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Researchers have developed a new blood test method, CloneSeq-SV, that tracks treatment-resistant ovarian cancer cells over time. The approach could help predict recurrence and guide targeted therapies.
Dr Alan Nafiiev evaluates template-based, docking and template-free approaches to PPI prediction, highlighting how AI can enhance structural accuracy.
Multiomics, AI and liquid biopsies are giving researchers real-time insight into tumour biology and enabling more personalised cancer therapies. Find out how these technologies are advancing biomarker discovery, improving patient stratification, and guiding the design of new treatments.
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.
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.
From precision proteomics to AI-powered immune profiling, next-generation laboratory technologies are changing how new therapies are discovered and developed. Here are four innovations set to shape the lab of the future - and the future of drug discovery.
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.
Scientists at Tufts University have developed an AI tool that demonstrates how tuberculosis drugs kill bacteria – an advancement that could speed-up the discovery of shorter, more effective treatments.
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.
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.
Scientists have developed a new computational model that reveals how the striosomal compartment of the brain’s striatum influences decision-making – which could lead to improved therapies for psychiatric disorders.
An interview with Thibault Géoui reveals why this technology wave might finally break through pharma's productivity crisis – and why it will take longer than the optimists claim.
12 August 2025 | By Wuxi Biologics
Join this expert-led webinar to learn how to break through common bottlenecks in TCR discovery with practical strategies that help teams move faster and smarter.
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.
Discover how interactive response technology (IRT) is revolutionising the management of cell and gene therapy (CGT) trials by streamlining complex workflows, ensuring regulatory compliance and enhancing patient outcomes.
