Machine learning applications in drug development

Réda, Clémence; Kaufmann, Emilie; Delahaye‐Duriez, Andrée

doi:10.1016/j.csbj.2019.12.006

Cited by 183 publications

(90 citation statements)

References 90 publications

(90 reference statements)

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“…Considering that these are typical applications of machine learning (i.e., classification and prediction), this is not surprising. However, machine learning can also play an important role in improving the treatment of rare diseases, and future studies could focus more on this aspect, for example by using machine learning to accelerate drug development [33].…”

Section: Discussionmentioning

confidence: 99%

The use of machine learning in rare diseases: a scoping review

Schaefer

Lehne

Schepers

et al. 2020

Orphanet J Rare Dis

138

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Background Emerging machine learning technologies are beginning to transform medicine and healthcare and could also improve the diagnosis and treatment of rare diseases. Currently, there are no systematic reviews that investigate, from a general perspective, how machine learning is used in a rare disease context. This scoping review aims to address this gap and explores the use of machine learning in rare diseases, investigating, for example, in which rare diseases machine learning is applied, which types of algorithms and input data are used or which medical applications (e.g., diagnosis, prognosis or treatment) are studied. Methods Using a complex search string including generic search terms and 381 individual disease names, studies from the past 10 years (2010–2019) that applied machine learning in a rare disease context were identified on PubMed. To systematically map the research activity, eligible studies were categorized along different dimensions (e.g., rare disease group, type of algorithm, input data), and the number of studies within these categories was analyzed. Results Two hundred eleven studies from 32 countries investigating 74 different rare diseases were identified. Diseases with a higher prevalence appeared more often in the studies than diseases with a lower prevalence. Moreover, some rare disease groups were investigated more frequently than to be expected (e.g., rare neurologic diseases and rare systemic or rheumatologic diseases), others less frequently (e.g., rare inborn errors of metabolism and rare skin diseases). Ensemble methods (36.0%), support vector machines (32.2%) and artificial neural networks (31.8%) were the algorithms most commonly applied in the studies. Only a small proportion of studies evaluated their algorithms on an external data set (11.8%) or against a human expert (2.4%). As input data, images (32.2%), demographic data (27.0%) and “omics” data (26.5%) were used most frequently. Most studies used machine learning for diagnosis (40.8%) or prognosis (38.4%) whereas studies aiming to improve treatment were relatively scarce (4.7%). Patient numbers in the studies were small, typically ranging from 20 to 99 (35.5%). Conclusion Our review provides an overview of the use of machine learning in rare diseases. Mapping the current research activity, it can guide future work and help to facilitate the successful application of machine learning in rare diseases.

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Section: Discussionmentioning

confidence: 99%

The use of machine learning in rare diseases: a scoping review

Schaefer

Lehne

Schepers

et al. 2020

Orphanet J Rare Dis

138

View full text Add to dashboard Cite

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“…Deep generation models, also known as AI imagination, can design novel therapeutic agents with possible desired activity [55]. These tools help reduce the cost and time of developing drugs, help in developing novel therapeutic agents, as well as predict possible off-label uses for some therapeutic agents [56]. Bayesian Machine Learning tools have been used to develop drugs against Ebola in in-vitro settings and the findings translated well to in-vivo settings as well [57].…”

Section: Treatment Developmentmentioning

confidence: 99%

Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review

et al. 2020

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The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data becomes available and is quicker in accomplishing these tasks. This review describes various techniques of machine learning that have been used in the past in the prediction, detection and management of infectious diseases, and how these tools are being brought into the battle against COVID-19. In addition, we also discuss their applications in various stages of the pandemic, the advantages, disadvantages and possible pit falls.

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“…Two significant steps are involved during any drug discovery, (i) drug target identification that are critical proteins involved in a particular metabolic or signaling pathways in a specific disease condition, and (ii) developing small molecules that interact with the targets. To speed-up the process, computer-aided methods are introduced for the automated drug discovery [35], which is fast and accurate. It involves steps like hit identification using virtual screening, hit-to-lead optimization of affinity, selectivity, and lead optimization of other pharmaceutical properties while maintaining affinity.…”

Section: Drug-target Databasesmentioning

confidence: 99%

<strong>Data Science in Unveiling COVID-19 Pathogenesis and Diagnosis: Evolutionary Origin to Drug Repurposing</strong>

Das¹,

Tradigo²,

Veltri³

et al. 2020

Preprint

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The outbreak of novel Coronavirus (SARS-COV-2 ) disease (COVID-19) in Wuhan has attracted worldwide attention. SARS-COV-2 known to share a similar clinical manifestation that includes various symptoms such as pneumonia, fever, breathing difficulty, and in particular, SARS-COV-2 also causes a severe in ammation state that leads to death. Consequently, massive and rapid research growth has been observed across the globe to elucidate the mechanisms of infections and disease progression in genotype and phenotype scale. Data Science is playing a pivotal role in in-silico analysis to draw hidden and novel insights about the SARS-COV-2 origin, pathogenesis, COVID-19 outbreak forecasting, medical diagnosis, and drug discovery. With the availability of multi-omics, radiological, biomolecular, and medical data urges to develop novel exploratory and predictive models or customise exiting learning models to t the current problem domain. The presence of many approaches generates the need for the systematic surveys to guide both data scientists and medical practitioners. We perform an elaborate study on the state-of-the-art data science method ologies in action to tackle the current pandemic scenario. We consider various active COVID-19 data analytics domains such as phylogeny analysis, SARS-COV-2 genome identication, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological analysis, and most importantly (existing) drug discovery. We highlight types of data, their generation pipeline, and the data science models in use. We believe that the current study will give a detailed sketch of the road map towards handling COVID-19 like situation by leveraging data science in the future. We summarise our review focusing on prime challenges and possible future research directions .

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Machine learning applications in drug development

Abstract: Graphical abstract

Cited by 183 publications

References 90 publications

The use of machine learning in rare diseases: a scoping review

The use of machine learning in rare diseases: a scoping review

Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review

<strong>Data Science in Unveiling COVID-19 Pathogenesis and Diagnosis: Evolutionary Origin to Drug Repurposing</strong>

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