This is a repository copy of Looking beyond the hype : applied AI and machine learning in translational medicine. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/151087/ Version: Published Version Article: Toh, T.S., Dondelinger, F. and Wang, D. (2019) Looking beyond the hype : applied AI and machine learning in translational medicine. EBioMedicine. ISSN 2352-3964 https://doi.org/10.1016/j.ebiom.2019.08.027 Reuse This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) licence. This licence only allows you to download this work and share it with others as long as you credit the authors, but you can’t change the article in any way or use it commercially. 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Toh a, Frank Dondelinger b, Dennis Wang c,d,⁎ a The Medical School, University of Sheffield, Sheffield, UK b Lancaster Medical School, Furness College, Lancaster University, Bailrigg, Lancaster, UK c NIHR Sheffield Biomedical Research Centre, University of Sheffield, Sheffield, UK d Department of Computer Science, University of Sheffield, Sheffield, UK article info abstract Article history: Big data problems are becoming more prevalent for laboratory scientists who look to make clinical impact. A Received 26 June 2019 large part of this is due to increased computing power, in parallel with new technologies for high quality data Received in revised form 30 July 2019 generation. Both new and old techniques of artificial intelligence (AI) and machine learning (ML) can now Accepted 13 August 2019 help increase the success of translational studies in three areas: drug discovery, imaging, and genomic medicine. Available online xxxx However, ML technologies do not come without their limitations and shortcomings. Current technical limitations Keywords: and other limitations including governance, reproducibility, and interpretation will be discussed in this article. Machine learning Overcoming these limitations will enable ML methods to be more powerful for discovery and reduce ambiguity Drug discovery within translational medicine, allowing data-informed decision-making to deliver the next generation of diag- Imaging nostics and therapeutics to patients quicker, at lowered costs, and at scale. Genomic medicine © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http:// Artificial intelligence creativecommons.org/licenses/by-nc-nd/4.0/). Translational medicine Contents 1. Introduction............................................................... 0 2. Drugdiscovery.............................................................. 0 2.1. Designingchemicalcompounds................................................... 0 2.2. Drugscreening.......................................................... 0 3. Imaging................................................................. 0 3.1. Cellmicroscopyandhistopathology................................................. 0 3.2. Definingrelationshipsbetweenmorphologyandgenomicfeatures................................... 0 4. Genomicmedicine............................................................ 0 4.1. Biomarkerdiscovery........................................................ 0 4.2. Integratingdifferentmodalitiesofdata................................................ 0 5. Datacollection,governance,reproducibility,andinterpretability....................................... 0 6. Outstandingquestions.......................................................... 0 7. Conclusions............................................................... 0 Searchstrategyandselectioncriteria...................................................... 0 Authorcontributions.............................................................. 0 Declarationofcompetinginterests....................................................... 0 Acknowledgements.............................................................. 0 AppendixA. Supplementarydata....................................................... 0 References.................................................................. 0 1. Introduction ⁎ fi fi Corresponding author at: NIHR Shef eld BRC, University of Shef eld, 385a Glossop Artificial intelligence (AI) and machine learning (ML) technologies Road, Sheffield S10 2HQ, UK. E-mail address: dennis.wang@sheffield.ac.uk (D. Wang). have begun to change how we deliver healthcare. These disruptive https://doi.org/10.1016/j.ebiom.2019.08.027 2352-3964/© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article as: T.S. Toh, F. Dondelinger and D. Wang, Looking beyond the hype: Applied AI and machine learning in translational medicine, EBioMedicine, https://doi.org/10.1016/j.ebiom.2019.08.027 2 T.S. Toh et al. / EBioMedicine xxx (2019) xxx Fig. 1. Process of AI/ML in translational medicine. A number of high-throughput assays generate data from many patient samples. Datasets are then structured into machine-readable format and potentially important variables are identified using an ML algorithm. The algorithm will learn relationships between the variables and may perform intelligent tasks such as grouping patients or predicting their outcomes. computational methodologies are able to transform the way clinicians estimated to be 11.83%, which shows the inherently risky nature of help patients in the clinic and also enhance the way scientists develop drug development [5]. new treatments and diagnostics in the laboratories [1–3]. Now that bio- After identifying a target, key steps of designing a suitable chemical medical scientists in academia and biotechnology industries frequently compound may be broken down into two processes, the retrosynthetic use high-throughput technologies such as parallelised sequencing, mi- process and the formulation of a well-motivated hypothesis for new croscopy imaging, and compound screening, there has been a rapid ex- lead compound generation (de novo design) [6–9]. Retrosynthesis in- pansion in the volume and quality of laboratory data being generated. volves planning the synthesis of small organic molecules where target Together with advancing techniques in ML, we are increasingly more molecules are transformed recurrently by a search tree ‘working back- capable of deriving biological insight from this “big data” to understand wards’ into simpler precursors until a set of known molecules are gained. the mechanisms of disease, identify new therapeutic strategies, and im- Each retrosynthetic step involves the selection of the most promising prove diagnostic tools for clinical application. transformations from many known transformations by chemists, but The term AI is often used these days to denote ML techniques, so it is there is no guarantee that the reaction will proceed as expected [9]. De worth defining both terms. Here, we adopt the US Food and Drug Ad- novodesigninvolves exploring compound libraries, estimated to contain ministration (FDA) definition, which describes AI as “the science and N1030 molecules. Due to the vast size of compound libraries, automated engineering of making intelligent machines”, while ML is “an artificial screening of selected compounds with the required properties and pos- intelligence technique that can be used to design and train software al- sibility of activity presents an opportunity to apply ML to navigate these gorithms to learn from and act on data” [4]. It follows that all ML tech- libraries [7,8]. Here, we review ML methods that help to design, synthe- niques are AI techniques, but not all AI techniques are ML techniques. sise, and prioritise new compounds before clinical testing. In this article, we will mainly be concerned with ML techniques, as these are most relevant in the context of translational medicine. Both 2.1. Designing chemical compounds simple and advanced ML methods may enhance the capability of trans- lational scientists who use them to develop new treatments and diag- Traditional computer-aided retrosynthesis is slow and does not pro- nostics for healthcare. Generally speaking, there are two main types of vide results of high quality. Advances in ML are shifting the generation ML approaches we will focus on; 1) unsupervised learning of data to of chemistry rules from hand-coded heuristics, to autonomous systems find previously unknown patterns or grouping labels for the patient that take advantage of a vast volume of available chemistry in assisting samples; and 2) supervised learning from data with labels in order to chemical synthesis. Chemists have been able to train deep neural net- make predictions on new samples (Fig. 1). We will describe specific works (DNNs) (Fig. 2) on all available reactions published in organic ML methods that have been heavily promoted or hyped and those chemistry to create a system that is thirty times quicker than traditional that