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In Silico Structure Generation: Recent Developments, Applications, and Challenges

Edited by José L. Medina-Franco, Emma Schymanski, Christoph Steinbeck

Chemical structures are at the core of the research in chemistry. In several applications, the chemical structures available in the physically exemplified chemical space are used. This includes virtual and “on-demand” libraries populated by chemical structures already constructed but not synthesized yet. However, it is well-known that the chemical space is huge and there is an increasing need to automatically generate novel chemical structures. Such need is evident in areas such as drug discovery, metabolomics, and planned organic synthesis.

The main objective of this special collection in the Journal of Cheminformatics is to show recent advances, applications, challenges in the enumeration of chemical structures: from the design to the analysis and use of either small, focused data sets, to large compound libraries. Analysis and handling of the newly constructed chemical structures include the storage, mining, integration of the constructed structures with other existing data sets, and curation.

  1. Rational drug design often starts from specific scaffolds to which side chains/substituents are added or modified due to the large drug-like chemical space available to search for novel drug-like molecules. Wi...

    Authors: Xuhan Liu, Kai Ye, Herman W. T. van Vlijmen, Adriaan P. IJzerman and Gerard J. P. van Westen
    Citation: Journal of Cheminformatics 2023 15:24
  2. In polypharmacology drugs are required to bind to multiple specific targets, for example to enhance efficacy or to reduce resistance formation. Although deep learning has achieved a breakthrough in de novo des...

    Authors: Xuhan Liu, Kai Ye, Herman W. T. van Vlijmen, Michael T. M. Emmerich, Adriaan P. IJzerman and Gerard J. P. van Westen
    Citation: Journal of Cheminformatics 2021 13:85
  3. The generation of constitutional isomer chemical spaces has been a subject of cheminformatics since the early 1960s, with applications in structure elucidation and elsewhere. In order to perform such a generat...

    Authors: Mehmet Aziz Yirik, Maria Sorokina and Christoph Steinbeck
    Citation: Journal of Cheminformatics 2021 13:48
  4. Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecul...

    Authors: Morgan Thomas, Robert T. Smith, Noel M. O’Boyle, Chris de Graaf and Andreas Bender
    Citation: Journal of Cheminformatics 2021 13:39
  5. In drug discovery, rapid and accurate prediction of protein–ligand binding affinities is a pivotal task for lead optimization with acceptable on-target potency as well as pharmacological efficacy. Furthermore,...

    Authors: Surendra Kumar and Mi-hyun Kim
    Citation: Journal of Cheminformatics 2021 13:28
  6. Enhanced/prolonged cAMP signalling has been suggested as a suppressor of cancer proliferation. Interestingly, two key modulators that elevate cAMP, the A2A receptor (A2AR) and phosphodiesterase 10A (PDE10A), are ...

    Authors: Leen Kalash, Ian Winfield, Dewi Safitri, Marcel Bermudez, Sabrina Carvalho, Robert Glen, Graham Ladds and Andreas Bender
    Citation: Journal of Cheminformatics 2021 13:17
  7. The process of drug discovery involves a search over the space of all possible chemical compounds. Generative Adversarial Networks (GANs) provide a valuable tool towards exploring chemical space and optimizing...

    Authors: Andrew E. Blanchard, Christopher Stanley and Debsindhu Bhowmik
    Citation: Journal of Cheminformatics 2021 13:14
  8. Virtual compound libraries are increasingly being used in computer-assisted drug discovery applications and have led to numerous successful cases. This paper aims to examine the fundamental concepts of library...

    Authors: Fernanda I. Saldívar-González, C. Sebastian Huerta-García and José L. Medina-Franco
    Citation: Journal of Cheminformatics 2020 12:64