What is the “biological space” and why is this space so important for all researchers interested in developing novel drugs? In the following, I will first establish a definition of the biological space and then highlight its use in computationally developing novel drug compounds and as a starting point in the experimental exploration of biological systems.

While chemical space has been defined as the entirety of all possible chemical compounds which could ever exist, the definition of biological space is less clear. In the following, I define biological space as the area(s) of chemical space that possess biologically active (”bioactive”) compounds for a specific target or target class¹. As such, they can modulate a given biological system and subsequently influence disease development and progression. In literature, this space has also been called “biologically relevant chemical space”².

Only a small percentage of the vast chemical space has been estimated to be biologically active and is thus relevant for drug development, as randomly searching bioactive compounds in chemical space with no prior information resembles the search for “the needle in a haystack”. Hence, it should come as no surprise that bioactive molecules are often used as a starting point in in-silico explorations of biological space.
The plethora of in-silico methods for this task includes similarity and pharmacophore searching methods^3-6 for novel compounds, scaffold-hopping approaches to derive novel chemotypes^7-8 or the development of quantitative structure-activity relationships (QSAR)^9-10 to explore the interplay between the 3D chemical structure and its biological activity towards a specific target.

The biological space is comprised of small molecules which are active on specific targets. If researchers want to explore the role the role of targets in a given biological system experimentally, they can use small molecules which are potent and selective towards a specific target (thus confided to a particular area in chemical space)^11-12.
Due to their high selectivity ( f.e. a greater than 30-fold selectivity towards proteins of the same family¹²), these so-called “tool compounds” can help establish the biological tractability – the relationship between the target and a given phenotype – and its clinical tractability – the availability of biomarkers – of a target¹¹. They are thus highly complementary to methods such as RNAi, CRISPR¹² and knock-out animals¹¹. Consequently, tool compounds are used in drug target validation and the information they provide on the biological system can increase the probability of a successful drug ¹¹. Most importantly, tool compounds are particularly important to annotate targets in currently unexplored biological systems and thus important for novel drug development¹³.

1. Sophie Petit-Zeman, http://www.nature.com/horizon/chemicalspace/background/figs/explore_b1.html, accessed on 03.07.2016.
2. Koch, M. A. et al. Charting biologically relevant chemical space: a structural classification of natural products (SCONP). Proceedings of the National Academy of Sciences of the United States of America 102, 17272–17277 (2005).
3. Stumpfe, D. & Bajorath, J. Similarity searching. Wiley Interdisciplinary Reviews: Computational Molecular Science 1, 260–282 (2011).
4. Bender, A. et al. How Similar Are Similarity Searching Methods? A Principal Component Analysis of Molecular Descriptor Space. Journal of Chemical Information and Modeling 49, 108–119 (2009).
5. Ai, G. et al. A combination of 2D similarity search, pharmacophore, and molecular docking techniques for the identification of vascular endothelial growth factor receptor-2 inhibitors: Anti-Cancer Drugs 26, 399–409 (2015).
6. Willett, P., Barnard, J. M. & Downs, G. M. Chemical Similarity Searching. Journal of Chemical Information and Computer Sciences 38, 983–996 (1998)
7. Sun, H., Tawa, G. & Wallqvist, A. Classification of scaffold-hopping approaches. Drug Discovery Today 17, 310–324 (2012).
8. Hu, Y., Stumpfe, D. & Bajorath, J. Recent Advances in Scaffold Hopping: Miniperspective. Journal of Medicinal Chemistry 60, 1238–1246 (2017)
9. Cruz-Monteagudo, M. et al. Activity cliffs in drug discovery: Dr Jekyll or Mr Hyde? Drug Discovery Today 19, 1069–1080 (2014).
10. Bradley, A. R., Wall, I. D., Green, D. V. S., Deane, C. M. & Marsden, B. D. OOMMPPAA: A Tool To Aid Directed Synthesis by the Combined Analysis of Activity and Structural Data. Journal of Chemical Information and Modeling 54, 2636–2646 (2014).
11. Garbaccio, R. & Parmee, E. The Impact of Chemical Probes in Drug Discovery: A Pharmaceutical Industry Perspective. Cell Chemical Biology 23, 10–17 (2016).
12. Arrowsmith, C. H. et al. The promise and peril of chemical probes. Nature Chemical Biology 11, 536–541 (2015).
13. Fedorov, O., Müller, S. & Knapp, S. The (un) targeted cancer kinome. Nature chemical biology 6, 166–169 (2010).