Chemical space as a source for new drugs

By Jean-Louis Reymond, Ruud Van Deursen, Lorenz C. Blum, Lars Ruddigkeit. 2010

319 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy

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Citations in the corpus

TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction (2022, background): “On the other hand, the estimated chemical space of drug is 10(60) and estimated number of unique proteins in human body is at least 20, 000, making the number of possible protein-ligand complex far exceeding the number of experimentally solved structures [5, 6].”
Neural Networks for the Prediction of Organic Chemistry Reactions (2016, unknown): “14,15 Another group attempted to grasp the diversity of reactions by creating an algorithm that automatically searches for reaction mechanisms using atom mapping and substructure matching.”
The enumeration of chemical space (2012, methodology): “32 , 146 , 147 Chemical spaces have been constructed by assigning dimensions to descriptors such as eigenvalues of matrices constructed from primary descriptors, 145 , 148 , 149 or more directly to descriptors of molecular structure and calculated physicochemical properties, 150 , 151 as well as by…”
Integrating virtual screening and combinatorial chemistry for accelerated drug discovery. (2011, background): “The drug discovery process is continuously evolving and a broad diversity of rational approaches is currently being pursued [1, 2].”
Artificial intelligence in drug development: present status and future prospects. (2019, background): “Chemical space provides the stage for identifying novel and high-quality molecules because it is possible to computationally enumerate the probable organic molecules [47].”
Visualisation and subsets of the chemical universe database GDB-13 for virtual screening (2011, background): “GDB-13 exceeds the number of known molecules of similar size by several orders of magnitude and represents a vast and mostly unexploited reservoir for innovation [18].”
All SMILES Variational Autoencoder (2019, background): “Even within this discrete space, almost all possible combinations of atoms and bonds do not form chemically stable molecules, and so must be excluded from the optimization domain, yet there remain as many as 10 60 small molecules to consider [51].”