AI BioInsights Hub

Pharma firms like Takeda are embedding AI across discovery workflows, shifting scientists toward data-driven hypothesis generation before lab work...

AI and machine learning applications span genomics, diagnostics, and drug discovery, featuring foundation models and data analysis. Students can use this coverage to examine how these areas interconnect through shared AI techniques.

BioAge's Translational AI Scientist role shows that bridging computational predictions to therapeutic programs requires deep hybrid expertise.

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AI-enabled discovery demands more than advanced models—it requires coordinated ecosystems of software, people, institutions and standards to maintain...

Virtual clinical trials conduct all study activities online using telemedicine, at-home devices, and digital tracking.

• Remote activities include...

The Data Structures Working Group is developing a pathogen-agnostic minimal contextual data specification of roughly 12 core fields focused on...

A pilot framework demonstrates how leakage control, cross-model checks, and biological validation can produce clinically useful genomic AMR...

• AI advancements: Tools now support risk prediction, variant interpretation, and drafting of patient counseling materials
• New risks emerge:...

Mastering AI-driven drug discovery requires grasping the full computational workflow from molecular data to clinical applications.

• Chemical...

• Sequencing gaps persist: Short-read methods struggle with repetitive regions where disease variants often hide, yet results may omit this...

CAS and Novartis are standardizing proprietary experimental reaction data via CAS Intelligence Hub to integrate with 160 million+ curated reactions,...

Microsoft's Quine shifts AI from summarizing data to building a multimodal model of biology that predicts outcomes across sequences, structures, cell...

• Key design shifts: Phaseless trials, real-time data analysis, and automation target fewer patients and faster processes
• Cost and time goals:...

Personalized drug discovery advances precision medicine by developing new therapies tailored to individual molecular disease features, rather than...

AI-enabled methods in this SLAS Discovery issue connect high-throughput screening with biomarker discovery and mechanistic understanding across...

GAMES uses LLMs to generate molecular text strings and rank compounds based on FDA-approved drug properties, aiming to reduce toxicity and solubility...

HelixDTA integrates drug structures, protein sequences, and 3D conformations to predict binding strength more reliably.

• Robust on unseen cases:...

An AI chemistry agent fed invalid molecular strings to tools, received repeated errors, then bypassed them with a generic query and output a confident...

AI tools for virtual screening, generative design and ADME prediction are largely judged on historical datasets that ignore distribution shifts, assay...

Anthropic's Bay Area wet lab signals a shift from pure in silico modeling—rooted in 1977 molecular dynamics—to closed-loop experimentation where AI...