Approaches for enhancing the analysis of chemical space for drug discovery

Abstract

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. Herein, 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. It is concluded that 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.

Towards uncoding hepatotoxicity of approved drugs through navigation of multiverse and consensus chemical spaces

“…For prospective studies, it will be necessary to assess multiple methods to fuse data [31] and use other high-dimensional reduction methods such as principal component analysis and support vector machine, etc. [32].…”

Quantum similarity and QSPR in Euclidean-, and Minkowskian–Banach spaces

“…Therefore, every molecular vector can be considered as the condensation of another Euclidian-Banach space of arbitrary dimensions. This point of view has never been discussed in the theory of classical QSPR at any level, as far as the author is aware; see for example a modern assorted sample of references [68][69][70][71][72][73][74][75][76][77][78].…”

Chemical Multiverse: An Expanded View of Chemical Space

“…As reviewed in Table 2 , several definitions conceptualize the chemical space as a chemical descriptor vector space set by the numerical vector D encoding property or molecular structure aspects as elements of the descriptor vector D. [35] Based on this notion, Figure 1 shows the chemical space concept like “M‐multidimensional cartesian space,” aka a “chemical space table.” Rows represent the number of (n) molecules, and the columns (M) are the number of descriptors or features that encode each molecule. The length of descriptor sets corresponds to the number of dimensions defining the chemical space itself.…”