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Overcoming the limitations of existing AI models
Researchers have developed a new AI tool which recognises that protein behaviour can vary by cell and by tissue type.
List view / Grid view
Artificial Intelligence
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Part one: an introduction to data quality
In this four-part series, Dr Singh will discuss the challenges surrounding limited data quality, and some pragmatic solutions. In this first article, the key attributes that define data quality and its requirement for data scientists are elucidated.
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The art and science of drug formulation
Drug formulation is a cornerstone of modern medicine, turning raw active ingredients into consumable, effective therapies. This critical phase in drug development ensures that medications are safe, effective, and user-friendly. Here Ningfeng Fiona Li, founder and CEO of VasoDynamics, explores the world of drug formulation from its foundations to exploring…
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Improving the effectiveness of immunotherapy for glioblastoma
Researchers utilised AI to identify genes that reprogramme GBM cancer cells into dendritic cells within the tumour.
webinar
Unprecedented fragment-based screening using Spectral Shift for GPCRs
2 August 2024 | By Eurofins Discovery
In this webinar, we will present how challenging recombinant protein such as GPCRs are produced and characterised and how biophysics participates in GPCRs hit finding and hit confirmation.
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Part three: pragmatic guidelines to getting the best out of LLMs
There have been a slew of announcements over the past few months from AI-led biotechs around the potential of Large Language Models (LLM) in early drug discovery. In the third of a three-part series, Dr Raminderpal Singh presents some pragmatic guidelines for scientists in accessing and obtaining value from LLMs.
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Part two: how ChatGPT enriched animal study results
Recently there has been a flurry of announcements from AI-led biotechs around the potential of Large Language Models (LLM) in early drug discovery. In the second of a three-part series, Dr Raminderpal Singh presents an example of usage of ChatGPT, which demonstrates how accessible LLMs have become for lab scientists.
news
Capturing the computational abilities of real neurons
A new neural network computational model has been developed, which more closely reflects the abilities of real neurons and could advance AI progress.
news
AI tool identifies high-risk subtype of endometrial cancer
The tool identified patients with high-risk disease, which normally goes unrecognised by traditional diagnostics.
news
The potential of deep learning: generating drug targets
Researchers have designed synthetic, soluble versions of cell membrane proteins, which will enable faster and easier screening for new drugs.
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Changing the paradigm of drug discovery processes with AI
We had the privilege of speaking to Cellarity’s CEO, Fabrice Chouraqui, about how the company is leveraging AI to completely revolutionise the drug discovery process and unlock treatments for a vast array of diseases, even in the absence of known targets.
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Part one: what can scientists do with LLMs today?
Recently there have been a flurry of announcements from AI-led biotechs around the potential of Large Language Models (LLM) in early drug discovery. In the first of a three-part series, Dr Raminderpal Singh explores what LLMs are, how early stage biotechs can take advantage of them, and what challenges they…
news
New AI process for accurate enrolment of patients to clinical trial
The AI model screened the medical records of heart failure patients more accurately, quickly and cheaply, compared to study staff.
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Kickstarting the use of AI for biotechs: part three
Traditional wet lab scientists working on target discovery, drug identification and drug optimisation have an opportunity to catch up with their AI-enabled peers – but why should they, and how? In this article – the third of a three-part series – Dr Raminderpal Singh touches on the decisions that need…
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New algorithm forms atlas of histomorphological phenotypes
The algorithm can accurately diagnose cases of lung adenocarcinoma, determining structural features that are statistically most significant for assessing disease severity and likelihood of tumour recurrence.