SARConnect: A Tool to Interrogate the Connectivity Between Proteins, Chemical Structures and Activity Data

Reaching for the bright StARs in chemical space

Visualization of activity data in chemical space is common in drug discovery. Navigating the space in a systematic manner is not trivial, given its size and huge coverage. To this end, methods for data visualization have been developed charting biological activity into chemical space. Herein, we review the progress in different visualization approaches to explore the chemical space aiming at reaching insightful structure-activity relationships (SARs) in the chemical space. We discuss recent methods including consensus diversity plots, ChemMaps, and constellation plots. Several of the methods we review can be extended to analyze other properties of interest in medicinal chemistry, such as structure-toxicity relationships, and can be adapted to postprocess results of virtual screening (VS) of large compound libraries.

Chemical structure and correlation analysis of HIV-1 NNRT and NRT inhibitors and database-curated, published inhibition constants with chemical structure in diverse datasets

Human immunodeficiency virus (HIV-1) reverse transcriptase is a major target for designing anti-HIV drugs. Developed inhibitors are divided into non-nucleoside analog reverse-transcriptase inhibitors (NNRTIs) and nucleoside analog reverse-transcriptase inhibitors (NRTIs) depending on their mechanism. Given that many inhibitors have been studied and for many of them binding affinity constants have been calculated, it is beneficial to analyze the chemical landscape of these families of inhibitors and correlate these inhibition constants with molecular structure descriptors. For this, the HIV-1 RT data was retrieved from the ChEMBL database, carefully curated, and original literature verified, grouped into NRTIs and NNRTIs, analyzed using a hierarchical scaffold classification method and modelled with best multi-linear regression approach. Analysis of the HIV-1 NNRTIs subset results in ten different common structural parent types of oxazepanone, piperazinone, pyrazine, oxazinanone, diazinanone, pyridine, pyrrole, diazepanone, thiazole, and triazine. The same analysis for HIV-1 NRTIs groups structures into four different parent types of uracil, pyrimide, pyrimidione, and imidazole. Each scaffold tree corresponding to the parent types has been carefully analyzed and examined, and changes in chemical structure favorable to potency and stability are highlighted. For both subsets, descriptive and predictive QSAR models are derived, discussed and externally validated, revealing general trends in relationships between molecular structure and binding affinity constants in structurally diverse datasets. Data and QSAR models are available at the QsarDB repository (http://dx.doi.org/10.15152/QDB.202).

Maximizing computational tools for successful drug discovery

Drug discovery is an iterative cycle of identifying promising hits followed by lead optimization via bioisosteric replacements. In the search for compounds affording good bioactivity, equal importance should also be placed on achieving those with favorable pharmacokinetic properties. Thus, the balance and realization of both key properties is an intricate problem that requires great caution. In this editorial, the authors explore the available computational tools in the context of the extant of big data that has borne out via advents of the Omics revolution. As such, the selection of appropriate computational tools for analyzing the vast number of chemical libraries, target proteins and interactomes is the first step toward maximizing the chance for success. However, in order to realize this, it is also necessary to have a solid foundation on the big concepts of drug discovery as well as knowing which tools are available in order to give drug discovery scientists the best opportunity.

Comparing the Chemical Structure and Protein Content of ChEMBL, DrugBank, Human Metabolome Database and the Therapeutic Target Database

ChEMBL, DrugBank, Human Metabolome Database and the Therapeutic Target Database are resources of curated chemistry-to-protein relationships widely used in the chemogenomic arena. In this work we have extended an earlier analysis (PMID 22821596) by comparing chemistry and protein target content between 2010 and 2013. For the former, details are presented for overlaps and differences, statistics of stereochemistry as well as stereo representation and MW profiles between the four databases. For 2013 our results indicate quality improvements, major expansion, increased achiral structures and changes in MW distributions. An orthogonal comparison of chemical content with different sources inside PubChem highlights further interpretable differences. Expansion of protein content by UniProt IDs is also recorded for 2013 and Gene Ontology comparisons for human-only sets indicate differences. These emphasise the expanding complementarity of chemistry-to-protein relationships between sources, although different criteria are used for their capture.

Practice and Challenges of Building a Semantic Framework for Chemogenomics Research

Effective discovery of new drugs for complex diseases demands an integrative analysis of big data aggregated from diverse sources in chemical and biological domains, to help better understand the mechanism of drug actions and to quickly translate discovery to clinical applications. Conventional approaches are confronting critical challenges in the integration of those huge heterogeneous datasets and the rapid transformation from data to knowledge. Semantic technologies aimed at facilitating the building of a common framework that allows data sharing and utilization across applications and domains in the web, have been developed quickly and have been exhibiting a broad impact in life science. Chemogenomics serves as a bridge to connect various chemical and biological data, thus building a semantic framework for chemogenomics research could not only facilitate the development of this field but also advance the intersection among other domains. During the last few years, such framework has been developed and applied in addressing real problems. In the review, we will describe the major techniques needed to build a semantic framework, and will discuss the challenges of having such framework making a broader impact.