Technological advances and practical applications of the chemical space concept in drug discovery, natural product research, and other research areas have attracted the scientific community´s attention. The large-and ultra-large chemical spaces are associated not only with the significant increase in the number of compounds that can potentially be made and exist but also with the increasing number of experimental and calculated descriptors that are emerging that encode the molecular structure and/or property aspects of the molecules. Due to the importance and continued evolution of compound libraries, herein, we discuss definitions proposed in the literature for chemical space. We also introduce the concept of chemical multiverse as an alternative view of the chemical space and discuss it considering a related idea: consensus chemical space.

Modification of the tubulin‐microtubule (Tub‐Mts) system has generated effective strategies for developing different treatments for cancer. A huge amount of clinical data about inhibitors of the tubulin‐microtubule system have supported and validated the studies on this pharmacological target. However, many tubulin‐microtubule inhibitors have been developed from representative and common scaffolds that cover a small region of the chemical space with limited structural innovation. The main goal of this study is to develop the first consensus virtual screening protocol for natural products (ligand‐ and structure‐based drug design methods) tuned for the identification of new potential inhibitors of the Tub‐Mts system. A combined strategy that involves molecular similarity, molecular docking, pharmacophore modeling, and in silico ADMET prediction has been employed to prioritize the selections of potential inhibitors of the Tub‐Mts system. Five compounds were selected and further studied using molecular dynamics and binding energy predictions to characterize their possible binding mechanisms. Their structures correspond to 5‐[2‐(4‐hydroxy‐3‐methoxyphenyl) ethyl]‐2,3‐dimethoxyphenol (1), 9,10‐dihydro‐3,4‐dimethoxy‐2,7‐phenanthrenediol (2), 2‐(3,4‐dimethoxyphenyl)‐5,7‐dihydroxy‐6‐methoxy‐4H‐1‐benzopyran‐4‐one (3), 13,14‐epoxyparvifoline‐4’,5’,6’‐trimethoxybenzoate (4), and phenylmethyl 6‐hydroxy‐2,3‐dimethoxybenzoate (5). Compounds 1–3 have been associated with literature reports that confirm their activity against several cancer cell lines, thus supporting the utility of this protocol.

Understanding structure-activity landscapes is essential in drug discovery.Similarly, it has been shown that the presence of activity cliffs in compound data sets can have a substantial impact not only on the design progress but also can influence the predictive ability of machine learning models. With the continued expansion of the chemical space and the currently available large and ultra-large libraries, it is imperative to implement efficient tools to analyze the activity landscape of compound data sets rapidly. The goal of this study is to show the applicability of the n-ary indices to quantify the structure-activity landscapes of large compound data sets using different types of structural representation rapidly and efficiently. We also discuss how a recently introduced medoid algorithm provides the foundation to finding optimum correlations between similarity measures and structure-activity rankings. The applicability of the n-ary indices and the medoid algorithm is shown by analyzing the activity landscape of 10 compound data sets with pharmaceutical relevance using three fingerprints of different designs, 16 extended similarity indices, and 11 coincidence thresholds.

Visualization of the chemical space is useful in many aspects of chemistry including compound library design, diversity analysis, and exploring structure-property relationships, to name a few. Examples of notable research areas where visualization of chemical space has strong applications are drug discovery and natural product research. However, the sheer volume of even comparatively small sub-sections of chemical space implies that we need to use approximations at the time of navigating through chemical space. ChemMaps is a visualization methodology that approximates the distribution of compounds in large datasets based on the selection of satellite compounds that yield a similar mapping of the whole dataset when principal component analysis on similarity matrix was performed. Here, we show how the recently proposed extended similarity indices can help to find regions that are relevant to sample satellites and reduce the amount of high dimensional data needed to describe a library’s chemical space.