Prediction of the Phase Composition Profile of Three-Compound Mixtures in Liquid-Liquid Equilibrium: A Chemoinformatics Approach.

Machine-learning models were developed to predict the composition profile of a three-compound mixture in liquid-liquid equilibrium (LLE), given the global composition at certain temperature and pressure. A chemoinformatics approach was explored, based on the MOLMAP technology to encode molecules and mixtures. The chemical systems involved an ionic liquid (IL) and two organic molecules. Two complementary models have been optimized for the IL-rich and IL-poor phases. The two global optimized models are highly accurate, and were validated with independent test sets, where combinations of molecule1+molecule2+IL are different from those in the training set. These results highlight the MOLMAP encoding scheme, based on atomic properties to train models that learn relationships between features of complex multi-component chemical systems and their profile of phase compositions.

QSPR modeling of selectivity at infinite dilution of ionic liquids.

The intelligent choice of extractants and entrainers can improve current mixture separation techniques allowing better efficiency and sustainability of chemical processes that are both used in industry and laboratory practice. The most promising approach is a straightforward comparison of selectivity at infinite dilution between potential candidates. However, selectivity at infinite dilution values are rarely available for most compounds so a theoretical estimation is highly desired. In this study, we suggest a Quantitative Structure-Property Relationship (QSPR) approach to the modelling of the selectivity at infinite dilution of ionic liquids. Additionally, auxiliary models were developed to overcome the potential bias from big activity coefficient at infinite dilution from the solute. Data from SelinfDB database was used as training and internal validation sets in QSPR model development. External validation was done with the data from literature. The selection of the best models was done using decision functions that aim to diminish bias in prediction of the data points associated with the underrepresented ionic liquids or extreme temperatures. The best models were used for the virtual screening for potential azeotrope breakers of aniline + n-dodecane mixture. The subject of screening was a combinatorial library of ionic liquids, created based on the previously unused combinations of cations and anions from SelinfDB and the test set extractants. Both selectivity at infinite dilution and auxiliary models show good performance in the validation. Our models' predictions were compared to the ones of the COSMO-RS, where applicable, displaying smaller prediction error. The best ionic liquid to extract aniline from n-dodecane was suggested.

The Solubility of Gases in Ionic Liquids: A Chemoinformatic Predictive and Interpretable Approach.

This work comprises the study of solubilities of gases in ionic liquids (ILs) using a chemoinformatic approach. It is based on the codification, of the atomic inter-component interactions, cation/gas and anion/gas, which are used to obtain a pattern of activation in a Kohonen Neural Network (MOLMAP descriptors). A robust predictive model has been obtained with the Random Forest algorithm and used the maximum proximity as a confidence measure of a given chemical system compared to the training set. The encoding method has been validated with molecular dynamics. This encoding approach is a valuable estimator of attractive/repulsive interactions of a generical chemical system IL+gas. This method has been used as a fast/visual form of identification of the reasons behind the differences observed between the solubility of CO2 and O2 in 1-butyl-3-methylimidazolium hexafluorophosphate (BMIM PF6 ) at identical temperature and pressure (TP) conditions, The effect of variable cation and anion effect has been evaluated.

Chemoinformatic Approaches To Predict the Viscosities of Ionic Liquids and Ionic Liquid-Containing Systems.

Modelling, predicting, and understanding the factors influencing the viscosities of ionic liquids and related mixtures are sequentially checked in this work. The molecular maps of atom-level properties (MOLMAP codification system) is adapted for a straightforward inclusion of ionic liquids and mixtures containing ionic liquids. Random Forest models have been tested in this context and an optimal model was selected. The interpretability of the selected Random Forest model is highlighted with selected structural features that might contribute to identify low viscosities. The constructed model is able to recognize the influence of different structural variables, temperature, and pressure for a correct classification of the different systems. The codification and interpretation systems are highlighted in this work.

Ionic Liquids and Salts from Ibuprofen as Promising Innovative Formulations of an Old Drug.

Herein we report the synthesis of novel ionic liquids (ILs) and organic salts by combining ibuprofen as anion with ammonium, imidazolium, or pyridinium cations. The methodology consists of an acid-base reaction of neutral ibuprofen with cation hydroxides, which were previously prepared by anion exchange from the corresponding halide salts with Amberlyst A-26(OH). In comparison with the parent drug, these organic salts display higher solubility in water and biological fluids and a smaller degree of polymorphism, which in some cases was completely eliminated. With the exception of [C16 Pyr][Ibu] and [N1,1,2,2OH1 ][Ibu], the prepared salts did not affect the viability of normal human dermal fibroblasts or ovarian carcinoma (A2780) cells. Therefore, these ibuprofen-based ionic liquids may be very promising lead candidates for the development of effective formulations of this drug.

Tetramethylguanidine-based gels and colloids of cellulose.

Novel and stable gels of cellulose were produced. These gels are prepared at room temperature by combination of cellulose and tetramethylguanidine (TMG) in different ratios (1:1, 1:2, 1:3 in equivalents of alcohol groups of cellulose per number of molecules of TMG). Detailed NMR, SEM, rheological and XRD studies of these gels were carried out. The concentration of cellulose in the gel, temperature, frequency of oscillation and shear rate were used as variables in order to understand the fundamentals and optimize operational conditions, considering their possible use as matrices for CO2 capture. Cellulose recovery from a specific gel was performed using ethanol as precipitating agent, leading to a lower crystallinity, which permits to consider this polymer in further studies associated to physical/chemical modification of cellulose.

Machine learning of chemical reactivity from databases of organic reactions.

Databases of chemical reactions contain knowledge about the reactivity of specific reagents. Although information is in general only explicitly available for compounds reported to react, it is possible to derive information about substructures that do not react in the reported reactions. Both types of information (positive and negative) can be used to train machine learning techniques to predict if a compound reacts or not with a specific reagent. The whole process was implemented with two databases of reactions, one involving BuNH2 as the reagent, and the other NaCNBH3. Negative information was derived using MOLMAP molecular descriptors, and classification models were developed with Random Forests also based on MOLMAP descriptors. MOLMAP descriptors were based exclusively on calculated physicochemical features of molecules. Correct predictions were achieved for approximately 90% of independent test sets. While NaCNBH3 is a selective reducing reagent widely used in organic synthesis, BuNH2 is a nucleophile that mimics the reactivity of the lysine side chain (involved in an initiating step of the mechanism leading to skin sensitization).