Network measures for chemical library design.

In this overview, we examine recent developments in network approaches to drug design. A brief overview of networks is followed by a discussion of how chemical similarity networks and their properties address challenges in drug design. Multiple methods used to assess or enhance chemical diversity for early-stage drug discovery are discussed, as well as methods that can be used for drug repositioning and ligand polypharmacology.

Graphs and networks in chemical and biological informatics: past, present and future.

Chemical and biological network analysis has recently garnered intense interest from the perspective of drug design and discovery. While graph theoretic concepts have a long history in chemistry - predating quantum mechanics - and graphical measures of chemical structures date back to the 1970s, it is only recently with the advent of public repositories of information and availability of high-throughput assays and computational resources that network analysis of large-scale chemical networks, such as protein-protein interaction networks, has become possible. Drug design and discovery are undergoing a paradigm shift, from the notion of 'one target, one drug' to a much more nuanced view that relies on multiple sources of information: genomic, proteomic, metabolomic and so on. This holistic view of drug design is an incredibly daunting undertaking still very much in its infancy. Here, we focus on current developments in graph- and network-centric approaches in chemical and biological informatics, with particular reference to applications in the fields of SAR modeling and drug design. Key insights from the past suggest a path forward via visualization and fusion of multiple sources of chemical network data.

Predictive cheminformatics in drug discovery: statistical modeling for analysis of micro-array and gene expression data.

The vast amounts of chemical and biological data available through robotic high-throughput assays and micro-array technologies require computational techniques for visualization, analysis, and predictive -modeling. Predictive cheminformatics and bioinformatics employ statistical methods to mine this data for hidden correlations and to retrieve molecules or genes with desirable biological activity from large databases, for the purpose of drug development. While many statistical methods are commonly employed and widely accessible, their proper use involves due consideration to data representation and preprocessing, model validation and domain of applicability estimation, similarity assessment, the nature of the structure-activity landscape, and model interpretation. This chapter seeks to review these considerations in light of the current state of the art in statistical modeling and to summarize the best practices in predictive cheminformatics.

Exploration of the topology of chemical spaces with network measures.

Discontinuous changes in molecular structure (resulting from continuous transformations of molecular coordinates) lead to changes in chemical properties and biological activities that chemists attempt to describe through structure-activity or structure-property relationships (QSAR/QSPR). Such relationships are commonly envisioned in a continuous high-dimensional space of numerical descriptors, referred to as chemistry space. The choice of descriptors defining coordinates within chemistry space and the choice of similarity metrics thus influence the partitioning of this space into regions corresponding to local structural similarity. These are the regions (known as domains of applicability) most likely to be successfully modeled by a structure-activity relationship. In this work the network topology and scaling relationships of chemistry spaces are first investigated independent of a specific biological activity. Chemistry spaces studied include the ZINC data set, a qHTS PubChem bioassay, as well as the space of protein binding sites from the PDB. The characteristics of these networks are compared and contrasted with those of the bioassay SALI subnetwork, which maps discontinuities or cliffs in the structure-activity landscape. Mapping the locations of activity cliffs and comparing the global characteristics of SALI subnetworks with those of the underlying chemistry space networks generated using different representations, can guide the choice of a better representation. A higher local density of SALI edges with a particular representation indicates a more challenging structure-activity relationship using that fingerprint in that region of chemistry space.

PESDserv: a server for high-throughput comparison of protein binding site surfaces.

SUMMARY: Structure-based approaches complement ligand-based approaches for lead-discovery and cross-reactivity prediction. We present to the scientific community a web server for comparing the surface of a ligand bound site of a protein against a ligand bound site surface database of 106 796 sites. The web server implements the property encoded shape distributions (PESD) algorithm for surface comparison. A typical virtual screen takes 5 min to complete. The output provides a ranked list of sites (by site similarity), hyperlinked to the corresponding entries in the PDB and PDBeChem databases. AVAILABILITY: The server is freely accessible at http://reccr.chem.rpi.edu/Software/pesdserv/

Binding affinity prediction with property-encoded shape distribution signatures.

We report the use of the molecular signatures known as "property-encoded shape distributions" (PESD) together with standard support vector machine (SVM) techniques to produce validated models that can predict the binding affinity of a large number of protein ligand complexes. This "PESD-SVM" method uses PESD signatures that encode molecular shapes and property distributions on protein and ligand surfaces as features to build SVM models that require no subjective feature selection. A simple protocol was employed for tuning the SVM models during their development, and the results were compared to SFCscore, a regression-based method that was previously shown to perform better than 14 other scoring functions. Although the PESD-SVM method is based on only two surface property maps, the overall results were comparable. For most complexes with a dominant enthalpic contribution to binding (DeltaH/-TDeltaS > 3), a good correlation between true and predicted affinities was observed. Entropy and solvent were not considered in the present approach, and further improvement in accuracy would require accounting for these components rigorously.