We have updated the Drug Name Recogniser Google Sheets™ plugin to cover molecular mass, chemical formula and SMILES strings.
Link to install the tool: https://fastdatascience.com/ai-in-pharma/drug-name-recogniser
Natural language processing
We have a Python library available which you can find on PyPI and on Github. It’s fully open source with MIT License.
You can install the Python library by typing in the command line:
pip install drug-named-entity-recognition
You can also try the library in your browser on Fast Data Science.
Fast Data Science provides NLP consulting for the pharma industries. Our flagship product is the Clinical Trial Risk Tool https://clinicaltrialrisk.org.
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Thomas Wood presents the Clinical Trial Risk Tool before the November meeting of the Clinical AI Interest Group at Alan Turing Institute The Clinical AI Interest group is a community of health professionals from a broad range of backgrounds with an interest in Clinical AI, organised by the Alan Turing Institute.
Fast Data Science will appear at Ireland’s Expert Witness Conference on 20 May 2026 in Dublin On 20 May 2026, La Touche Training is running the Expert Witness Conference 2026, at the Radisson Blu Hotel, Golden Lane, Dublin 8, Ireland. This is a full-day event combining practical workshops and interactive sessions, aimed at expert witnesses and legal professionals who want to enhance their expertise. The agenda covers critical topics like recent developments in case law, guidance on report writing, and techniques for handling cross-examination.
Guest post by Alex Nikic In the past few years, Generative AI technology has advanced rapidly, and businesses are increasingly adopting it for a variety of tasks. While GenAI excels at tasks such as document summarisation, question answering, and content generation, it lacks the ability to provide reliable forecasts for future events. GenAI models are not designed for forecasting, and along with the tendancy to hallucinate information, the output of these models should not be trusted when planning key business decisions. For more details, a previous article on our blog explores in-depth the trade-offs of GenAI vs Traditional Machine Learning approaches.
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