Showing papers by "Viviana Consonni published in 2018"

Scaffold hopping from natural products to synthetic mimetics by holistic molecular similarity

Abstract: Natural products offer unexplored molecular frameworks for the development of chemical leads and innovative drugs. However, the structural complexity of natural products compared with synthetic drug-like molecules often limits the scaffold hopping potential of natural-product-inspired molecular design. Here we introduce a holistic molecular representation incorporating pharmacophore and shape patterns, which facilitates scaffold hopping from natural products to isofunctional synthetic compounds. This computational approach captures simultaneously the partial charge, atom distributions and molecular shape. In a prospective application, we use four natural cannabinoids as queries in a chemical database search for novel synthetic modulators of human cannabinoid receptors. Of the synthetic compounds selected by the new method, 35% are experimentally confirmed as active. These cannabinoid receptor modulators are structurally less complex than their respective natural product templates. The results of this study validate this holistic molecular representation for hit and lead finding in drug discovery. The successful prediction of drug-like structures by scaffold hopping can be limited by the structural complexity of natural products. Here, a molecular descriptor which captures partial charge, atom density distributions, and molecular shape is used to predict novel active compounds which are simpler than the original natural products.

Molecular descriptors for structure–activity applications: A hands-on approach

Abstract: Molecular descriptors capture diverse parts of the structural information of molecules and they are the support of many contemporary computer-assisted toxicological and chemical applications. After briefly introducing some fundamental concepts of structure-activity applications (e.g., molecular descriptor dimensionality, classical vs. fingerprint description, and activity landscapes), this chapter guides the readers through a step-by-step explanation of molecular descriptors rationale and application. To this end, the chapter illustrates a case study of a recently published application of molecular descriptors for modeling the activity on cytochrome P450.

Impact of Molecular Descriptors on Computational Models.

Abstract: Molecular descriptors encode a wide variety of molecular information and have become the support of many contemporary chemoinformatic and bioinformatic applications. They grasp specific molecular features (e.g., geometry, shape, pharmacophores, or atomic properties) and directly affect computational models, in terms of outcome, performance, and applicability. This chapter aims to illustrate the impact of different molecular descriptors on the structural information captured and on the perceived chemical similarity among molecules. After introducing the fundamental concepts of molecular descriptor theory and application, a step-by-step retrospective virtual screening procedure guides users through the fundamental processing steps and discusses the impact of different types of molecular descriptors.

Publisher Correction: Scaffold hopping from natural products to synthetic mimetics by holistic molecular similarity

Abstract: The original PDF and HTML versions of this Article contained errors in Eqs. (4) and (6). In Eq. (4), the not equal symbol displayed incorrectly as #, and in Eq. (6), the greater than symbol was displayed in place of the less than symbol. This has been corrected in both the PDF and HTML versions of the Article.

Mapping of Activity through Dichotomic Scores (MADS): A new chemoinformatic approach to detect activity‐rich structural regions

Abstract: A new chemoinformatic approach, called Mapping of Activity through Dichotomic Scores, is introduced. Its goal is the supervised projection of molecules, represented with strings of binary digits expressing the presence or absence of selected structural features, onto a novel 2‐dimensional space, which highlights regions of active (inactive) molecules of interest. At the same time, variables are projected onto a second 2‐dimensional space, which highlights those structural features that are more related to the molecular activity of interest. Unlike the classical weighting schemes used in substructural analysis, which consider the substructures independently of each other, the Mapping of Activity through Dichotomic Scores approach considers the interactions between pairs of substructures, that is, their frequencies of cooccurrence in the molecules. In this work, the theory is presented and elucidated, with an example dataset and in comparison with a benchmark fragment‐based scoring scheme.