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

...

  1. Privacy of patients and data donors in the age of genomics and big data mining: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1741-0
  2. Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis. By Alexander Button, Daniel Merk, Jan A. Hiss & Gisbert Schneider, Nature Machine Intelligence, volume 1, pages307–315 (2019)
  3. Review: Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery: https://pubs.acs.org/doi/full/10.1021/acs.chemrev.8b00728
  4. Machine and deep learning meet genome-scale metabolic modeling: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007084
  5. Disease gene prediction for molecularly uncharacterized diseases: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007078
  6. Not quite ML, but curious: Identifying determinants of persistent MRSA bacteremia using mathematical modeling: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007087
  7. A popular piece on using AI to mine Pubmed data in an attempt to diagnose a rare disease: "How an AI expert took on his toughest project ever: writing code to save his son’s life", but what is really interesting, is that this project has a public github page, from which one can get one's own copy of the software
  8. Artificial intelligence for assisting diagnostics and assessment of Parkinson's disease-A review. Main point is that after 48 completed studies, there is still not enough data.
  9. Putting benchmarks in their rightful place: The heart of computational biology. Editorial
  10. Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks. Best practices
  11. Three pitfalls to avoid in machine learning (Nature, Google)  (*** added 07/2019)

...

  1. Review: Deep learning in drug discovery: opportunities, challenges and future prospects
  2. Learning Health System for Breast Cancer: Pilot Project Experience. "It is possible to extract, read, and combine data from the EHR to view the patient journey. The agreement between NLP and the gold standard was high, which supports validity."  
  3. WE-E-213CD-06: A Locally Adaptive, Intensity-Based Label Fusion Method for Multi- Atlas Auto-Segmentation. Comparison of proprietary anatomy segmentation methods for medical images.
  4. Best practices of AI use in medicine: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6599029/
  5. A collection of papers on use of AI and ML in diagnostic imaging: The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review; State-of-the-Art Deep Learning in Cardiovascular Image Analysis; Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist; Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology
  6. The combination of computational chemistry and computational materials science with machine learning and artificial intelligence    This is relevant to AI in CADD
  7. "Learning rates of state-of-the-art artificial learning algorithms can be improved by adopting fundamental principles that govern the dynamics of the brain" Biological learning curves outperform existing ones in artificial intelligence algorithms
  8. Artificial intelligence in digital pathology
  9. An Analytical Review of Computational Drug Repurposing
  10. Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning
  11. Yearbook of Medical Informatics, published by Thieme Journals in August 2019, contains many reviews on the AI applications in medical research and practice, for instance, "Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data", "Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications", and many more. (*** added 08/2019)

...

  1. Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier
  2. PLATYPUS: A Multiple–View Learning Predictive Framework for Cancer Drug Sensitivity Prediction
  3. Tumor classification based on image analysis: A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks
  4. Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer
  5. Towards trustable machine learning  "Clinical implementations of machine learning that are accurate, robust and interpretable will eventually gain the trust of healthcare providers and patients"
  6. Gartner on ML in business (very general and not focused on pharma, but could be a good primer):    https://www.gartner.com/binaries/content/assets/events/keywords/catalyst/catus8/preparing_and_architecting_for_machine_learning.pdf
  7. NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules
  8. Review: Looking beyond the hype: Applied AI and machine learning in translational medicine
  9. Special issue of the Journal of the American College of Radiology on AI and Data Science, with many application cases of AI to clinical image processing
  10. Statistical considerations for testing an AI algorithm used for prescreening lung CT images. Although a highly specialized use case is employed here, the statistical concepts are broadly valid.
  11. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Report from A. Zhavoronkov's group to Nature Biotechnology.
  12. Machine Learning to Predict, Detect, and Intervene Older Adults Vulnerable for Adverse Drug Events in the Emergency Department 
  13.  Extracting biological age from biomedical data via deep learning: too much of a good thing? An attempt to create a CNN predictor of the natural "biological clock"
  14. Computational prediction of diagnosis and feature selection on mesothelioma patient health records. Random forest outperformed all models tried, but MCC is low for all model categories. Data is available via the Irvine depository.
  15. Modelling the prevalence of diabetes mellitus risk factors based on artificial neural network and multiple regression. Logistic regression outperformes a neural net.    (*** added 09/2019)

...

