Chemical space and biology

By Christopher M. Dobson. Nature, 2004

917 citations recorded by the source corpus.

Chemical space — which encompasses all possible small organic molecules, including those present in biological systems — is vast. So vast, in fact, that so far only a tiny fraction of it has been explored. Nevertheless, these explorations have greatly enhanced our understanding of biology, and have led to the development of many of today's drugs. The discovery of new bioactive molecules, facilitated by a deeper understanding of the nature of the regions of chemical space that are relevant to biology, will advance our knowledge of biological processes and lead to new strategies to treat disease.

Subfields: Computational Theory and Mathematics, Organic Chemistry, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/330f1bc84d15dd54a95e9e31f2bf15e107ddfa14

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In Computational Theory and Mathematics

In Organic Chemistry

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Protein drug stability: a formulation challenge (2005, background): “On the other hand, the decoding of the human genomic and proteomic maps is anticipated to lead to discoveries in molecular medicine that could revitalize the development of small-molecular synthetic drug”
Collaborative matrix factorization with multiple similarities for predicting drug-target interactions (2013, background): “Known drug-target interactions are however limited to a small number [8], say only less than 7,000 compounds in fact having target protein information in PubChem [22], one of the largest chemical compound databases with currently around 35 million entries.”
Machine Learning Methods in Drug Discovery (2020, background): “The chemical area is a relative set of descriptors, consisting of thousands of compounds within a frame with boundaries generated by ML algorithms [64,65].”
Artificial intelligence in drug discovery: recent advances and future perspectives (2021, background): “…entities with desired pharmacological properties from scratch [109], can be considered as one of the most challenging computer-assisted tasks in drug discovery, due to the cardinality of the chemical space of drug-like molecules (estimated to range in the order of 10 60 –10 100 ) [110,111].”
A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data (2012, background): “Actually, it is estimated that the set of all possible small molecules has already consisted of more than 10(60) compounds [4], which creates incredibly great difficulties in comprehensive understanding of the interface between chemical space and biological systems [5].”
Virtual screening strategies in drug discovery: a critical review. (2013, background): “the number of different compounds that could in theory be synthesised [5] is estimated to be in the region of 10 [6, 7].”
Graph kernels for chemical informatics (2005, background): “We hope that developing efficient kernels and other machine learning methods for molecular structures will foster over time greater synergies between bio- and chemical informatics [Dobson, 2004] and help us better understand chemical space.”
Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework (2010, methodology): “The proposed method consists of two steps: (i) prediction of pharmacological effects from chemical structures of given compounds and (ii) inference of unknown drug–target interactions based on the pharmacological effect similarity in the framework of supervised bipartite graph inference.”