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AI & Deep Learning Papers, Links, Articles etc

AI & Deep Learning Papers, Links, Articles etc

Feb 26, 2024

This page is to collect and save scientific citations and useful links.

At this time the research papers of interest are (1) quality reviews; (2) use cases in AI and ML use in drug discovery; (3) high-profile use cases of AI and ML in medical diagnostics; (4) regulatory and ethical aspects of AI application in drug discovery and medicine.

  1. AI-powered drug discovery captures pharma interest https://www.nature.com/nbt/journal/v35/n7/full/nbt0717-604.html

  2. Great intro to ML and Big Data Demystifying Big Data and Machine Learning for Healthcare  https://www.crcpress.com/Demystifying-Big-Data-and-Machine-Learning-for-Healthcare/Natarajan-Frenzel-Smaltz/p/book/9781138032637

  3. The Next Era: Deep Learning in Pharmaceutical Research https://www.ncbi.nlm.nih.gov/pubmed/27599991 - Ekins Nov 2016 
    https://www.pharma-iq.com/informatics/articles/is-big-pharma-really-on-cusp-of-ai-shake-out-0

  4. Review of AI June 2017 http://www.biorxiv.org/content/early/2017/05/28/142760

  5. Github discussions is exellent https://github.com/greenelab/deep-review/issues

  6. Excellent Blog from Bharath Ramsundar http://rbharath.github.io/what-cant-deep-learning-do/

  7. IBM Watson A Reality Check for IBM’s AI Ambitions https://www.technologyreview.com/s/607965/a-reality-check-for-ibms-ai-ambitions/

  8. June 2017 Next Generation AI algorithms need to make most of AI chips optimisation

  9. Greg Diamos, Head of Systems Research at Baidu Silicon Valley AI

  10.  https://www.forbes.com/sites/ciocentral/2017/06/21/we-need-next-generation-algorithms-to-harness-the-power-of-todays-ai-chips/#7f20f83a47a0

  11. WSJ June 2017How AI Is Transforming Drug Creation https://www.wsj.com/articles/how-ai-is-transforming-drug-creation-1498442760

  12. Forbes Aug 2017 https://www.forbes.com/sites/forbestechcouncil/2017/08/03/artificial-intelligence-in-drug-discovery-a-bubble-or-a-revolutionary-transformation/#26079df64494

  13. Beyond the Hype: Deep Neural Networks 2 Outperform Established Methods Using 3 A ChEMBL Bioactivity Benchmark Set http://www.biorxiv.org/content/biorxiv/early/2017/07/28/168914.full.pdf

  14. WSJ Article March 2017 - broader business space review https://www.wsj.com/articles/how-ai-is-transforming-the-workplace-1489371060

  15. Top 10 Recommendations for the AI Field in 2017 Oct 2017

  16. The challenge of AI technology adoption in healthcare https://www.linkedin.com/pulse/challenge-ai-technology-adoption-healthcare-jay-chyung-md-phd/

  17. Oct 2017 Challenages of AI adoption in Healthcare https://www.linkedin.com/pulse/challenge-ai-technology-adoption-healthcare-jay-chyung-md-phd/?trackingId=Cpa346%2FuUurbWLcO%2FhUTxw%3D%3D

  18. Jan 2017 First FDA Approved Cloud Based Deep learning https://www.forbes.com/sites/bernardmarr/2017/01/20/first-fda-approval-for-clinical-cloud-based-deep-learning-in-healthcare/#357cbfe1161c     (*** added 2017)

  1. Fundamental of Deep Learning https://www.analyticsvidhya.com/blog/2016/03/introduction-deep-learning-fundamentals-neural-networks/?utm_content=bufferf9225&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

  2. Drug Makers Guide to the Galaxy: Excentia and polypharmacology https://www.nature.com/news/the-drug-maker-s-guide-to-the-galaxy-1.22683

  3. Data is Key ingredient for AI https://www.elsevier.com/connect/5-reasons-data-is-a-key-ingredient-for-ai-applications?sf175330640=1

  4. HBR AI for the real world https://hbr.org/2018/01/artificial-intelligence-for-the-real-world

  5. AI for Health and Healtcare report (Mitre Group) https://www.healthit.gov/sites/default/files/jsr-17-task-002_aiforhealthandhealthcare12122017.pdf

  6. Deep Learning for Biology https://www.nature.com/articles/d41586-018-02174-z

  7. An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling https://www.ncbi.nlm.nih.gov/pubmed/27885862

  8. The ELF Honest Data Broker: informatics enabling public–private collaboration in a precompetitive arena https://www.sciencedirect.com/science/article/pii/S1359644615004249?via%3Dihub

