An RNA Sequence Is Not a Molecular State: RNA Foundation Models in 2026
RNA models share an alphabet, not an estimand: choose them by molecular object, output, information inputs, evidence class, and artifact contract.
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#computational biology
14 articles tagged with #computational biology.
A DNA Likelihood Is Not a Functional Assay: Genomic Foundation Models in 2026
A reported genomic-model comparison is auditable only when its tokenisation, strand rule, biological target, evaluation split, and artifact terms are explicit.
A Protein Embedding Is Not an Explanation: Protein Language Models in 2026
A practical guide to choosing, extracting, adapting and validating protein language-model representations without mistaking a system score for biological generalisation.
An Antibody Sequence Is Not a Research Plan: Antibody Foundation Models in 2026
A task-first guide to antibody representations, structure prediction, CDR design, humanisation and developability, with the evidence and reproducibility checks that model scores leave out.
A FASTA File Is Not a Specification: Protein Structure Prediction in 2026
Suppose somebody hands you a FASTA file and asks for “the structure.” First decide whether the target is one chain, a protein assembly or a mixed complex containing ligands or nucleic acids.
MIMIC: One 1B Model Across the Central Dogma, and Why Multimodality Beats Scale
A 1-billion-parameter model conditioned on RNA chemical-probing reactivity folds a held-out transcript to an F1 of 0.987 against an experimentally guided reference.
The Historical Accident That Split Drug Design in Two (And the Contrastive Model That Reunites It)
Structure-based and ligand-based drug design evolved as separate fields solving the same problem. ConGLUDe, a contrastive geometric learning model, unifies both approaches and outperforms specialist methods on realistic benchmarks without requiring pre-defined binding pockets.
How AlphaGenome Tackles Variant Effect Prediction
AlphaGenome processes 1 million DNA base pairs to predict variant effects across 7,000+ genomic tracks in one second, outperforming specialized models on 25 of 26 VEP benchmarks.
How AlphaGenome Models Gene Regulation: 2D Embeddings, Splicing, and the Race to Read Non-Coding DNA
A technical look at AlphaGenome's architecture, its 2D pairwise embeddings for splicing prediction, and what the model means for clinical variant interpretation.
EDEN: 28 Billion Parameters for Programming Biology
Basecamp Research's EDEN model trains on proprietary environmental metagenomics to design gene-insertion enzymes, antimicrobial peptides, and synthetic microbiomes -- all validated in the wet lab.
When 62 Days of Compute Becomes 3: Diffusion Models as Fast Surrogates for Agent-Based Biological Simulations
How generative diffusion models can serve as fast surrogates for expensive biological simulations, achieving 22x speedup while preserving the stochastic diversity that makes these models scientifically useful.
Biological World Models: The Projects You're Not Building (But Should Be)
Why computational biologists should stop building embeddings and start building simulators, with three tractable project ideas you can implement today using flow matching, Neural ODEs, and cell fate trajectory modeling.
Biology's Secret Weapon: Physics-Based Benchmarks for Training RL Agents
Why biological systems offer the ideal training ground for reinforcement learning: automated verification through physics, not human judgment. From protein design with AlphaFold to RNA folding with ViennaRNA, biology provides the verifiable inverse problems that RL needs at scale.
The Dark Matter of Biology: What Machine Learning Reveals About the Invisible Proteome
Machine learning is illuminating biology's hidden half: intrinsically disordered proteins and RNA structures that traditional methods could never capture. But can we trust what we're seeing?
