The Avogadro application uses OpenGL to render molecular representations to the screen interactively.
Testing the statistical significance of molecular representations is a rational approach to select molecular representations to generate robust activity landscape models and identify statistically significant activity cliffs.
We expect that just as cheminformatics tools are currently applied to molecular representations to cluster, search and model molecules, these approaches can be applied to cluster, search and model transformations.
By using PyBioMed, users are able to start a full pipelining from getting molecular data, pretreating molecules, molecular representation to constructing machine learning models conveniently.
By using BioTriangle, users are able to start a full pipelining from getting molecular data, molecular representation to constructing machine learning models conveniently.
In this review, we survey the theories developed in recent years, which are categorized into Folding-based and Molecular-Mechanics-based. In addition, physical bases in the selection of CG beads/time-step, the choice of effective potentials, representation of solvent, and restoration of molecular representations back to their atomic details are systematically discussed.
To test the hypothesis whether a landscape and its activity cliffs are valid, we used several data sets and different molecular representations previously used to generate activity landscapes.
This indicates that not all molecular representations should be used to a) interpret the SAR and b) combined to generate consensus models.
To do so, in Section 2, we first define the molecular representations and similarity scores to be used in the study.
By analogy with the concept of activity [41] and property cliffs [42] this observation could be associated with a multiple-diversity cliff: compound collections that share similar diversity according to molecular representations (e.g., scaffold and fingerprints) but have opposite diversity considering other representation (e.g., physicochemical properties).
We begin by describing molecular representations and different metrics used to quantify the similarity between pairs of molecules.