  9. Feb 2018: AI Startups https://blog.benchsci.com/startups-using-artificial-intelligence-in-drug-discovery?lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3BmEHpBm1fQ%2BKkySB%2FSg8G%2Fw%3D%3D#step11

  10. Opportunities And Obstacles For Deep Learning In Biology And Medicine https://www.biorxiv.org/content/early/2018/01/19/142760

  11. J. R. Soc. Interface 15: 20170387. http://dx.doi.org/10.1098/rsif.2017.0387

  12. Perspectives: Augmented intelligence https://cen.acs.org/articles/96/i14/Perspectives-Augmented-intelligence.html.html

  13. Computer system predicts products of chemical reactions: Machine learning approach could aid the design of industrial processes for drug manufacturing http://people.csail.mit.edu/tommi/papers/Connor_etal_ACS_2017.pdf

  14. ANN used for Image analysis - Novartis NIBR https://www.novartis.com/stories/discovery/machine-learning-poised-accelerate-drug-discovery and the original publication in Bioinformatics: https://academic.oup.com/bioinformatics/article/33/13/2010/2997285?searchresult=1

  15. The failure of IBM Watson: https://www.statnews.com/2018/07/25/ibm-watson-recommended-unsafe-incorrect-treatments/

  16. An optimistic editorial from Nature references success of Wuxi NextCode: https://www.nature.com/articles/d41586-018-05267-x

  17. AI interpretation of radiology images is hard, harder than initially anticipated: https://www.technologyreview.com/s/610552/google-x-ray-project-shows-ai-wont-replace-doctors-any-time-soon/

  18. PLASTER, Nvidia's methodology for assessment of AI performance (PDF): https://images.nvidia.com/content/pdf/plaster-deep-learning-framework.pdf

  19. UK national platform for AI? http://news.top-consultant.com//New-White-Paper-Calls-for-Government-wide-AI-Platform-19336.html

  20. How Technology can tame scientific literature https://www.nature.com/articles/d41586-018-06617-5

  21. Oncology use case: http://gate250.com/rk/2018FLAG_UsingArtificialIntelligence_Whitepaper_digital-Final.pdf  

  22. Rules of Machine Learning http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf - could be expanded

  23. Google DeepMind and healthcare in an age of algorithms   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5741783/

  24. "Winner's Curse" - How to do better science in AI: https://openreview.net/pdf?id=rJWF0Fywf

  25. A list of biomedical start-ups that claim AI as a core technology: https://blog.benchsci.com/startups-using-artificial-intelligence-in-drug-discovery

  26. AI used to predict CRISPR DNA cuts; in the future, this may be used for gene therapy: http://www.bio-itworld.com/2018/11/08/machine-learning-predicts-how-dna-breaks-under-crispr.aspx

  27. Augmenting Medicinal Chemist with data https://doi.org/10.1016/j.drudis.2018.03.011

  28. Review of Forrester Wave views on AI and Automation https://www.forbes.com/sites/gilpress/2018/11/06/ai-and-automation-2019-predictions-from-forrester/#788550a54cb5 Nov 2018

  29. Ethics in AI: Rabbi Jonathan Sacks explores how we should respond to the ways in which AI is transforming our world https://www.bbc.co.uk/sounds/play/b0bgrw3k

  30. What it means to open AI’s black box? http://usblogs.pwc.com/emerging-technology/to-open-ai-black-box/

  31. Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome–Inhibitor Interaction Landscapes https://pubs.acs.org/doi/full/10.1021/acs.jcim.8b00640      (*** added 2018)

  1. Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators https://www.nature.com/articles/s42004-018-0068-1

  2. Artificial intelligence and its potential in oncology https://doi.org/10.1016/j.artmed.2018.08.008

  3. Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review, by Bernal, J., Artificial Intelligence In Medicine,

  4. https://doi.org/10.1016/j.artmed.2018.08.008

  5. Transforming Computational Drug Discovery with Machine Learning and AI ACS Med. Chem. Lett. 2018, 9, 11, 1065-1069

  6. The convergence of artificial intelligence and chemistry for improved drug discovery (AZ) https://www.future-science.com/doi/full/10.4155/fmc-2018-0161

  7. AI in Pharma, by LEK Consulting: https://www.lek.com/insights/artificial-intelligence-life-sciences-formula-pharma-success-across-drug-lifecycle#.XAvu0zCNM7M.linkedin

