Reverse Hierarchy of Alkane Adsorption in Metal–Organic Frameworks (MOFs) Revealed by Immersion Calorimetry

Immersion calorimetry into liquids of different dimensions is a powerful tool to learn about the pore size and shape in nanoporous solids. In general, in the absence of specific interactions with the solid surface, the accessibility of the liquid probe molecule to the inner porosity and the associated enthalpy value decreases with an increase in its kinetic diameter (bulkier molecules have lower accessibility and packing density). Although this is true for the majority of solids (e.g., activated carbons and zeolites), this study anticipates that this is not straightforward in the specific case of metal–organic frameworks (MOFs). The evaluation of different hydrocarbons and their derivatives reveals the presence of reverse selectivity for C6 isomers (2,2-dimethylbutane > 2-methylpentane > n-hexane) in UiO-66 and HKUST-1, whereas size exclusion effects take place in ZIF-8. The immersion calorimetric findings have been compared with vapor adsorption isotherms and computational studies. Monte Carlo simulations suggest that the reverse selectivity in UiO-66 is attributed to the strong confinement of the dibranched hydrocarbons in the small tetragonal cages, whereas the presence of strong interactions with the open metal sites accounts for the preferential adsorption in HKUST-1. These results open the gate toward the application of immersion calorimetry for the prescreening of MOFs to identify in an easy, fast and reliable way interesting characteristics and/or properties such as separation ability, reversed hierarchy, pore-window size, presence of unsaturated metal sites, molecular accessibility, and so on.Authors would like to acknowledge financial support from MINECO (MAT2016-80285-p), Generalitat Valenciana (PROMETEOII/2014/004) and H2020 (MSCA-RISE-2016/NanoMed Project). P.Z.M. is grateful for start-up funds from the University of Sheffield

Uncovering activity cliff generators using distribution of SALI values

Los acantilados de actividad se definen como compuestos con alta similitud estructural, pero también con altadiferencia en potencia. Estos compuestos tienen un impacto significativo en la optimización de líderes en químicamedicinal y en aplicaciones computacionales, como el desarrollo de modelos predictivos y en la selección demoléculas de referencia en búsquedas basadas en similitud molecular. Por lo tanto, es de gran relevancia laidentificación de compuestos altamente asociados con los acantilados como por ejemplo los “generadores deacantilados de actividad”. En este trabajo se reportan la identificación de acantilados de actividad y las relacionesestructura-actividad de un grupo de 289 compuestos obtenidos por síntesis química que han sido evaluados a travésde una proteína quinasa reguladora de receptores acoplados a proteínas-G. Para considerar la información demúltiples representaciones estructurales, se empleó el promedio del índice SALI (Structure-Activity Landscape Index)y se discuten también fragmentos estructurales responsables de la actividad biológica

Uncovering activity cliff generators using distribution of SALI values

Los acantilados de actividad se definen como compuestos con alta similitud estructural, pero también con alta diferencia en potencia. Estos compuestos tienen un impacto significativo en la optimización de líderes en química medicinal y en aplicaciones computacionales, como el desarrollo de modelos predictivos y en la selección de moléculas de referencia en búsquedas basadas en similitud molecular. Por lo tanto, es de gran relevancia la identificación de compuestos altamente asociados con los acantilados como por ejemplo los “generadores de acantilados de actividad”. En este trabajo se reportan la identificación de acantilados de actividad y las relaciones estructura-actividad de un grupo de 289 compuestos obtenidos por síntesis química que han sido evaluados a través de una proteína quinasa reguladora de receptores acoplados a proteínas-G. Para considerar la información de múltiples representaciones estructurales, se empleó el promedio del índice SALI (Structure-Activity Landscape Index) y se discuten también fragmentos estructurales responsables de la actividad biológica. Activity cliffs are defined as compounds with high structure similarity but large potency difference. Identification of activity cliffs have a significant impact in lead optimization in medicinal chemistry, and computational applications such as the development of predictive models and the selection of queries for similarity searching. Therefore, the identification of compounds highly associated with activity cliffs in a given data set i.e., ‘activity cliff generators’, is of major relevance. Herein, we report the identification of activity cliffs and structure-activity relationships of a set of 289 synthetic compounds tested in a G protein-coupled receptor kinase, GRK. To account for information of multiple structure representations we used mean Structure-Activity Landscape Index (SALI). Structural fragments responsible for the activity are discussed.

Characterization of a comprehensive flavor database

Flavor perception involves, among a number of physiological and psychological processes, the recognition of chemicals by olfactory and taste receptors. The highly complex and multidimensional nature of flavor perception challenges our ability to both predict and design new flavor entities. Toward this endeavor, classifications of flavor descriptors have been proposed. Here, we developed a fingerprint‐based representation of a large data set comprising 4181 molecules taken from the commercially available Leffingwell & Associates Canton, Georgia, USA database marketed as Flavor‐Base Pro©2010. Flavor descriptions of the materials in this database were composite descriptions, collected from numerous sources over the course of more than 40 years. The flavor descriptors were referenced against a detailed and authoritative sensory lexicon (ASTM, American Society for Testing and Materials publication DS 66) comprising 662 flavor attributes. Comparison of clustering analysis, principal component analysis, and descriptor associations provided similar conclusions for various mutually correlated descriptors. Regarding analysis of the flavor similarity of the molecules, the clustering performed provided a means for the quick selection of molecules with either high or low flavor similarity description. Preliminary comparison of the chemical structures to the flavor description demonstrated the feasibility but also the complexity of this task. Additional studies including different structural representations, careful selection of subsets from this data set, as well as the use of a number of classification methods will demonstrate the utility of structure–flavor associations. This work shows that the flavor information contained in databases, such as that used in the present study, can be analyzed following standard chemoinformatics methods

MOESM1 of Database fingerprint (DFP): an approach to represent molecular databases

Additional file 1:Table S1. DFPs of representative data sets used in this work. Table S2. Inter-set relationship computed with the newly developed database fingerprint using DFP/Tanimoto coefficient. Fig. S1 Distributions of MACCS keys (166-bits) of selected data sets studied in this work (others are shown in the main text). Fig. S2 Visual representation of the distance matrix comparing inter-set relationships of the compound data sets computed with the database fingerprint (DFP) and city block distance. Fig. S3 Relationship between inverse normalized city block distance and Tanimoto similarity using the DFP. Fig. S4 Inter-set relationships of the compound data sets computed with MACCS keys and the Tanimoto coefficient. Fig. S5 Relationship between mean similarities computed with MACCS keys and DFP. Fig. S6 Relationship Shannon Entropy and DFP/Tanimoto similarity and k-mean Euclidean clustering for the ten compound data sets in Table 2 at threshold of 0.6. Fig. S7 Probability distribution of the 198 significant bit positions recovered from the original databases represented by PubChem fingerprint at threshold of 0.6.Fig. S8 Relationship Shannon Entropy and DFP/Tanimoto similarity and k-mean Euclidean clustering for the ten compound data sets in Table 2 at threshold of 0.7. Fig. S9 Probability distribution of the 198 significant bit positions recovered from the original databases represented by PubChem fingerprint at threshold of 0.7