Solvent similarity index

Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center

Effective synthesis planning powered by deep learning (DL) can significantly accelerate the discovery of new drugs and materials. However, most DL-assisted synthesis planning methods offer either none or very limited capability to recommend suitable reaction conditions (RCs) for their reaction predictions. Currently, the prediction of RCs with a DL framework is hindered by several factors, including: (a) lack of a standardized dataset for benchmarking, (b) lack of a general prediction model with powerful representation, and (c) lack of interpretability. To address these issues, we first created 2 standardized RC datasets covering a broad range of reaction classes and then proposed a powerful and interpretable Transformer-based RC predictor named Parrot. Through careful design of the model architecture, pretraining method, and training strategy, Parrot improved the overall top-3 prediction accuracy on catalysis, solvents, and other reagents by as much as 13.44%, compared to the best previous model on a newly curated dataset. Additionally, the mean absolute error of the predicted temperatures was reduced by about 4 °C. Furthermore, Parrot manifests strong generalization capacity with superior cross-chemical-space prediction accuracy. Attention analysis indicates that Parrot effectively captures crucial chemical information and exhibits a high level of interpretability in the prediction of RCs. The proposed model Parrot exemplifies how modern neural network architecture when appropriately pretrained can be versatile in making reliable, generalizable, and interpretable recommendation for RCs even when the underlying training dataset may still be limited in diversity.

The surface site interaction point approach to non-covalent interactions

The functional properties of molecular systems are generally determined by the sum of many weak non-covalent interactions, and therefore methods for predicting the relative magnitudes of these interactions is fundamental to understanding the relationship between function and structure in chemistry, biology and materials science. This review focuses on the Surface Site Interaction Point (SSIP) approach which describes molecules as a set of points that capture the properties of all possible non-covalent interactions that the molecule might make with another molecule. The first half of the review focuses on the empirical non-covalent interaction parameters, α and β, and provides simple rules of thumb to estimate free energy changes for interactions between different types of functional group. These parameters have been used to have been used to establish a quantitative understanding of the role of solvent in solution phase equilibria, and to describe non-covalent interactions at the interface between macroscopic surfaces as well as in the solid state. The second half of the review focuses on a computational approach for obtaining SSIPs and applications in multi-component systems where many different interactions compete. Ab initio calculation of the Molecular Electrostatic Potential (MEP) surface is used to derive an SSIP description of a molecule, where each SSIP is assigned a value equivalent to the corresponding empirical parameter, α or β. By considering the free energies of all possible pairing interactions between all SSIPs in a molecular ensemble, it is possible to calculate the speciation of all intermolecular interactions and hence predict thermodynamic properties using the SSIMPLE algorithm. SSIPs have been used to describe both the solution phase and the solid state and provide accurate predictions of partition coefficients, solvent effects on association constants for formation of intermolecular complexes, and the probability of cocrystal formation. SSIPs represent a simple and intuitive tool for describing the relationship between chemical structure and non-covalent interactions with sufficient accuracy to understand and predict the properties of complex molecular ensembles without the need for computationally expensive simulations.

Evaluation of fatty tissue representative solvents in extraction of medical devices for chromatographic analysis of devices' extractables and leachables based on Abraham general solvation model

Extraction solvents used in chemical characterization (i.e., extractables and leachables testing, E&L) of fatty tissue-contacting medical devices for biocompatibility assessment per ISO 10993 have been studied by Abraham general solvation models. Chemically suitable alternative solvents to fatty tissues in solvation properties (solubility, partition, extraction, etc.) have been proposed based on Abraham's organic solvent system coefficients for water and air to condensed organic solvent phases. This evaluation is built upon the conclusion by Abraham, Acree Jr and Cometto-Muñiz that olive oil is chemically corresponding to fatty tissues. However, olive oil, if used as an extraction solvent to simulate fatty tissues, is in general not analytically expedient (realistic) per ISO 10993-18 (2020) for chromatographic analysis, and it is critical to seek alternative solvents to olive oil to perform the extraction. Although nonpolar solvents such as alkanes have been proposed and used as alternative solvents to vegetable oils, they are not equivalent to olive oil in solvation properties. Due to the practical challenge in chromatographic analysis of oil samples and the difference in migration kinetics of E&L between oil and organic solvents, the computational approach is the only realistic option to evaluate chemically alternative solvents to olive oil to simulate fatty tissue extraction. By comparing Abraham solvent system coefficients for water and air to condensed organic solvent phases distribution, a five-dimensional space distance (D) between solvents and olive oil as a reference solvent is calculated using Abraham and Martin equation to predict alternative or similar solvents to olive oil. The results of the calculation are further evaluated using E&L solubility ratio between solvents and olive oil, taking into consideration of solvent safety and physical properties. It is concluded from the study that butanone and dioxane are chemically the most suitable alternative or representative solvents to olive oil. They can be used as fatty tissue representative solvents in chemical characterization study of medical device. As Abraham solvation model is solvent system specific, not solute specific, the conclusions from this study are considered as universal.

