Rethinking drug design in the artificial intelligence era

Schneider, Petra; Walters, W. Patrick; Plowright, Alleyn T.; Sieroka, Norman; Listgarten, Jennifer; Goodnow, Robert A.; Fisher, Jasmin; Jansen, Johanna M.; Duca, José S.; Rush, Thomas S.; Zentgraf, Matthias; Hill, John E.; Krutoholow, Elizabeth; Köhler, Matthias; Blaney, Jeff; Funatsu, Kimito; Luebkemann, Chris

doi:10.1038/s41573-019-0050-3

Cited by 530 publications

(396 citation statements)

References 170 publications

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“…Holzinger et al [2019] discuss the difference between explainability and causality for medical applications, and the necessity of a person to be involved. For the successful application of ML for drug design, Schneider et al [2019] identify five 'grand challenges': obtaining appropriate datasets, generating new hypotheses, optimizing in a multi-objective manner, reducing cycle times, and changing the research culture and mindset. These underlying themes should be valid for many scientific endeavours.…”

Section: Related Surveys About Machine Learning In the Natural Sciencesmentioning

confidence: 99%

Explainable Machine Learning for Scientific Insights and Discoveries

et al. 2020

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Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and discoveries from observational or simulated data. A prerequisite for obtaining a scientific outcome is domain knowledge, which is needed to gain explainability, but also to enhance scientific consistency. In this article we review explainable machine learning in view of applications in the natural sciences and discuss three core elements which we identified as relevant in this context: transparency, interpretability, and explainability. With respect to these core elements, we provide a survey of recent scientific works that incorporate machine learning and the way that explainable machine learning is used in combination with domain knowledge from the application areas. * R. Roscher and J. Garcke contributed equally to this work arXiv:1905.08883v3 [cs.LG]

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Section: Related Surveys About Machine Learning In the Natural Sciencesmentioning

confidence: 99%

Explainable Machine Learning for Scientific Insights and Discoveries

et al. 2020

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“…Data-driven (machine learning) methods are increasingly being explored for rapid therapeutic development [8,9], potentially offering the promise of a very rapid design phase. However, a purely data-driven machine learning system would be limited in rapid response scenarios due to the inevitable insufficiency of available data on novel or emerging pathogens.…”

Section: Introductionmentioning

confidence: 99%

Rapidin silicodesign of antibodies targeting SARS-CoV-2 using machine learning and supercomputing

Desautels

Zemła

Lau

et al. 2020

Preprint

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Rapidly responding to novel pathogens, such as SARS-CoV-2, represents an extremely challenging and complex endeavor. Numerous promising therapeutic and vaccine research efforts to mitigate the catastrophic effects of COVID-19 pandemic are underway, yet an efficacious countermeasure is still not available. To support these global research efforts, we have used a novel computational pipeline combining machine learning, bioinformatics, and supercomputing to predict antibody structures capable of targeting the SARS-CoV-2 receptor binding domain (RBD). In 22 days, using just the SARS-CoV-2 sequence and previously published neutralizing antibody structures for SARS-CoV-1, we generated 20 initial antibody sequences predicted to target the SARS-CoV-2 RBD. As a first step in this process, we predicted (and publicly released) structures of the SARS-CoV-2 spike protein using homology-based structural modeling. The predicted structures proved to be accurate within the targeted RBD region when compared to experimentally derived structures published weeks later. Next we used our in silico design platform to iteratively propose mutations to SARS-CoV-1 neutralizing antibodies (known not to bind SARS-Cov-2) to enable and optimize binding within the RBD of SARS-CoV-2. Starting from a calculated baseline free energy of -48.1 kcal/mol (± 8.3), our 20 selected first round antibody structures are predicted to have improved interaction with the SARS-CoV-2 RBD with free energies as low as -82.0 kcal/mole. The baseline SARS-CoV-1 antibody in complex with the SARS-CoV-1 RBD has a calculated interaction energy of -52.2 kcal/mole and neutralizes the virus by preventing it from binding and entering the human ACE2 receptor. These results suggest that our predicted antibody mutants may bind the SARS-CoV-2 RBD and potentially neutralize the virus. Additionally, our selected antibody mutants score well according to multiple antibody developability metrics. These antibody designs are being expressed and experimentally tested for binding to COVID-19 viral proteins, which will provide invaluable feedback to further improve the machine learning-driven designs. This technical report is a high-level description of that effort; the Supplementary Materials includes the homology-based structural models we developed and 178,856 in silico free energy calculations for 89,263 mutant antibodies derived from known SARS-CoV-1 neutralizing antibodies.

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“…There is a significant amount of literature concerning the application of machine learning methods in the drug discovery pipeline. These works span application fields such as target identification, target-disease association, drug design, drug repurposing, patient stratification, and biomarker discovery (7,8). In the novel target identification field, Kumari et al (9) proposed an improved random forest (RF) algorithm that integrates bootstrap and rotation feature matrix components, to discriminate human drug targets from non-drug targets.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide investigation of gene-cancer associations for the prediction of novel therapeutic targets in oncology

Bazaga

Leggate

Weisser

2020

Preprint

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Motivation: A major cause of failed drug discovery programs is suboptimal target selection, resulting in the development of drug candidates that are potent inhibitors, but ineffective at treating the disease. In the genomics era, the availability of large biomedical datasets with genome-wide readouts has the potential to transform target selection and validation.Results: We have investigated how computational intelligence methods can be applied to predict novel therapeutic targets in oncology. We compared different machine learning classifiers applied to the task of drug target classification for nine different human cancer types. For each cancer type, a set of "known" target genes was obtained and equally-sized sets of "non-targets" were sampled multiple times from the human protein-coding genes. Models were trained on mutation, gene expression (TCGA), and gene essentiality (DepMap) data. In addition, we generated a numerical embedding of the interaction network of protein-coding genes using deep network representation learning and included the results in the modeling. We assessed feature importance using a random forests classifier and performed feature selection based on measuring permutation importance against a null distribution. Our best models achieved good generalization performance based on the AUC metric. With the best model for each cancer type, we ran predictions on more than 15,000 protein-coding genes to identify potential novel targets. Our results indicate that this approach may be useful to inform early stages of the drug discovery pipeline. machine learning | therapeutic target identification | oncologyCorrespondence: ar989@cam.ac.uk

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Rethinking drug design in the artificial intelligence era

Cited by 530 publications

References 170 publications

Explainable Machine Learning for Scientific Insights and Discoveries

Explainable Machine Learning for Scientific Insights and Discoveries

Rapidin silicodesign of antibodies targeting SARS-CoV-2 using machine learning and supercomputing

Genome-wide investigation of gene-cancer associations for the prediction of novel therapeutic targets in oncology

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