CheS-Mapper 2.0 for visual validation of (Q)SAR models

ChemVA: Interactive Visual Analysis of Chemical Compound Similarity in Virtual Screening

In the modern drug discovery process, medicinal chemists deal with the complexity of analysis of large ensembles of candidate molecules. Computational tools, such as dimensionality reduction (DR) and classification, are commonly used to efficiently process the multidimensional space of features. These underlying calculations often hinder interpretability of results and prevent experts from assessing the impact of individual molecular features on the resulting representations. To provide a solution for scrutinizing such complex data, we introduce ChemVA, an interactive application for the visual exploration of large molecular ensembles and their features. Our tool consists of multiple coordinated views: Hexagonal view, Detail view, 3D view, Table view, and a newly proposed Difference view designed for the comparison of DR projections. These views display DR projections combined with biological activity, selected molecular features, and confidence scores for each of these projections. This conjunction of views allows the user to drill down through the dataset and to efficiently select candidate compounds. Our approach was evaluated on two case studies of finding structurally similar ligands with similar binding affinity to a target protein, as well as on an external qualitative evaluation. The results suggest that our system allows effective visual inspection and comparison of different high-dimensional molecular representations. Furthermore, ChemVA assists in the identification of candidate compounds while providing information on the certainty behind different molecular representations.

Inhibitors or toxins? Large library target-specific screening of fullerene-based nanoparticles for drug design purpose

Fullerene-based nanoparticles have been the subject of vital interest due to their unique properties and potential application in many areas, including medicine. Here we explore their characteristics that could make them prospective leads for known disease-related proteins. High-throughput virtual screening supported by comprehensive multi-software protein-ligand docking simulation and cheminformatics approaches has been applied in investigation of interactions of 1117 proteins with a 169 fullerene nanoparticles decorated with different small molecules. Moreover, obtained docking results were confirmed by the series of unrestricted all-atom molecular dynamics (MD) simulations. Hydrophobicity of fullerene core along with hydrophilic interaction of side chains plays a key role in binding with the studied proteins. We identified a series of nanoparticles that can lead to development of robust drugs for target proteins and another series that can behave as a highly toxic agent. The structure-activity relationship analysis revealed two significant molecular properties responsible for the binding score values. The application of carefully selected computational techniques and described outcome of the study facilitate development of functional fullerene nanoparticles for drug-like and drug delivery applications.

Scaffold Hunter: a comprehensive visual analytics framework for drug discovery

The era of big data is influencing the way how rational drug discovery and the development of bioactive molecules is performed and versatile tools are needed to assist in molecular design workflows. Scaffold Hunter is a flexible visual analytics framework for the analysis of chemical compound data and combines techniques from several fields such as data mining and information visualization. The framework allows analyzing high-dimensional chemical compound data in an interactive fashion, combining intuitive visualizations with automated analysis methods including versatile clustering methods. Originally designed to analyze the scaffold tree, Scaffold Hunter is continuously revised and extended. We describe recent extensions that significantly increase the applicability for a variety of tasks.

Open source molecular modeling

The success of molecular modeling and computational chemistry efforts are, by definition, dependent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry. An updated online version of this catalog can be found at https://opensourcemolecularmodeling.github.io.