Extending Microsoft excel and Hansen solubility parameters relationship to double Hansen’s sphere calculation

Abstract

Previous studies have shown the feasibility of using the Microsoft Excel "Solver" add-in to determine the Hansen solubility parameters and the radius of the Hansen sphere. Compounds such as di-block copolymer or ionic liquids are best represented by a Hansen double sphere, calculated with the HSPiP software; the development of other tools for evaluating this type of case is not reported in the literature. This communication provides the steps for the determination of a Hansen double sphere with the help of an evolutionary algorithm of Microsoft Excel, validated with, five study cases reported in the literature, with the HSPiP software itself. Other improvements for Microsoft Excel 365 version are also described.

Article Highlights

SSIPTools: Software and Methodology for Surface Site Interaction Point (SSIP) Approach and Applications

We present the SSIPTools suite of programs. SSIPTools is a collection of software modules enabling the use of the Surface Site Interaction Point (SSIP) molecular descriptors, used for the modeling of noncovalent interactions in neutral organic molecules. It contains an implementation of the workflow for the generation of the SSIP descriptors, as well as the Functional Group Interaction Profiles (FGIPs) and Solvent Similarity Indexes (SSIs) applications, based on the SSIMPLE (Surface Site Interaction model for the Properties of Liquids at Equilibria) approach.

The green solvent: a critical perspective

Solvents are important in most industrial and domestic applications. The impact of solvent losses and emissions drives efforts to minimise them or to avoid them completely. Since the 1990s, this has become a major focus of green chemistry, giving rise to the idea of the 'green' solvent. This concept has generated a substantial chemical literature and has led to the development of so-called neoteric solvents. A critical overview of published material establishes that few new materials have yet found widespread use as solvents. The search for less-impacting solvents is inefficient if carried out without due regard, even at the research stage, to the particular circumstances under which solvents are to be used on the industrial scale. Wider sustainability questions, particularly the use of non-fossil sources of organic carbon in solvent manufacture, are more important than intrinsic 'greenness'. While solvency is universal, a universal solvent, an alkahest, is an unattainable ideal.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10098-021-02188-8.

Translation of Chemical Structure into Dissipative Particle Dynamics Parameters for Simulation of Surfactant Self-Assembly

Dissipative particle dynamics (DPD) can be used to simulate the self-assembly properties of surfactants in aqueous solutions, but in order to simulate a new compound, a large number of new parameters are required. New methods for the calculation of reliable DPD parameters directly from chemical structure are described, allowing the DPD approach to be applied to a much wider range of organic compounds. The parameters required to describe the bonded interactions between DPD beads were calculated from molecular mechanics structures. The parameters required to describe the nonbonded interactions were calculated from surface site interaction point (SSIP) descriptions of molecular fragments that represent individual beads. The SSIPs were obtained from molecular electrostatic potential surfaces calculated using density functional theory and used in the SSIMPLE algorithm to calculate transfer free energies between different bead liquids. This approach was used to calculate DPD parameters for a range of different types of surfactants, which include ester, amide, and sugar moieties. The parameters were used to simulate the self-assembly properties in aqueous solutions, and comparison of the results for 27 surfactants with the available experimental data shows that these DPD simulations accurately predict critical micelle concentrations, aggregation numbers, and the shapes of the supramolecular assemblies formed. The methods described here provide a general approach to determining DPD parameters for neutral organic compounds of arbitrary structure.

Utilizing polymer-conjugate albumin-based ultrafine gas bubbles in combination with ultra-high frequency radiations in drug transportation and delivery

Ultrafine bubbles stabilized by human serum albumin conjugate polyethylene glycol ameliorates the stability of complex as well as the drug payload. Polyethylene glycol presents the crucial role in releasing drug by means of acoustic sound.

Fuzzy Divisive Hierarchical Clustering of Solvents According to Their Experimentally and Theoretically Predicted Descriptors

The present study describes a simple procedure to separate into patterns of similarity a large group of solvents, 259 in total, presented by 15 specific descriptors (experimentally found and theoretically predicted physicochemical parameters). Solvent data is usually characterized by its high variability, different molecular symmetry, and spatial orientation. Methods of chemometrics can usefully be used to extract and explore accurately the information contained in such data. In this order, advanced fuzzy divisive hierarchical-clustering methods were efficiently applied in the present study of a large group of solvents using specific descriptors. The fuzzy divisive hierarchical associative-clustering algorithm provides not only a fuzzy partition of the solvents investigated, but also a fuzzy partition of descriptors considered. In this way, it is possible to identify the most specific descriptors (in terms of higher, smallest, or intermediate values) to each fuzzy partition (group) of solvents. Additionally, the partitioning performed could be interpreted with respect to the molecular symmetry. The chemometric approach used for this goal is fuzzy c-means method being a semi-supervised clustering procedure. The advantage of such a clustering process is the opportunity to achieve separation of the solvents into similarity patterns with a certain degree of membership of each solvent to a certain pattern, as well as to consider possible membership of the same object (solvent) in another cluster. Partitioning based on a hybrid approach of the theoretical molecular descriptors and experimentally obtained ones permits a more straightforward separation into groups of similarity and acceptable interpretation. It was shown that an important link between objects’ groups of similarity and similarity groups of variables is achieved. Ten classes of solvents are interpreted depending on their specific descriptors, as one of the classes includes a single object and could be interpreted as an outlier. Setting the results of this research into broader perspective, it has been shown that the fuzzy clustering approach provides a useful tool for partitioning by the variables related to the main physicochemical properties of the solvents. It gets possible to offer a simple guide for solvents recognition based on theoretically calculated or experimentally found descriptors related to the physicochemical properties of the solvents.