The complexation of metal ions with various organic ligands in water: prediction of stability constants by QSPR ensemble modelling

Соловьев, В.П.; Kireeva, Natalia; Ovchinnikova, Svetlana I.; Tsivadze, A. Yu.

doi:10.1007/s10847-015-0543-6

Cited by 18 publications

(17 citation statements)

References 77 publications

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“…The ligands include derivatives of cyclic and acyclic polydentate molecules of various organic classes. The datasets were previously described for the complexes of VO 2+ , Co 2+ , Cu 2+ , Ni 2+ , Mg 2+ , Ca 2+ , Sr 2+ , Ba 2+ , lanthanides, Ag + , Zn 2+ , Cd 2+ , Mn 2+ , Fe 2+ , Y 3+ , La 3+ , Pb 2+ and UO 2 2+ . The number of ligands in each metal dataset is given in Figure .…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Preparation of regression models for stability constants requires experimental data measured strictly at the same conditions (solvent, temperature, ionic strength, pH, etc) which significantly limits the data availability. That's why in our earlier studies [28][29][30][31][32][33][34][35][36] of the complexation of 42 different metal ions with various organic ligands (Table 1) only minor part of the data stored in the IUPAC Stability Constants Database [23] was selected for the regression modeling. 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 [52] 3 Na + [60] Full Paper www.molinf.com…”

Section: Introductionmentioning

confidence: 99%

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Classification of Metal Binders by Naïve Bayes Classifier on the Base of Molecular Fragment Descriptors and Ensemble Modeling

Solov'ev

Tsivadze

Marcou

et al. 2019

Molecular Informatics

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Here, we report two‐class classification models for organic molecules (“ligands”) able to bind various metal cations in water. The modeling was performed on 30 data sets, each corresponding to a particular metal, using the Naïve Bayes method and the ISIDA fragment descriptors. The ligands were classified on weak and strong binders according to threshold of the logarithm of the stability constant of the 1 : 1 (metal : ligand) complexes. The “consensus models” consisted each of 50 best individual models demonstrated a good predictive performance in 5‐fold cross validation: the balanced accuracy (BA) varies from 0.965 (Yb3+) to 0.767 (Mg2+). The best predictions (BA>0.90) were obtained for the binders of rare‐earth metals (Yb3+, Tm3+, Er3+, Lu3+, Ho3+, Gd3+ and Dy3+) and Pb2+. For 17 small external test sets of new ligands, BA varies from 0.800 to 1. The impact of variables selection on the predictive performance of the models is discussed.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification of Metal Binders by Naïve Bayes Classifier on the Base of Molecular Fragment Descriptors and Ensemble Modeling

Solov'ev

Tsivadze

Marcou

et al. 2019

Molecular Informatics

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“…12 and the references therein). The ligands were mostly described using the Substructural Molecular Fragments (SMF) descriptors 8,23,24 and consensus models were developed for each metal ion separately [7][8][9][10][11][12][13][14][15] . In addition, these works did not consider any properties of the metal ion or the medium while developing the QSPR models.…”

mentioning

confidence: 99%

“…Furthermore, these models were built for either a limited class of ligands or metal ions only. Owing to these restrictions, the errors in these models were relatively high, limiting their generalizability to predict M-L binding constants across vastly different ligand chemistries [7][8][9][10][11][12][13][14][15] .…”