  1. AI used to make sonogram movies of living heart. STAT news report.
  2. User-centric design of AI products: People+AI Guidebook from Google
  3. AAIH whitepaper describes the basic AI techniques and discusses some applications in healthcare: https://www.theaaih.org/pdf/1571334853.pdf 
  4. AI in health care delivery: https://catalyst.nejm.org/health-care-ai-systems-changing-delivery/
  5. WanDB, a package to log hyperparameters and output metrics from your runs, explore model architectures, and compare results. (FYI only. We at Pistoia Alliance have NOT tested this).
  6. Bradshaw molecular design tool GSK Oct 2019  https://link.springer.com/article/10.1007/s10822-019-00234-8 
  7. Looking beyond the hype: Applied AI and machine learning in translational medicine
  8. Characterizing Artificial Intelligence Applications in Cancer Research: A Latent Dirichlet Allocation Analysis. Trends in AI publications in medicine.
  9. Patient similarity networks for precision medicine
  10. How far have decision tree models come for data mining in drug discovery?
  11. Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement
  12. An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning
  13. A collection of psychiatry-themed use cases of AI came out this month: 
    1. Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication‐class of response in complex patients
    2. Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram
    3. Machine learning in major depression: From classification to treatment outcome prediction
    4. Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression  Apparently, ML-based prognostic engine out-performs human psychiatrists
  14. The plan to mine the world’s research papers. Nature on a controversial plan to subvert the copyright
  15. Discovery of Novel Conotoxin Candidates Using Machine Learning. De-novo design of toxic peptides. Interesting but unfortunately does not include experimental validation of the pharmaceutical properties of these sequences
  16. Why deep-learning AIs are so easy to fool  DNNs are brilliant at highly specific tasks and brittle when unexpected input arrives. Nature editorial on adversarial learning in DNNs.
  17. Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics. Review
  18. Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability. Responsibility to explain AI results is not only for the regulators, but mainly for patients.
  19. “A patient like me” – An algorithm-based program to inform patients on the likely conditions people with symptoms like theirs have.
  20. Key challenges for delivering clinical impact with artificial intelligence. "Robust clinical evaluation, using metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy to include quality of care and patient outcomes, is essential. Further work is required (1) to identify themes of algorithmic bias and unfairness while developing mitigations to address these, (2) to reduce brittleness and improve generalisability, and (3) to develop methods for improved interpretability of machine learning predictions."     (*** added 10/2019)

...

  1. Real-time multi-model ensemble forecasts for seasonal influenza in the U.S.
  2. A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer  Nature Communications
  3. Are we doing a good job in validation of medical devices that are powered by AI? Lifecycle Regulation of Artificial Intelligence– and Machine Learning–Based Software Devices in Medicine, JAMA
  4. How to Read Articles That Use Machine Learning. JAMA  2019 Users’ Guides to the Medical Literature
  5. Adversarial Controls for Scientific Machine Learning. ACS Chemical Biology 2018 "Machine learning algorithms readily exploit confounding variables and experimental artifacts instead of relevant patterns, leading to overoptimistic performance and poor model generalization."
  6. CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data. A machine-learning technique
  7. Algorithms on regulatory lockdown in medicine. Science paper on the regulation of AI-driven medical technologies.
  8. Rethinking drug design in the artificial intelligence era. Nature review with an extensive reference list. Gisbert et al..
  9. Attitudes Of Chinese Cancer Patients Toward The Clinical Use Of Artificial Intelligence. Cancer patients trust human doctors more than AI when AI recommendations and human MD opinions differ. Going forward this topic of trust will be gaining importance
  10. A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification.
  11. Visualizing structure and transitions in high-dimensional biological data. An algorithm to present highly complex, multi-dimensional data in a form with reduced dimensionality, suitable for human review.
  12. PathFlowAI: A High-Throughput Workflow for Preprocessing, Deep Learning and Interpretation in Digital Pathology. Using AI to analyze liver microscopy images
  13. Pacific Symposium in Biocomputing announcement of the workshop in ethics of AI.
  14. DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
  15. Ten quick tips for effective dimensionality reduction
  16. Random forest prediction of Alzheimer’s disease using pairwise selection from time series data. Not AI but machine learning
  17. Converging a Knowledge-Based Scoring Function: DrugScore2018.  DrugScore2018 is a competitive scoring and objective function for structure-based ligand design purposes.
  18. Practical Model Selection for Prospective Virtual Screening. Random Forest wins over complex NN methods.
  19. Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions Nature Communications 2019 Gasteiger et al
  20. Rule of thumb: Which AI / ML algorithms to apply to business problems. Has a small section on problems in health sciences. Ideally we'd be able to produce a similar guide but for problems specific to life science research.
  21. Conditional Molecular Design with Deep Generative Models
  22. The global landscape of AI ethics guidelines. Nature 09/2019
  23. Artificial Intelligence in medical imaging practice: looking to the future. A short review that covers the major application advances in the field as well as the ethical part of the AI use in medical decision-making. 11/2019
  24. Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer. Use ML to cluster cancer cell lines based on their drug combination sensitivity profiles. PLOS Comp Bio 05/2019
  25. Implementation of machine learning algorithms to create diabetic patient re-admission profiles  using public data sources at UC Irvine Machine Learning Repository
  26. Machine Learning Technical Landscape picture
  27. Bayesian Nonparametric Models tutorial
  28. Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities
  29. From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices
  30. How the FDA Regulates AI
  31. Ethical considerations about artificial intelligence for prognostication in intensive care "Respect for patients’ autonomy during decision-making requires transparency of the data processing by AI models to explain the predictions derived from these models."  (*** added 12/2019)
  32. International evaluation of an AI system for breast cancer screening. This is a Google-developed tool which is claimed to achieve a higher accuracy in diagnosis of breast cancer than that by human oncologists. However, code and data used to build the classifier are not released.
  33. Applications of Machine Learning Approaches in Emergency Medicine; a Review Article. In this paper the authors compare performance of a different ML and AI methods for prediction (diagnosis, to be precise) of various diseases from EHR records. Performance is variable and the methods used for different disease types are vastly different. 
  34. Identification of pharmacodynamic biomarker hypotheses through literature analysis with IBM Watson
  35. Opportunities for Artificial Intelligence in Advancing Precision Medicine. Review of ML methods used to analyze "-omics" data.
  36. Towards A Rigorous Science of Interpretable Machine Learning. Arxiv
  37. Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research
  38. The MELLODDY Consortium. Pharma Companies Join Forces to Train AI for Drug Discovery Using Blockchain
  39. Addressing Bias in Artificial Intelligence in Health Care. Abstract only.
  40. AI in biomedical sciences attracted over $5B in VC investment. Market report
  41. Deep Learning-driven research for drug discovery: Tackling Malaria. PLOS
  42. Contract offers unprecedented look at Google deal to obtain patient data from the University of California. UCSF allowed Google to use patient data for AI research, which raises all kinds of ethical questions.
  43. A comparative study of deep learning architectures on melanoma detection
  44. Gartner Hype Cycle for Artificial Intelligence, 2019
  45. A Deep Learning Approach to Antibiotic Discovery    (*** added 02/2020)

