bioinformatics

This paper introduces a graph neural network method to predict transmembrane protein topology from 3D structures, demonstrating promising results by leveraging atom-level embeddings from AlphaFold without pre-trained weights.

The paper introduces BaseCamp, an agentic AI framework that automates the decision layer in end-to-end DNA sequencing pipelines by using specialized AI agents for tasks like quality control, alignment, and variant calling.

NVIDIA, Google DeepMind, and other organizations have released predicted 3D structures for over 2,800 viral proteins using AlphaFold2 and NVIDIA BioNeMo tools to help researchers prepare for future pandemics, making the data openly available.

MT-ProtBERT is a multi-task learning model for classifying intrinsically disordered proteins under data scarcity, integrating self-supervised and biochemistry-informed tasks to outperform existing methods like PARROT.

The paper proposes CorrFlow, a correlation-guided flow matching framework for predicting spatial transcriptomics from histology images, explicitly modeling gene-gene dependencies to improve biological coherence in generated profiles.

MIRCID is a framework that infers hub-miRNAs to enhance drug mechanism-of-action modeling by comparing gene expression with inferred transcription factor activity and miRNA expression, achieving improvements in pathway classification and similarity-based retrieval.

Adaptyv Bio and Anthropic have partnered to launch a global Protein Design Competition, using AI to design new drug candidates with over $2 million in support and open data sharing.

The author criticizes strict AI safety measures in virology research, comparing Claude's restrictions to Deepseek V4.1's permissiveness, and advocates for open-source AI to unblock scientific progress.

Support for mmCIF, PDB, fasta, and fastq formats has been added to the Hugging Face datasets library, which should significantly accelerate bioinformatics workflows.

Google has developed AlphaGenome, an AI system that evaluates every possible one-base change in genomes to identify functional non-coding DNA sequences, aiding biologists in understanding genetic functions.

Bioinfoysis is a multi-agent harness for bioinformatics that uses persistent runs and artifact-grounded execution to achieve state-of-the-art accuracy on benchmarks like BixBench and LAB-Bench 2.

This paper investigates the impact of prompt engineering on drug toxicity prediction using large language models, finding that natural variance in LLM outputs outweighs prompt fine-tuning, while chemoinformatic feature extraction improves model performance.

Jingtian Zhou builds single-cell and computational frameworks to probe the 3D genome, contributing to major publications in Science, Cell, and Cancer Cell that advance understanding of cell states and diseases.

This paper introduces function-aware masking, a pretraining algorithm for antibody language models that aligns mask placement with functional priors, yielding significant improvements on structure and CDR-related tasks.

The MAMMAL model is an AI model that has outperformed AlphaFold 3 and champions in 9 out of 11 fields, showing promise for accelerating drug discovery, yet it remains largely unnoticed in the community.

Tamarind introduces a molecular AI model router that automatically selects the best model for specific inputs based on benchmarks and input characteristics, rather than relying on average performance.

MIT researchers have developed PottsMPNN, a machine-learning framework that incorporates physical principles to improve protein sequence generation and stability prediction, enabling the design of novel proteins beyond native sequences.

This paper introduces BixBench3, a benchmark for evaluating AI agents on computational biology tasks, revealing that frontier LLMs can reproduce approximately 48% of real research workflows but struggle with large datasets and sequential steps.

Nearly 300 teams have submitted initial models to the Virtual Cell Challenge leaderboard, with current standings based on six metrics this year.

A university researcher criticizes AI companies for restricting biological research capabilities in models like GPT and Claude, and seeks high-reasoning alternatives for bioinformatics work.