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Artificial Intelligence in Drug Design pp 125–151Cite as

Deep Learning and Computational Chemistry

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Part of the Methods in Molecular Biology book series (MIMB,volume 2390)

Abstract

Within the context of the latest resurgence in the application of artificial intelligence approaches, deep learning has undergone a renaissance over recent years. These methods have been applied to a number of problems in computational chemistry. Compared to other machine learning approaches, the practical performance advantages of deep neural networks are often unclear. However, deep learning does appear to offer a number of other advantages such as the facile incorporation of multitask learning and the enhancement of generative modeling. The high complexity of contemporary network architectures represents a potentially significant barrier to their future adoption due to the costs of training such models and challenges in interpreting their predictions. When combined with the relative paucity of very large datasets, it is interesting to reflect on whether deep learning is likely to have the kind of transformational impact on computational chemistry that it is commonly held to have had in other domains such as image recognition.

Key words

  • AI
  • Artificial intelligence
  • Computational chemistry
  • Deep learning
  • Explainable AI
  • Generative models
  • Interpretability
  • Machine learning
  • Quantitative structure-activity relationships
  • QSAR
  • Virtual screening

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Authors and Affiliations

  1. Evotec (UK) Ltd., Abingdon, Oxfordshire, UK

    Tim James & Dimitar Hristozov

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  1. Tim James
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  2. Dimitar Hristozov
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Correspondence to Tim James .

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  1. Computational Drug Discovery, Evotec (UK) Ltd., Abingdon, Oxfordshire, UK

    Ph.D. Alexander Heifetz

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James, T., Hristozov, D. (2022). Deep Learning and Computational Chemistry. In: Heifetz, A. (eds) Artificial Intelligence in Drug Design. Methods in Molecular Biology, vol 2390. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1787-8_5

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  • DOI: https://doi.org/10.1007/978-1-0716-1787-8_5

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