Consistent Cell-selective Analog Series as Constellation Luminaries in Chemical Space

Approaches for enhancing the analysis of chemical space for drug discovery

INTRODUCTION

Chemical space is a powerful, general, and practical conceptual framework in drug discovery and other areas in chemistry that addresses the diversity of molecules and it has various applications. Moreover, chemical space is a cornerstone of chemoinformatics as a scientific discipline. In response to the increase in the set of chemical compounds in databases, generators of chemical structures, and tools to calculate molecular descriptors, novel approaches to generate visual representations of chemical space in low dimensions are emerging and evolving. Such approaches include a wide range of commercial and free applications, software, and open-source methods.

AREAS COVERED

The current state of chemical space in drug design and discovery is reviewed. The topics discussed herein include advances for efficient navigation in chemical space, the use of this concept in assessing the diversity of different data sets, exploring structure-property/activity relationships for one or multiple endpoints, and compound library design. Recent advances in methodologies for generating visual representations of chemical space have been highlighted, thereby emphasizing open-source methods.

EXPERT OPINION

Quantitative and qualitative generation and analysis of chemical space require novel approaches for handling the increasing number of molecules and their information available in chemical databases (including emerging ultra-large libraries). In addition, it is of utmost importance to note that chemical space is a conceptual framework that goes beyond visual representation in low dimensions. However, the graphical representation of chemical space has several practical applications in drug discovery and beyond.

Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications

Analogue series play a key role in drug discovery. They arise naturally in lead optimization efforts where analogues are explored based on one or a few core structures. However, it is much harder to accurately identify and extract pairs or series of analogue molecules in large compound databases with no predefined core structures. This methodological review outlines the most common and recent methodological developments to automatically identify analogue series in large libraries. Initial approaches focused on using predefined rules to extract scaffold structures, such as the popular Bemis-Murcko scaffold. Later on, the matched molecular pair concept led to efficient algorithms to identify similar compounds sharing a common core structure by exploring many putative scaffolds for each compound. Further developments of these ideas yielded, on the one hand, approaches for hierarchical scaffold decomposition and, on the other hand, algorithms for the extraction of analogue series based on single-site modifications (so-called matched molecular series) by exploring potential scaffold structures based on systematic molecule fragmentation. Eventually, further development of these approaches resulted in methods for extracting analogue series defined by a single core structure with several substitution sites that allow convenient representations, such as R-group tables. These methods enable the efficient analysis of large data sets with hundreds of thousands or even millions of compounds and have spawned many related methodological developments.

Progress on open chemoinformatic tools for expanding and exploring the chemical space

The concept of chemical space is a cornerstone in chemoinformatics, and it has broad conceptual and practical applicability in many areas of chemistry, including drug design and discovery. One of the most considerable impacts is in the study of structure-property relationships where the property can be a biological activity or any other characteristic of interest to a particular chemistry discipline. The chemical space is highly dependent on the molecular representation that is also a cornerstone concept in computational chemistry. Herein, we discuss the recent progress on chemoinformatic tools developed to expand and characterize the chemical space of compound data sets using different types of molecular representations, generate visual representations of such spaces, and explore structure-property relationships in the context of chemical spaces. We emphasize the development of methods and freely available tools focusing on drug discovery applications. We also comment on the general advantages and shortcomings of using freely available and easy-to-use tools and discuss the value of using such open resources for research, education, and scientific dissemination.

DiaNat-DB: a molecular database of antidiabetic compounds from medicinal plants

Natural products are an invaluable source of molecules with a large variety of biological activities. Interest in natural products in drug discovery is documented in an increasing number of publications of bioactive secondary metabolites. Among those, medicinal plants are one of the most studied for this endeavor. An ever thriving area of opportunity within the field concerns the discovery of antidiabetic natural products. As a result, a vast amount of secondary metabolites are isolated from medicinal plants used against diabetes mellitus but whose information has not been organized systematically yet. Several research articles enumerate antidiabetic compounds, but the lack of a chemical database for antidiabetic metabolites limits their application in drug development. In this work, we present DiaNat-DB, a comprehensive collection of 336 molecules from medicinal plants reported to have in vitro or in vivo antidiabetic activity. We also discuss a chemoinformatic analysis of DiaNat-DB to compare antidiabetic drugs and natural product databases. To further explore the antidiabetic chemical space based on DiaNat compounds, we searched for analogs in ZINC15, an extensive database listing commercially available compounds. This work will help future analyses, design, and development of new antidiabetic drugs. DiaNat-DB and its ZINC15 analogs are freely available at http://rdu.iquimica.unam.mx/handle/20.500.12214/1186.

Medicinal plants are widely used against diabetes mellitus. Here, we provide DiaNat-DB, the first publicly available database of secondary metabolites from medicinal plants, chemically characterized, and integrated with antidiabetic activity.