Dataset Information

Biological Properties of Transition Metal Complexes with Metformin and Its Analogues.

ABSTRACT: Metformin is a widely prescribed medication for the treatment and management of type 2 diabetes. It belongs to a class of biguanides, which are characterized by a wide range of diverse biological properties, including anticancer, antimicrobial, antimalarial, cardioprotective and other activities. It is known that biguanides serve as excellent N-donor bidentate ligands and readily form complexes with virtually all transition metals. Recent evidence suggests that the mechanism of action of metformin and its analogues is linked to their metal-binding properties. These findings prompted us to summarize the existing data on the synthetic strategies and biological properties of various metal complexes with metformin and its analogues. We demonstrated that coordination of biologically active biguanides to various metal centers often resulted in an improved pharmacological profile, including reduced drug resistance as well as a wider spectrum of activity. In addition, coordination to the redox-active metal centers, such as Au(III), allowed for various activatable strategies, leading to the selective activation of the prodrugs and reduced off-target toxicity.

SUBMITTER: Rusanov DA

PROVIDER: S-EPMC9031419 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Biological Properties of Transition Metal Complexes with Metformin and Its Analogues.

Rusanov Daniil A DA Zou Jiaying J Babak Maria V MV

Pharmaceuticals (Basel, Switzerland) 20220406 4

Metformin is a widely prescribed medication for the treatment and management of type 2 diabetes. It belongs to a class of biguanides, which are characterized by a wide range of diverse biological properties, including anticancer, antimicrobial, antimalarial, cardioprotective and other activities. It is known that biguanides serve as excellent N-donor bidentate ligands and readily form complexes with virtually all transition metals. Recent evidence suggests that the mechanism of action of metform ...[more]

PMID: 35455450

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Project description:High-throughput computational screening has emerged as a critical component of materials discovery. Direct density functional theory (DFT) simulation of inorganic materials and molecular transition metal complexes is often used to describe subtle trends in inorganic bonding and spin-state ordering, but these calculations are computationally costly and properties are sensitive to the exchange-correlation functional employed. To begin to overcome these challenges, we trained artificial neural networks (ANNs) to predict quantum-mechanically-derived properties, including spin-state ordering, sensitivity to Hartree-Fock exchange, and spin-state specific bond lengths in transition metal complexes. Our ANN is trained on a small set of inorganic-chemistry-appropriate empirical inputs that are both maximally transferable and do not require precise three-dimensional structural information for prediction. Using these descriptors, our ANN predicts spin-state splittings of single-site transition metal complexes (i.e., Cr-Ni) at arbitrary amounts of Hartree-Fock exchange to within 3 kcal mol-1 accuracy of DFT calculations. Our exchange-sensitivity ANN enables improved predictions on a diverse test set of experimentally-characterized transition metal complexes by extrapolation from semi-local DFT to hybrid DFT. The ANN also outperforms other machine learning models (i.e., support vector regression and kernel ridge regression), demonstrating particularly improved performance in transferability, as measured by prediction errors on the diverse test set. We establish the value of new uncertainty quantification tools to estimate ANN prediction uncertainty in computational chemistry, and we provide additional heuristics for identification of when a compound of interest is likely to be poorly predicted by the ANN. The ANNs developed in this work provide a strategy for screening transition metal complexes both with direct ANN prediction and with improved structure generation for validation with first principles simulation.

Dataset Information

Biological Properties of Transition Metal Complexes with Metformin and Its Analogues.

Publications

Biological Properties of Transition Metal Complexes with Metformin and Its Analogues.

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