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Computational Approaches for De Novo Drug Design: Past, Present, and Future

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Artificial Neural Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2190))

Abstract

Drug discovery is time- and resource-consuming. To this end, computational approaches that are applied in de novo drug design play an important role to improve the efficiency and decrease costs to develop novel drugs. Over several decades, a variety of methods have been proposed and applied in practice. Traditionally, drug design problems are always taken as combinational optimization in discrete chemical space. Hence optimization methods were exploited to search for new drug molecules to meet multiple objectives. With the accumulation of data and the development of machine learning methods, computational drug design methods have gradually shifted to a new paradigm. There has been particular interest in the potential application of deep learning methods to drug design. In this chapter, we will give a brief description of these two different de novo methods, compare their application scopes and discuss their possible development in the future.

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Acknowledgments

X.L. thanks the Chinese Scholarship Council (CSC) for funding.

Competing interests: The authors declare that they have no competing interests.

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Authors and Affiliations

  1. Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden, The Netherlands

    Xuhan Liu, Adriaan P. IJzerman & Gerard J. P. van Westen

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  1. Xuhan Liu
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  2. Adriaan P. IJzerman
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  3. Gerard J. P. van Westen
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Correspondence to Gerard J. P. van Westen .

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  1. Chemistry, Oxford University, Oxford, UK

    Hugh Cartwright

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Liu, X., IJzerman, A.P., van Westen, G.J.P. (2021). Computational Approaches for De Novo Drug Design: Past, Present, and Future. In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 2190. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0826-5_6

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  • DOI: https://doi.org/10.1007/978-1-0716-0826-5_6

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