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confidence: 99%

Applied machine learning for predicting the lanthanide-ligand binding affinities

Chaube

Srinivasan

Rai

2020

Sci Rep

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Binding affinities of metal-ligand complexes are central to a multitude of applications like drug design, chelation therapy, designing reagents for solvent extraction etc. While state-of-the-art molecular modelling approaches are usually employed to gather structural and chemical insights about the metal complexation with ligands, their computational cost and the limited ability to predict metalligand stability constants with reasonable accuracy, renders them impractical to screen large chemical spaces. In this context, leveraging vast amounts of experimental data to learn the metal-binding affinities of ligands becomes a promising alternative. Here, we develop a machine learning framework for predicting binding affinities (logK 1) of lanthanide cations with several structurally diverse molecular ligands. Six supervised machine learning algorithms-Random Forest (RF), k-Nearest Neighbours (KNN), Support Vector Machines (SVM), Kernel Ridge Regression (KRR), Multi Layered Perceptrons (MLP) and Adaptive Boosting (AdaBoost)-were trained on a dataset comprising thousands of experimental values of logK 1 and validated in an external 10-folds cross-validation procedure. This was followed by a thorough feature engineering and feature importance analysis to identify the molecular, metallic and solvent features most relevant to binding affinity prediction, along with an evaluation of performance metrics against the dimensionality of feature space. Having demonstrated the excellent predictive ability of our framework, we utilized the best performing AdaBoost model to predict the logK 1 values of lanthanide cations with nearly 71 million compounds present in the PubChem database. Our methodology opens up an opportunity for significantly accelerating screening and design of ligands for various targeted applications, from vast chemical spaces. Rare Earth Elements (REEs), that constitute the lanthanide block of the periodic table, together with Yttrium and Scandium, lie at the heart of many modern technologies in diverse fields ranging from health care to clean energy applications 1. With increasing adoption of clean and energy efficient technologies, the demand for REEs is expected to grow manifold in the coming years 2. Although conventional mining remains the primary source of global REE supply currently 3 , owing to the huge quantities of electronic waste (e-waste) generated, REE recovery from e-wastes becomes a promising secondary source of these critical elements 4. Much of the metal processing industry relies upon hydrometallurgical operations such as liquid-liquid extraction (LLE) to recover the target element 5. The success of an LLE operation depends critically on the choice of ligands that can selectively bind to one or more target metal ions and transport them into an oil phase in contact with an aqueous phase which originally contained the metal ions. Thus, successful recovery of REEs from e-wastes calls for the design of ligands with a high affinity for one or more target lanthanide ions. The binding strength of a ...

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QSPR Modeling of Potentiometric Mg²⁺/Ca²⁺ Selectivity for PVC‐plasticized Sensor Membranes

et al. 2020

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The development of novel ionophores for ion‐selective sensors is a time‐consuming and tedious process requiring synthesis of candidate substances, preparation of plasticized polymeric membranes, and their thorough characterization with traditional protocols to assess sensitivity, selectivity and detection limits for target ions. The vast amount of literature data accumulated on various ion‐selective sensors allows for significant facilitation of the development through in silico experiments. In this report, we performed the feasibility study on the prediction of potentiometric Mg2+/Ca2+ selectivity for various amide ligands using quantitative structure‐property relationship (QSPR) modeling. The approach proved to be promising for ionophore screening purposes with achieved precision in prediction of the selectivity coefficient logK(Mg2+/Ca2+) of 0.5 in the range from −1.7 to +2.3. The study also shows a route for prediction of new potential ionophores with high selectivity values.

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The complexation of metal ions with various organic ligands in water: prediction of stability constants by QSPR ensemble modelling

Abstract: Quantitative structure-property relationship modelling of the stability constants (log K) for the 1:1 (M:L) complexes of metal ions (

Cited by 18 publications

References 77 publications

Classification of Metal Binders by Naïve Bayes Classifier on the Base of Molecular Fragment Descriptors and Ensemble Modeling

Classification of Metal Binders by Naïve Bayes Classifier on the Base of Molecular Fragment Descriptors and Ensemble Modeling

Applied machine learning for predicting the lanthanide-ligand binding affinities

QSPR Modeling of Potentiometric Mg²⁺/Ca²⁺ Selectivity for PVC‐plasticized Sensor Membranes

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The complexation of metal ions with various organic ligands in water: prediction of stability constants by QSPR ensemble modelling

Abstract: Quantitative structure-property relationship modelling of the stability constants (log K) for the 1:1 (M:L) complexes of metal ions (

Cited by 18 publications

References 77 publications

Classification of Metal Binders by Naïve Bayes Classifier on the Base of Molecular Fragment Descriptors and Ensemble Modeling

Classification of Metal Binders by Naïve Bayes Classifier on the Base of Molecular Fragment Descriptors and Ensemble Modeling

Applied machine learning for predicting the lanthanide-ligand binding affinities

QSPR Modeling of Potentiometric Mg2+/Ca2+ Selectivity for PVC‐plasticized Sensor Membranes

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QSPR Modeling of Potentiometric Mg²⁺/Ca²⁺ Selectivity for PVC‐plasticized Sensor Membranes