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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…
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.
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 second of a three-part series – Dr Raminderpal Singh touches on methods that are being…
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 first of a three-part series – Dr Raminderpal Singh seeks to demystify the topic by outlining…
In this Q&A, Debiopharm’s Principal Scientist Dr Luke Piggott defines the critical requirement of biomarkers for identifying rare diseases. He illuminates how AI-enhanced approaches are accelerating the drug discovery process, particularly regarding clinical trial enrolment, and the breakthroughs he hopes to see in the future.
As we move towards more generalised AI models, neural networks and natural language interfaces, we’re starting to see machine learning take the place of higher order reasoning and data analysis “sense making.” Traditional scientific inquiry has typically been about asking specific questions of a specific model system under specific conditions.…
9 April 2024 | By Eurofins Discovery
Join Dr Carleton Sage to learn about predicting ADME properties as a key approach to improving the efficiency of small molecule drug discovery. AI ADME model development approaches and case studies within drug discovery projects will be discussed.
Dr Richard Cote and Dr Ramaswamy Govindan of the Washington University School of Medicine elucidate how AI, particularly deep learning networks, could identify histopathologic features in non-small cell lung cancer, and impact the treatment approach for early-stage patients.
Natural products play an underappreciated role in drug discovery. Tandem mass spectrometry (MS/MS)-based metabolomics coupled with machine learning is allowing new, highly diverse molecules from natural products to be identified, revealing bioactive compounds and pinpointing promising drug targets. The additional dimension provided by trapped ion mobility (TIMS) enables researchers to…
Six molecules that had potent antibacterial effect against one of the world’s most dangerous antibiotic-resistant bacteria were generated.
Scientists have examined gene activity in mice models, noting important indicators of liver disease severity which may be used as therapeutic strategies.
Researchers have developed an algorithm which could improve diagnostics of ovarian high-grade serous carcinoma.
We had the privilege of speaking to Dr Víctor Sebastián Pérez, Associate Director of Computational Drug Design, following his presentation at ELRIG UK 2023. He shares his insights into how Exscientia is using AI to design drug candidates for cancer treatment.
Amidst the transformative era of AI in drug discovery, this report focuses on recent advancements, notably the development of highly accurate drug target models, and how AI is revolutionising precision in identifying drug targets.
Researchers have developed a computational analysis tool which will improve patient stratification and enable personalised medicine.
