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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.
ArticleWhy AI models need patient data to deliver in drug discovery
Despite rapid advances in AI, many drug discovery models still struggle to translate computational predictions into clinical outcomes. Thomas Clozel explains how Owkin is training AI on large-scale patient-derived data while integrating experimental and clinical validation directly into model development.
ArticleTurning GWAS signals into drug targets with scalable CRISPR
Genome-wide association studies have linked thousands of genetic variants to disease, yet most remain disconnected from drug-relevant biology. Neville Sanjana, Professor at New York University and Core Faculty Member at the New York Genome Center, explains how scalable CRISPR screens systematically link noncoding variants to causal genes and therapeutic targets.
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Machine 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.
Why AI models need patient data to deliver in drug discovery
Despite rapid advances in AI, many drug discovery models still struggle to translate computational predictions into clinical outcomes. Thomas Clozel explains how Owkin is training AI on large-scale patient-derived data while integrating experimental and clinical validation directly into model development.
Turning GWAS signals into drug targets with scalable CRISPR
Genome-wide association studies have linked thousands of genetic variants to disease, yet most remain disconnected from drug-relevant biology. Neville Sanjana, Professor at New York University and Core Faculty Member at the New York Genome Center, explains how scalable CRISPR screens systematically link noncoding variants to causal genes and therapeutic targets.
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More Artificial Intelligence
Machine 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.
Why AI models need patient data to deliver in drug discovery
Despite rapid advances in AI, many drug discovery models still struggle to translate computational predictions into clinical outcomes. Thomas Clozel explains how Owkin is training AI on large-scale patient-derived data while integrating experimental and clinical validation directly into model development.
Turning GWAS signals into drug targets with scalable CRISPR
Genome-wide association studies have linked thousands of genetic variants to disease, yet most remain disconnected from drug-relevant biology. Neville Sanjana, Professor at New York University and Core Faculty Member at the New York Genome Center, explains how scalable CRISPR screens systematically link noncoding variants to causal genes and therapeutic targets.
AACR 2026 part 1: AI design, precision biology and the next wave of oncology innovation
At AACR 2026, industry leaders discussed how oncology R&D is moving beyond isolated technological advances towards integrated discovery systems.
When scale meets insight: reinventing SPR for the future of drug discovery
Carterra’s new 48-channel SPR platform reimagines throughput, automation and data quality for modern discovery workflows.
From leaderboards to lab notebooks: AI designs reach preclinical testing
For years, AI drug discovery has been judged on benchmark performance. Now, a set of studies shows what happens when those designs are made and tested in preclinical settings.
The future of academic core labs: scaling operational excellence without increasing staff
The grounds have shifted the foundations of academic core facilities and the current climate demands their strategic agility in order to thrive. Boyd Butler at Molecular Devices reveals how these labs can capitalise on this opportunity to increase value and efficiency.
AI builds dual-action cancer drug targeting PKMYT1
Research published in Nature Communications shows how generative AI can be used to design complex dual-action cancer drug candidates. Insilico Medicine has developed a PKMYT1 degrader that both eliminates the target protein and blocks its activity, demonstrating the growing role of AI in advanced drug discovery.
AI to antibody in days: breaking the wet lab bottleneck via high-throughput integration
AI is accelerating drug discovery at an unprecedented pace. Thousands of antibody candidates can now be designed in silico within hours. The challenge now is keeping experimental workflows fast enough to keep up. High-throughput expression and integrated developability assessment are making it possible to move from sequence to data in days.
Parse Biosciences and bit.bio map transcription factor-driven cell identity
Parse Biosciences and bit.bio have formed an alliance to map transcription factor-driven cell identity using single cell sequencing and causal transcriptomics.
Ginkgo Bioworks launches ADME-One platform for early-stage screening
Ginkgo Bioworks has launched ADME-One, an integrated platform combining high-throughput ADME testing with AI-powered human pharmacokinetic projections.
AI 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.
AI 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.
ISSCR consortium submits recommendations on NAMs to FDA
The ISSCR Consortium on Advanced Stem Cell-Based Models is calling for greater flexibilty from the FDA to accomodate rapidly changing technologies like stem cell-derived systems, organoids and computational approaches.
C-Path launches coalition to advance human-relevant drug discovery
The New Approach Methodologies Developer Coalition brings together technology developers, pharmaceutical companies and regulators in a precompetitive initiative to establish qualification standards for complex in vitro models, microphysiological systems and related human-relevant technologies.
OutSee 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.