Complexation of Mn2+, Fe2+, Y3+, La3+, Pb2+, and UO22+ with Organic Ligands: QSPR Ensemble Modeling of Stability Constants

Thermodynamic, Spectroscopic, and Structural Study on a Sodium Uranyl Tri-2-Methoxybenzoate Dodecahydrate Coordination Compound with Considerably Low Solubility in Acidic Conditions

A spontaneous crystallization of an uranium(VI)-organic coordination compound with sodium and 2-methoxybenzoate (2-mba) was observed in acidic solutions, and the solubility product, molecular vibrations, crystal structure, thermal stability, and emission properties of the atypically low-soluble U(VI) complex (Na[UO₂(2-mba)₃]·12H₂O(s)) were fully investigated for the first time. A long-term solubility experiment and speciation modeling gave a solubility product of log K_s,0 = -12.18 ± 0.02 (T = 25 °C and I = 0.1 M NaClO₄), and vibrational analyses confirmed the overall molecular structure of complex and the frequencies of characteristic stretching motions of uranyl moiety as well. The crystal quality of Na[UO₂(2-mba)₃]·12H₂O(s) was improved by a digestion method, and X-ray diffraction analysis of the single crystalline specimen verified that the newly studied uranyl-organic compound contains one-dimensional channels with a diameter of 20 Å along the [001] direction; the sodium and water molecules are arranged in the channel structures. In the coordination environment around uranyl, three aromatic carboxylates are symmetrically bound in the equatorial plane of uranyl coplanarily, and the unit [UO₂(2-mba)₃]^- complexes are further extended along the plane to form the layered-morphologies. The three-dimensional packing of [UO₂(2-mba)₃]^- anions is driven by the parallel-displaced π-stacking of aromatic rings with a centroid-centroid distance of 3.7 Å. Additional thermogravimetric analysis confirmed that the Na[UO₂(2-mba)₃]·12H₂O(s) is stable up to 250 °C, and dehydration and release of the organic ligand were subsequently observed beyond that temperature. Photoluminescence spectrum of the Na[UO₂(2-mba)₃]·12H₂O(s) clearly displayed the characteristic U(VI) emission, and a band spacing between the ground electronic states of U(VI) uranyl was evaluated to be 831 ± 14 cm^-1. Such detailed characterization of the unique Na[UO₂(2-mba)₃]·12H₂O(s) is advancing upon a systematic understanding of the structural effects of the aromatic model ligands on U(VI) complexation, with relevance to the environmental chemistry of U(VI) and crystal engineering for development of diverse uranyl-organic frameworks.

Machine learning-based analysis of overall stability constants of metal-ligand complexes

The stability constants of metal(M)-ligand(L) complexes are industrially important because they affect the quality of the plating film and the efficiency of metal separation. Thus, it is desirable to develop an effective screening method for promising ligands. Although there have been several machine-learning approaches for predicting stability constants, most of them focus only on the first overall stability constant of M-L complexes, and the variety of cations is also limited to less than 20. In this study, two Gaussian process regression models are developed to predict the first overall stability constant and the n-th (n > 1) overall stability constants. Furthermore, the feature relevance is quantitatively evaluated via sensitivity analysis. As a result, the electronegativities of both metal and ligand are found to be the most important factor for predicting the first overall stability constant. Interestingly, the predicted value of the first overall stability constant shows the highest correlation with the n-th overall stability constant of the corresponding M-L pair. Finally, the number of features is optimized using validation data where the ligands are not included in the training data, which indicates high generalizability. This study provides valuable insights and may help accelerate molecular screening and design for various applications.

Design of phosphoryl containing podands with Li+/Na+ selectivity using machine learning

In this work we demonstrated, that machine learning opens a way for real design of ligands with required metal ion selectivity. We performed the ensemble QSPR modelling of the Li⁺/Na⁺ complexation selectivity and the stability constants for the Li⁺L and Na⁺L complexes of phosphoryl podands in nonaqueous solvent THF/СНCl₃ (4:1 v/v). The models were built and cross-validated using MLR with the ISIDA QSPR program and SVM with the libSVM package. The program SVMsmf was implemented to fulfil an ensemble modelling using libSVM and the Substructural Molecular Fragments (SMF) descriptors. SMF were used as descriptors for the ensemble modelling, properties predictions by consensus models and design of combinatorial library of new ligands. SMF such as the P=O group, the ether and P=O groups bound through the aromatic ring contribute significantly to the Li⁺/Na⁺ selectivity. The developed models were applied for the prediction of the studied properties for a focused virtual library of 3057 phosphoryl podands generated using SMF contributions promising for selective binding of lithium. Consensus models selected hits for a synthesis by combinatorial library screening. Among the constructed selective ligands - hits, three new podands were synthesized, for which the experimentally estimated selectivity is in satisfactory agreement with that predicted.

Spectroscopic Study on Aqueous Uranyl(VI) Complexes with Methoxy- and Methylbenzoates: Electronic and Steric Effects of the Substituents

Aqueous complexation of uranyl(VI) ions with methoxy- and methylbenzoates in 0.1 M NaClO₄ solutions was studied by means of UV-vis absorption and Raman spectroscopy. The predominance of 1:1 complexation (uranyl to ligand) was verified for all uranyl carboxylates under acidic conditions (-log [H⁺] < 3.2), and absorption spectra, stability constants, and symmetric stretching frequencies of the uranyl group of the complexes were determined for the first time. For meta- and para-substituted benzoates, a linear free energy relationship (LFER) was observed between the equilibrium constants for the protonation (log β_P) and uranyl complexation (log β_U) reactions, and the electronic effects of the substituents were successfully described by the Hammett equation. In the case of ortho-substituted benzoates, the stability constant of uranyl 2-methoxybenzoate is slightly lower than the LFER trend, which is generally explained by the destabilization of cross-conjugation in the uranyl complex due to the steric hindrance between the reaction center and adjacent methoxy group. On the contrary, the stability constant of uranyl 2-methylbenzoate is comparable to the LFER trend, implying that the steric effect is relatively insignificant for the smaller methyl group. The utility of such thermodynamic correlations between the uranyl-substituted benzoates is useful for the molecular understanding and predictive modeling of chemical interactions between actinyl(VI) ions and various organic carboxyl groups.

Applied machine learning for predicting the lanthanide-ligand binding affinities

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 metal-ligand 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 (logK1) 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 logK1 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 logK1 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.

The molecular basis of memory. Part 2: chemistry of the tripartite mechanism

We propose a tripartite mechanism to describe the processing of cognitive information (cog-info), comprising the (1) neuron, (2) surrounding neural extracellular matrix (nECM), and (3) numerous "trace" metals distributed therein. The neuron is encased in a polyanionic nECM lattice doped with metals (>10), wherein it processes (computes) and stores cog-info. Each [nECM:metal] complex is the molecular correlate of a cognitive unit of information (cuinfo), similar to a computer "bit". These are induced/sensed by the neuron via surface iontophoretic and electroelastic (piezoelectric) sensors. The generic cuinfo are used by neurons to biochemically encode and store cog-info in a rapid, energy efficient, but computationally expansive manner. Here, we describe chemical reactions involved in various processes that underline the tripartite mechanism. In addition, we present novel iconographic representations of various types of cuinfo resulting from"tagging" and cross-linking reactions, essential for the indexing cuinfo for organized retrieval and storage of memory.