...

  1. AI gone astray: How subtle shifts in patient data send popular algorithms reeling, undermining patient safety
  2. Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings
  3. Pharma AI Use Cases collection. Very basic, and with vendor advertising, but it is nice to have the majority of use cases in one place
  4. EPAM AI Solutions Library. Many products are open-source
  5. Chan Zuckerberg Initiative to build largest biomedical research computing system
  6. AI’s potential to accelerate drug discovery needs a reality check. Nature
  7. Clinical landscape of AI. An interesting figure from a Twitter post
  8. What is new in Artificial Intelligence from the 2023 Gartner Hype Cycle
  9. Highlights in AI for Drug Discovery. Funding deals and business advancements
  10. Generative artificial intelligence for de novo protein design
  11. AI drug discovery companies are now mature enough to show examples of successes and failures. This clearly indicates that AI as a technology is not a magic bullet many thought it would become during the hype days. This LinkedIn post delivers some of the sobering statistics. Lack of reliable data is clearly identified as the main bottleneck
  12. Predetermined Change Control Plans for Machine Learning-Enabled Medical Devices: Guiding Principles, by the US FDA; and a LinkedIn post on the same topic with some historic data and statistics on the approvals of AI medical devices
  13. White House orders up a plan to vet AI in health care
  14. EU AI Act
  15. AI Standards Hub
  16. On the LLMs: Google's Gemini AI model beats ChatGPT4 in various tests. The part relevant to biotechnology R&D is at 19:19
  17. Advances in genetics and artificial intelligence have helped scientists to crack the second “code of life” – gene regulation
  18. Library of digital endpoints
  19. LoRAX: The Open Source Framework for Serving 100s of Fine-Tuned LLMs by Predibase
  20. Deciphering RNA splicing logic with interpretable machine learning
  21. Detection of AI-Created Images Using Pixel-Wise Feature Extraction and Convolutional Neural Networks
  22. Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation
  23. Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images
  24. The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery
  25. Trends in Medicinal Chemistry: KNIME Workflows, QSAR Models, LLMs and Chemical Search Strategies
  26. “There is an AI for that” A list of AI models for many tasks (not biology research, or not only)
  27. Deep convolutional and conditional neural networks for large-scale genomic data generation
  28. Recognition and reconstruction of cell differentiation patterns with deep learning   
  29. Google AI models for healthcare (added 12/2023)

...

  1. UK AI Standards Hub
  2. LoRA Land
  3. Ten simple rules for using large language models in science, version 1.0
  4. Generative AI Tracker: A guide to the health systems and companies driving adoption
  5. AMIE: A research AI system for diagnostic medical reasoning and conversations
  6. WHO's 6 Principles For An AI Regulatory Framework For Medical Product Development
  7. Roche AI Ethics Principles
  8. Roche Data Ethics Principles
  9. Langtest: Safe and Effective Language Models
  10. Account of AI in drug discovery in 2023 and projections for 2024 on Tech Meets Bio
  11. More predictions for AI in 2024
  12.  Pistoia Alliance launches freely available IDMP Ontology 1.0, covering all five standards set by ISO - Pistoia Alliance
  13. Google's Gemini AI model beats ChatGPT4 in various tests. YouTube link. The relevant part is at 19:19
  14. SCOPE: FDA facilitates AI and ML use for clinical trials and drug development
  15. Exploring chemical reaction space for ML    (added 02/2024)







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