  8. Are Ontologies relevant in a Machine Learning-centric world? By Lee Harland, based on our own Boston AI workshop: https://www.scibite.com/are-ontologies-relevant-in-a-machine-learning-centric-world/ 

  9. Computers turn neural signals into speech:http://science.sciencemag.org/content/363/6422/14?utm_campaign=wnews_sci_2019-01-03&et_rid=17144995&et_cid=2581434

  10. How is automated text summarization done? https://arxiv.org/pdf/1707.02268.pdf

  11. FREE ML courses (the title says 10, but the text contains links to over 30): https://www.kdnuggets.com/2018/12/10-more-free-must-see-courses-machine-learning-data-science.html

  12. Eric Topol: High-performance medicine: the convergence of human and artificial intelligence

  13. AI and Neural Net Summary in 21 pages:https://www.linkedin.com/feed/update/activity:6492067351771574272/      (*** added 01/2019)

  1. Dark Secret at heart of AI -  Black box https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/

  2. Classification of ligand-binding pockets in proteins with a convolutional neural network: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006718  (but only works well for nucleotide and heme binding sites, so only in a couple of highly specialized cases)

  3. Overfitting dangers with small datasets: https://www.chemistryworld.com/news/dispute-over-reaction-prediction-puts-machine-learnings-pitfalls-in-spotlight/3009912.article

  4. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks (Nature): https://www.nature.com/articles/s41598-019-40041-7.pdf

  5. AI recognizes speech‐based markers for posttraumatic stress disorder in US veterans, with 89% classification rate: https://onlinelibrary.wiley.com/doi/abs/10.1002/da.22890

  6. A machine learning approach for somatic mutation discovery for diagnostics: https://stm.sciencemag.org/content/10/457/eaar7939  and the full-length research article: https://stm.sciencemag.org/content/11/489/eaat6177?utm_source=STAT+Newsletters&utm_campaign=50dc0470e5-MR_COPY_01&utm_medium=email&utm_term=0_8cab1d7961-50dc0470e5-149702353

  7. Learn the basics of statistics: https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/ Well, not only the basics! Some advanced topics too.

  8. On the interpretability of Neural Net models: https://appsilon.com/please-explain-black-box/

  9. Predict risk of lung cancer by a Google AI model? Claimed 94% AUC, but refused to release the code: https://www.nature.com/articles/s41591-019-0447-x

  10. What is the difference between AI, ML, and Deep Learning models? See here: https://www.edureka.co/blog/ai-vs-machine-learning-vs-deep-learning/  and here: https://www.geospatialworld.net/blogs/difference-between-ai%EF%BB%BF-machine-learning-and-deep-learning/    (*** added 05/2019)

  1. Review: AI in Drug Design: https://www.mdpi.com/1420-3049/23/10/2520/htm

  2. Is China way ahead of the West in AI use in medicine? See here (may require registration for full-length paper): https://www.statnews.com/2019/06/05/china-leapfrogging-us-using-ai-in-medicine/

  3. ML Flow, a system for managing the AI model life cycle, and deployment to the cloud: https://mlflow.org/

  4. An interesting lecture in the history of science. Bernard Widrow, a Stanford professor, talks about the Least Mean-Squares (LMS) adaptive algorithm used to train first neuron models. The device shown in the lecture dates back to 1959 and is actually an electro-mechanical switch box. It has parts that broke due to age, but still functions, and can be trained to tell apart letters of the alphabet.  Part 1 and part 2. 

  5. Clinical validity and technical validity of AI methods in medicine: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6526255/#r10

  6. In a review of 516 research papers in the diagnostic analysis of medical images with AI, only 31 (or 6%) performed external validation, and none used best practices in clinical trial design: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6389801/  The papers reviewed were published between Jan 1st and Aug 18th 2018.

  7. FDA: Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback: https://www.regulations.gov/document?D=FDA-2019-N-1185-0001  (download the actual document in PDF format from this page)

  8. Example of QSAR in 3D using Convolutional Neural Network: https://www.jstage.jst.go.jp/article/cpb/67/5/67_c18-00757/_html/-char/en

  9. Nature Review: Technologies to watch in 2019: https://www.nature.com/articles/d41586-019-00218-6

  10. Machine Learning for Medical Ultrasound: Status, Methods, and Future Opportunities. This review includes organ segmentation, an apparently hard topic. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5886811/

  11. Predict Adverse Drug Reactions (ADRs) through drug-gene interactions: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473874/  This review might be of interest in view of our recent drug repurposing datathon, where prediction of ADRs played a large role. (*** added 06/2019)