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New technologies are shaping the way drug discovery data is analyzed and shared. Open data initiatives and web servers are assisting the analysis of the large amounts of data that we are now able to produce. The final goal is to accelerate the process of moving from new data to useful information that could lead to treatments for human diseases. This review discusses open chemoinformatic resources to analyze the diversity and coverage of the chemical space of screening libraries and to explore structure–activity relationships of screening data sets. Free resources to implement workflows and representative web-based applications are emphasized. Future directions in this field are also discussed.

Aim: Fungi are valuable resources for bioactive secondary metabolites. However, the chemical space of fungal secondary metabolites has been studied only on a limited basis. Herein, we report a comprehensive chemoinformatic analysis of a unique set of 207 fungal metabolites isolated and characterized in a USA National Cancer Institute funded drug discovery project. Results: Comparison of the molecular complexity of the 207 fungal metabolites with approved anticancer and nonanticancer drugs, compounds in clinical studies, general screening compounds and molecules Generally Recognized as Safe revealed that fungal metabolites have high degree of complexity. Molecular fingerprints showed that fungal metabolites are as structurally diverse as other natural products and have, in general, drug-like physicochemical properties. Conclusion: Fungal products represent promising candidates to expand the medicinally relevant chemical space. This work is a significant expansion of an analysis reported years ago for a smaller set of compounds (less than half of the ones included in the present work) from filamentous fungi using different structural properties.

Activity landscape modeling is a powerful method for the quantitative analysis of structure-activity relationships. This cheminformatics area is in continuous growth, and several quantitative and visual approaches are constantly being developed. However, these approaches often fall into disuse due to their limited access. Herein, we present Activity Landscape Plotter as the first freely available web-based tool to automatically analyze structure-activity relationships of compound data sets. Based on the concept of activity landscape modeling, the online service performs pairwise structure and activity relationships from an input data set supplied by the user. For visual analysis, Activity Landscape Plotter generates Structure-Activity Similarity and Dual-Activity Difference maps. The user can interactively navigate through the maps and export all the pairwise structure-activity information as comma delimited files. Activity Landscape Plotter is freely accessible at https://unam-shiny-difacquim.shinyapps.io/ActLSmaps /.

Many drug discovery projects rely on commercial compounds to discover active leads. However, current commercial libraries, with mostly synthetic compounds, access a small fraction of the possible chemical diversity. Natural products, in contrast, possess a vast structural diversity and have proven to be an outstanding source of new drugs. Several chemoinformatic analyses of natural products have demonstrated their diversity and structural complexity. However, to our knowledge, the scaffold content and structural diversity of fungal secondary metabolites have never been studied. Herein, the scaffold diversity of 223 fungal metabolites was measured and compared to the diversity of approved drugs and commercial libraries for HTS containing natural, synthetic, and semi-synthetic compounds. In addition, the global diversity of the fungal isolates was assessed and compared to other reference data sets using Consensus Diversity Plots, a chemoinformatic tool recently developed. It was concluded that fungal secondary metabolites are cyclic systems with few ramifications and more diverse than the commercial libraries with natural products and semi-synthetic compounds. The fungal metabolites data set was one of the most structurally diverse, containing a large proportion of different and unique scaffolds not found in the other compound data sets including ChEMBL. Therefore, fungal metabolites offer a rich source of molecules suited for identifying diverse candidates for drug discovery.

We introduce a free platform for chemoinformatic-based diversity analysis and visualization of chemical space of user supplied data sets. Platform for Unified Molecular Analysis (PUMA) integrates metrics used to characterize compound databases including visualization of chemical space, scaffold content, and analysis of chemical diversity. The user's input is a file with SMILES, database names, and compound IDs. PUMA computes molecular properties of pharmaceutical relevance, Murcko scaffolds, and diversity analysis. The user can interactively navigate through the graphs and export image files and the raw data of the diversity calculations. The platform links two public online resources: Consensus Diversity Plots for the assessment of global diversity and Activity Landscape Plotter to analyze structure-activity relationships. Herein, we describe the functionalities of PUMA and exemplify its use through the analysis of compound databases of general interest. PUMA is freely accessible at the authors web-site https://www.difacquim.com/d-tools/ .

Background Measuring the structural diversity of compound databases is relevant in drug discovery and many other areas of chemistry. Since molecular diversity depends on molecular representation, comprehensive chemoinformatic analysis of the diversity of libraries uses multiple criteria. For instance, the diversity of the molecular libraries is typically evaluated employing molecular scaffolds, structural fingerprints, and physicochemical properties. However, the assessment with each criterion is analyzed independently and it is not straightforward to provide an evaluation of the “global diversity”. Results Herein the Consensus Diversity Plot (CDP) is proposed as a novel method to represent in low dimensions the diversity of chemical libraries considering simultaneously multiple molecular representations. We illustrate the application of CDPs to classify eight compound data sets and two subsets with different sizes and compositions using molecular scaffolds, structural fingerprints, and physicochemical properties. Conclusions CDPs are general data mining tools that represent in two-dimensions the global diversity of compound data sets using multiple metrics. These plots can be constructed using single or combined measures of diversity. An online version of the CDPs is freely available at: https://consensusdiversityplots-difacquim-unam.shinyapps.io/RscriptsCDPlots/.Graphical AbstractConsensus Diversity Plot is a novel data mining tool that represents in two-dimensions the global diversity of compound data sets using multiple metrics. Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0176-9) contains supplementary material, which is available to authorized users.