of action by use of DFT is still on high demand for rational design of novel platinum‐based antitumor drugs [25–53] …”

Section: Introductionmentioning

confidence: 99%

Predicting ¹⁹⁵Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning

2023

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Water-soluble Pt complexes are the key components in medicinal chemistry and catalysis. The well-known cisplatin family of anticancer drugs and industrial hydrosylilation catalysts are two leading examples. On the molecular level, the activity mechanisms of such complexes mostly involve changes in the Pt coordination sphere. Using 195 Pt NMR spectroscopy for operando monitoring would be a valuable tool for uncovering the activity mechanisms; however, reliable approaches for the rapid correlation of Pt complex structure with 195 Pt chemical shifts are very challenging and not available for everyday research practice. While NMR shielding is a response property, molecular 3D structure determines NMR spectra, as widely known, which allows us to build up 3D structure to 195 Pt chemical shift correlations. Accordingly, we present a new workflow for the determination of lowest-energy configurational/conformational isomers based on the GFN2-xTB semiempirical method and prediction of corresponding chemical shifts with a Machine Learning (ML) model tuned for Pt complexes. The workflow was designed for the prediction of 195 Pt chemical shifts of water-soluble Pt(II) and Pt(IV) anionic, neutral, and cationic complexes with halide, NO 2 À , (di)amino, and (di)carboxylate ligands with chemical shift values ranging from À 6293 to 7090 ppm. The model offered an accuracy (normalized root-mean-square deviation/RMSD) of 1.08 %/ 145.02 ppm on the held-out test set.

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Section: Introductionmentioning

confidence: 99%

Predicting ¹⁹⁵Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning

2023

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Pharmacological Properties of Pt(II) and Pt(IV) Complexes with 2,2′-Dipyridylamine; the Comparative In Vitro Thereof

Aghmasheh

Ostad

Abedi

2020

Cell Biochem Biophys

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Quantum mechanical approaches and molecular docking studies of metal based anticancer drugs cis-Diammine glycolato platinum and Diaminocyclohexane oxalatoplatinum structures

Sahadevan,

Sundaram,

Subramanian

2023

Computational Biology and Chemistry

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A DFT Investigation on Two Proposed Anticancer Platinum(IV) Drugs

Cited by 5 publications

References 29 publications

Predicting ¹⁹⁵Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning

Predicting ¹⁹⁵Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning

Pharmacological Properties of Pt(II) and Pt(IV) Complexes with 2,2′-Dipyridylamine; the Comparative In Vitro Thereof

Quantum mechanical approaches and molecular docking studies of metal based anticancer drugs cis-Diammine glycolato platinum and Diaminocyclohexane oxalatoplatinum structures

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A DFT Investigation on Two Proposed Anticancer Platinum(IV) Drugs

Cited by 5 publications

References 29 publications

Predicting 195Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning

Predicting 195Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning

Pharmacological Properties of Pt(II) and Pt(IV) Complexes with 2,2′-Dipyridylamine; the Comparative In Vitro Thereof

Quantum mechanical approaches and molecular docking studies of metal based anticancer drugs cis-Diammine glycolato platinum and Diaminocyclohexane oxalatoplatinum structures

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Product

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Predicting ¹⁹⁵Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning

Predicting ¹⁹⁵Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning