Category: Bioinformatics
LLMs and machine learning in biology, drug discovery, genomics, clinical AI, and biosecurity.
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Why AI Has Not Yet Found RNA’s AlphaFold Moment
AlphaFold solved protein folding. RNA structure prediction remains unsolved. Here is the specific chemistry and data gap that makes it harder.
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Why R Still Beats Python in Clinical Biostatistics
SAS still dominates regulatory submissions, but R runs the survival models, mixed-effects analysis, and increasingly the FDA-facing tables behind it.
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LLMs in Veterinary Clinical Practice: What the Evidence Actually Shows
ChatGPT-4.5 scored 90% on feline eye disease cases vs 96.7% for experienced veterinary ophthalmologists and significantly outperformed novices (56-67%). Where LLMs add clinical value in veterinary practice, where…
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AI-Assisted Zoonotic Disease Detection: From SARS to H5N1
H5N1 in US dairy cattle is the live test of AI-assisted zoonotic detection. NGS with AI flags novel pathogens before specific assays exist. What AI surveillance can and…
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One Health and Machine Learning: How AI Bridges Human and Animal Disease Surveillance
Machine learning now integrates electronic health records, social media, wearable sensors, and environmental data to detect outbreaks earlier than traditional systems. The AI4MPOX-SN initiative in Senegal and the…
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Generative AI for Small Molecule Drug Discovery: How It Works and What the Evidence Shows
Generative AI is producing novel molecules from VAEs, GANs, and diffusion models. Machine learning virtual screening shows 75% hit validation rates against 106M-compound libraries. Why no AI-designed drug…
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AI in Digital Pathology: What Computational Pathology Can and Cannot See
An NIH multi-institution study in Lancet Oncology classified 52 CNS tumor types from tissue images at 80% accuracy across 5,516 test samples. A Cancer Science paper simultaneously documented…
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FDA Clearance for AI Medical Devices: What 510(k), De Novo, and PMA Actually Mean
The FDA has cleared 700+ AI medical devices through 510(k), De Novo, and PMA pathways. A March 2026 European Radiology review documents how the EU AI Act, FDA…
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AI-Driven ADMET Prediction: What the Blind Challenge Results Actually Show
Deep learning beat classical methods for ADME prediction in a 65-team blind challenge at the 2025 OpenADMET competition. An AI-PBPK platform predicted full human pharmacokinetic curves from molecular…
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Poisoning the Medical Brain: RAG Attacks and Security in Clinical AI Systems
Clinical LLMs failed prompt injection at 94% in JAMA testing. RAG systems face a harder attack: poisoned retrieved documents that the LLM cannot distinguish from legitimate sources. How…
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RFdiffusion and ProteinMPNN: How AI Now Designs Proteins From Scratch
RFdiffusion generates protein backbones. ProteinMPNN designs the sequences that fold into them. Together they achieved sub-Angstrom accuracy at influenza binding interfaces. How the two-step pipeline works, why AI-generative…
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What ASL-3 Actually Means: Anthropic’s Biorisk Threshold Explained
ASL-3 is Anthropic’s threshold where models could provide serious uplift on bioweapons with mass casualty potential. What the Virology Capabilities Test actually evaluates, how the 4x novice uplift…
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DNA Synthesis Screening Cannot Keep Up With AI-Designed Sequences
The IGSC DNA synthesis screening standard was built on sequence homology to known pathogens. AI-designed sequences achieve dangerous functions through novel sequences that homology checks cannot recognize. What…
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Evo 2: The Genomic Foundation Model Trained on 9.3 Trillion DNA Bases
Evo 2 from Arc Institute is a 40B-parameter genomic foundation model trained on 9.3 trillion DNA bases spanning all domains of life. How the 128K context architecture works,…
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ESM3: The Protein Language Model That Unifies Sequence, Structure and Function
ESM3 from EvolutionaryScale is a 98B-parameter generative protein language model that reasons across sequence, structure, and function simultaneously. How the VQ-VAE structural tokenization works, what the GFP design…
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AlphaFold 3 in Drug Discovery: Where It Works and Where It Fails
AlphaFold 3 predicts protein backbones well but fails significantly on ligand-binding poses in GPCR drug targets, which represent 33% of approved drugs. Five PubMed-sourced studies covering what works,…
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Radiology Foundation Models: What Merlin, the 22% Hallucination Rate, and ED Fracture Data Tell Us
Stanford published Merlin in Nature: a CT foundation model tested on 44,098 scans across 3 institutions. Meanwhile 22% of AI radiology reports contain factual errors and LLMs miss…
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AI in Radiology: Three Phases and What the Clinical Evidence Shows
Radiology AI has moved through three phases: rule-based CAD, the deep learning benchmark era, and clinical deployment validation. A 556-paper bibliometric analysis and a multicenter thymus CT validation…
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AlphaFold 3: What It Gets Right and Where It Still Fails
AlphaFold 3 improves GPCR backbone prediction over AF2 but shows significant discrepancies in ligand-binding poses for ions, peptides, and protein ligands. PubMed-sourced evidence from the Shanghai Institute of…
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Poisoning the Medical Brain: How RAG Attacks Corrupt Biomedical AI
When the knowledge base is the attack surface. RAG poisoning allows adversaries to redirect medical AI outputs without touching model weights. Five arXiv papers explain the mechanism and…
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