All Machine Learning (ML) articles – Page 4
-
NewsFibroblast mapping reveals potential universal drug targets
Scientists have mapped the diversity of fibroblasts and discovered how ‘rogue’ fibroblasts drive multiple diseases, revealing drug targets that could transform treatments across the body.
-
ArticleMaking sense of AI: bias, trust and transparency in pharma R&D
AI is increasingly used in drug discovery, but hidden bias and ‘black box’ models threaten trust and transparency. This article explores how explainable AI can turn opaque predictions into clear, accountable insights.
-
ArticleAI meets human tissue to fast-track precision medicine development
By combining human tissue models with explainable AI, researchers can analyse complex patient data to identify which treatments work best for which patients. First applied to inflammatory bowel disease, this approach could improve clinical trial success rates across many diseases.
-
ArticleAnimal-free drug discovery is closer with QSP
Quantitative Systems Pharmacology (QSP) is fast becoming a standard tool in drug development, offering a human-relevant way to predict drug effects before the clinic. Dr Josh Apgar of Certara explains how it is helping to cut reliance on animal testing and speed discovery.
-
ArticleBeyond the hype: a veteran’s honest assessment of AI in drug discovery – Part 3
AI is starting to transform drug discovery, but progress is still slow and big challenges remain. Thibault Géoui explores the gaps, hurdles and breakthroughs needed before it can truly change pharma R&D.
-
ArticleBeyond the hype: a veteran's honest assessment of AI in drug discovery - Part 2
Thibault Géoui explains why AI could finally help pharma overcome its productivity crisis and why the payoff won’t come as quickly as the optimists claim.
-
ArticleWhat if drug discovery took months, not decades?
Drug discovery is slow, costly and often unsuccessful. DTR hears how GATC Health is applying AI and multiomics to make the process faster, more precise and less reliant on trial and error.
-
ArticleHow AI is quietly changing drug manufacturability
AI is moving beyond drug design to answer a critical question: can a promising compound actually be manufactured at scale? By predicting synthetic feasibility early, machine learning tools are helping drug developers avoid costly failures, streamline R&D and design molecules that are both effective and practical to produce.
-
ReportBeyond the Lab: Biomarkers Powering Tomorrow’s Therapies
Biomarkers are redefining how precision therapies are discovered, validated and delivered. This exclusive expert-led report reveals how leading teams are using biomarker science to drive faster insights, cleaner data and more targeted treatments – from discovery to diagnostics.
-
ArticleWhy smarter financial planning could be key to clinical trial success
Effective financial management is vital for clinical trial success, yet many preclinical and clinical companies face inefficiencies due to outdated systems. Jennifer Kyle, CEO of Condor Software, explains how advanced financial platforms can streamline processes, improve forecasting and ensure better resource allocation throughout drug development.
-
ArticleFixing failed drugs: AI solutions for toxicity in drug discovery – part 3
What role could large language models and AI agents play in drug safety? In Part 3, Layla Hosseini-Gerami of Ignota Labs discusses how emerging technologies might make toxicity analysis faster, more accessible and part of the drug discovery workflow from day one.
-
NewsNew AI model PROTsi identifies aggressive tumours using protein markers
Researchers in Brazil and Poland have developed an AI-powered tool that predicts cancer aggressiveness by analysing protein expression - offering new insights into tumour behaviour.
-
ArticleFixing failed drugs: AI solutions for toxicity in drug discovery – part 2
Why do so many drug candidates fail before reaching patients – and can AI help stop the losses? In Part 2, Layla Hosseini-Gerami of Ignota Labs outlines the scope of the toxicity problem and explains why failures often come too late to fix.
-
ArticleThe future of CNS drug development: signs of real progress
New therapeutic approaches are emerging for CNS disorders – but can they overcome the toughest barriers in drug development? Find out what is driving progress and what still stands in the way.
-
ArticleFixing failed drugs: AI solutions for toxicity in drug discovery – part 1
Why do so many drug candidates fail before reaching patients – and can AI help stop the losses? In Part 1, Layla Hosseini-Gerami of Ignota Labs outlines the scope of the toxicity problem and explains why failures often come too late to fix.
-
ArticleWhat single cells are revealing about brain disorders
Single-cell and spatial technologies are giving researchers an unprecedented view of how brain diseases like Alzheimer’s really work. The result? Faster discovery, clearer targets and a new path towards more effective treatments.
-
ArticleFixing drug discovery’s most persistent problem with AI
AI will not replace drug discovery, but it might finally fix one of its most frustrating bottlenecks. Read how a targeted approach to ADMET is cutting through the noise.
-
ArticleHow dual-targeting ADCs aim to tackle resistance
Find out how dual-target ADCs and tumour-specific Treg depletion are shaping the next wave of targeted cancer therapies.
-
NewsVirtual cell model rankings just got a major upgrade
Shift Bioscience has published a new study introducing enhanced metrics and baselines for evaluating virtual cell models - boosting gene target discovery and accelerating its rejuvenation therapeutics pipeline.
-
ArticleMachine Learning and AI in Enhancing Image Analysis of 3D Samples
Want to understand the real impact of AI in 3D sample analysis? This episode cuts straight to how machine learning is accelerating research and overcoming current limitations.