Drug discovery with explainable artificial intelligence

By José Jiménez-Luna, Francesca Grisoni, Gisbert Schneider. Nature Machine Intelligence, 2020

857 citations recorded by the source corpus.

Deep learning bears promise for drug discovery, including advanced image analysis, prediction of molecular structure and function, and automated generation of innovative chemical entities with bespoke properties. Despite the growing number of successful prospective applications, the underlying mathematical models often remain elusive to interpretation by the human mind. There is a demand for ‘explainable’ deep learning methods to address the need for a new narrative of the machine language of the molecular sciences. This Review summarizes the most prominent algorithmic concepts of explainable artificial intelligence, and forecasts future opportunities, potential applications as well as several remaining challenges. We also hope it encourages additional efforts towards the development and acceptance of explainable artificial intelligence techniques. Drug discovery has recently profited greatly from the use of deep learning models. However, these models can be notoriously hard to interpret. In this Review, Jiménez-Luna and colleagues summarize recent approaches to use explainable artificial intelligence techniques in drug discovery.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/d3a857a4bccf9a23b2d1e523c89456de728403b3

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Citations in the corpus

Artificial Intelligence For Natural Product Drug Discovery (2023, background): “Furthermore, explainable AI methods have been shown to improve interpretability of such deep learning models 104,105 ; for example, in the assessment of preclinical relevance 106 and for pharmacophore and toxicophore identification 107,108 .”
Computational approaches streamlining drug discovery (2023, background): “There have also been attempts to develop tools to make AI ‘explainable’, that is, able to formulate some general trends in the data, specifically in the drug discovery applications 93 .”
Artificial intelligence in drug discovery: recent advances and future perspectives (2021, methodology): “The newfound interest in explainable AI [57], with methodologies such as feature attribution [190], instance-based molecular counterfactual explanation [191], and uncertainty estimation [64], will increase the acceptance of AI-supported drug discovery.”
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design (2022, background): “Although many recent efforts have been put to review the literature of machine learning models for molecule design, most of them only focus on part of the literature in terms of generative methods and tasks [8], [9], [10], [11], [12], [13], [14].”
Graph Representation Learning in Biomedicine and Healthcare (2021, positive): “Biomedically focused review articles have surveyed the use of GNNs for molecular generation 33,34 , single-cell biology 35 , drug discovery and drug repurposing 36–40 , and histopathology 41 .”
Discovery of a structural class of antibiotics with explainable deep learning (2023, positive): “We anticipate that future work will build on this and similar approaches 45,46 to further analyse and understand the predictions generated by deep learning models, for instance by using methods centred on perturbing model inputs 47 for additional tests of explainability, as well as perturbing…”
Geometric deep learning on molecular representations (2021, methodology): “, via XAI [85]) whenever possible and transparently communicate with domain experts.”
Artificial intelligence in drug development (2025, positive): “Computer-aided synthesis planning (CASP) and automatic synthesis of organic compounds can help alleviate the burden of repetitive laborious tasks for chemists, enabling them to engage in more innovative works 137,138 .”
Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, background): “However, the introduction of AI has given a broader scope to develop new approaches that can improve and optimize drug discovery [442].”