All Machine Learning (ML) articles
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NewsAutomated MRI pipeline standardises preclinical stroke damage measurement
An automated imaging pipeline developed at USC’s Stevens Neuroimaging and Informatics Institute can measure stroke-related brain tissue damage from MRI scans with accuracy matching human experts, offering a scalable, standardised tool for preclinical drug evaluation across multi-site research networks.
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ArticleWhy single-cell foundation models have underdelivered and what drug discovery needs instead
Foundation models have delivered breakthroughs in protein biology, but single-cell models have struggled to match them. What is holding them back?
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NewsWhat Google’s AI talent exodus could mean for future drug discovery
The departure of leading AI researchers from Google to found Discovery Loop raises important questions about the future direction of artificial intelligence in scientific research and drug development.
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ArticleGene therapy’s biggest challenge is the cell’s own defence mechanisms
What if one of gene therapy’s biggest obstacles isn’t delivery, but the body’s own cells? Discover why DNA silencing is emerging as a major challenge for long-lasting genetic medicines.
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NewsInsilico Medicine launches AI drug discovery benchmarking platform
Insilico Medicine has unveiled a benchmarking platform designed to assess whether AI models can perform genuine drug discovery tasks, using decontaminated real-world datasets and proprietary validated programmes to move beyond inflated benchmark scores.
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NewsResearchers develop AI system to accelerate tuberculosis drug discovery
Researchers at Texas A&M have developed AI-driven platforms to help scientists navigate the bottlenecks of tuberculosis drug discovery, from eliminating nuisance compounds to unlocking years of archived research data.
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ArticleAI’s promise and practical limits in drug discovery
From ChatGPT to AI agents and world models, where is AI really heading in drug discovery? Dr Raminderpal Singh explains what researchers should focus on now.
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ArticleAI’s real value in drug discovery may be choosing the right experiment
AI is becoming more capable, but its value still depends on the data, questions and decisions behind it. Where is it genuinely improving drug discovery and where do the limitations remain?
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NewsAI and lab techniques accelerate tuberculosis drug discovery
Researchers at UMass Amherst have combined high-throughput laboratory screening with an AI neural network to identify compounds capable of breaching the protective outer membrane of Mycobacterium tuberculosis, potentially accelerating the search for new TB therapeutics.
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NewsLargest chemical reactions database launched to boost AI drug discovery
Researchers at the University of Michigan have assembled a database of more than 50,000 chemical experiments, offering AI systems an unprecedented resource to accelerate drug discovery and reduce reliance on scarce precious metal catalysts.
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NewsLabGenius and LG Chem partner on AI-driven cancer antibodies
LabGenius Therapeutics has partnered with LG Chem to develop next-generation multispecific antibodies targeting solid tumours. The collaboration combines AI-driven drug discovery with oncology development expertise to identify therapeutics with improved selectivity and reduced toxicity.
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NewsAI-powered $6M project targets new Alzheimer’s treatments
A $6 million NIH-funded collaboration between Indiana University School of Medicine and Luddy School of Informatics aims to deploy AI and machine learning to identify promising Alzheimer’s drug candidates, screening billions of compounds to overcome traditional discovery bottlenecks.
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ReportAI in Drug Discovery: Progress, Limits and What Comes Next
AI has attracted enormous investment across drug discovery, but major questions still remain around validation, reproducibility and real-world application. In our latest Beyond the Lab report, experts discuss where the technology is starting to influence discovery workflows – and where limitations continue to slow adoption.
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ArticleFrom chemist to AI agent builder: inside the rise of agentic AI in drug discovery
Dr Raminderpal Singh speaks with Dr Srijit Seal about why specialised AI agents are outperforming general-purpose models in drug discovery and what a new consortium paper shows about their use in practice.
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ArticleMachine learning identifies biological signals linked to emotional hunger
Researchers at Phenomix Sciences are using machine learning and genetic risk scoring to investigate emotional hunger, an obesity phenotype linked to emotional and reward-driven eating behaviours. Dr Timothy O’Connor discusses how the approach could improve patient stratification, obesity research and treatment selection.
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NewsAI platform identifies novel gp130 inhibitor for colorectal cancer
An AI-assisted drug discovery platform using transfer learning has identified a promising gp130 inhibitor for colorectal cancer.
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NewsAI identifies 23 antiviral candidates for Bundibugyo Ebola strain
US researchers have deployed artificial intelligence and molecular docking software to identify 23 antiviral compounds with potential activity against Bundibugyo Ebolavirus, as the rare strain continues to spread in the Democratic Republic of Congo with a fatality rate approaching 40 percent.
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NewsOutSee wins Longitude Prize award for ALS target discovery
OutSee has secured a Discovery Award from the Longitude Prize on ALS, providing £100,000 in funding and access to genomic data from 9,000 patients. The company will deploy its AI-driven Nomaly platform to identify novel therapeutic targets for amyotrophic lateral sclerosis over a nine-month research programme.
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NewsAI-designed viral vectors achieve 50-fold brain enrichment over AAV9
WhiteLab Genomics has presented preclinical data showing that viral vectors designed using artificial intelligence achieved approximately 50-fold higher DNA enrichment in the brain compared to AAV9, with no detectable liver signal following intravenous administration in mice.
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NewsAI system transforms weak antibiotics into powerful treatments
University of Pennsylvania researchers have developed ApexGO, an AI system that refines imperfect antibiotic candidates through calculated modifications rather than database screening.