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Review
. 2024 Jan 5:16:1336004.
doi: 10.3389/fnmol.2023.1336004. eCollection 2023.

Targeting ion channels with ultra-large library screening for hit discovery

Kortney Melancon #  1   2 , Palina Pliushcheuskaya #  3 , Jens Meiler  1   2   3   4 , Georg Künze  3   4   5
Affiliations
Review

Targeting ion channels with ultra-large library screening for hit discovery

Kortney Melancon et al. Front Mol Neurosci. .

Abstract

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Keywords: chemical library; deep learning; drug design; ion channels; virtual screening.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Overview of the general VS workflow.
Figure 2
Figure 2
Number of unique structures of human ion channels released every year since 2011 (data were obtained from mpstruc database, source: mpstruc database: Available at: https://blanco.biomol.uci.edu/mpstruc/, accessed on 23 October 2023). Values on top of blue bars indicate the number of new human ion channel structures released every year.
Figure 3
Figure 3
Cryo-EM structure of wild-type human NaV1.7. (A) EM map of wild-type NaV1.7-β1-β2 complex. (B) EM maps of previously unresolved cytosolic regions. (C) Structure of NaV1.7-β1-β2 complex. The core domain is gray, previously unresolved regions are colored correspondingly to those in (B). VSD, voltage-sensing domain; NTD, amino-terminal domain, CTD, carboxy-terminal domain. Reprinted (adapted) from Huang et al. (2022a). Copyright 2022, Elsevier.
Figure 4
Figure 4
V-SYNTHES approach to modular screening of Enamine REAL Space. The flow chart from left to right provides a broad outline of the 4-step algorithm developed by Sadybekov et al. (2022).
Figure 5
Figure 5
Workflow of the DrugEx method for designing molecules with selectivity to A1 and A2A adenosine receptors but no affinity for hERG. (1) New molecules are sampled as SMILES based on the probability calculated by the RNN-based agent generator. (2) The SMILES are encoded into descriptors and their affinity for A1, A2A and hERG is predicted. (3) The predicted affinities are transformed into a single value as the reward for each molecule based on Pareto optimization. (4) For training the generative model, the SMILES and their rewards are sent back to the generator. Steps (1) to (4) are repeated until convergence of the training process is reached. Reproduced from Liu et al. (2021) under permission of Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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