BestHub
Sign in
Tagged articles
10 articles
Page 1 of 1
Machine Heart
Machine Heart
Apr 29, 2026 · Artificial Intelligence

How a Nobel‑Lab‑Born Team Uses a World Model to Break Modal Islands in AI‑Driven Molecular Design

The article examines ODesign, a full‑modal generative framework from a team emerging from David Baker’s Nobel‑winning lab, which unifies protein, DNA, RNA, small‑molecule and ion representations to enable cross‑modal learning, dramatically improve design throughput, and achieve nanomolar‑picomolar affinities in wet‑lab validation.

AI4BioODesignRNA design
0 likes · 13 min read
How a Nobel‑Lab‑Born Team Uses a World Model to Break Modal Islands in AI‑Driven Molecular Design
HyperAI Super Neural
HyperAI Super Neural
Mar 30, 2026 · Artificial Intelligence

MIT Introduces VibeGen: The First End‑to‑End Dynamic Protein Generator Linking Sequence and Vibration

MIT and Carnegie Mellon unveil VibeGen, an agentic end‑to‑end de novo protein design system that jointly generates amino‑acid sequences and predicts low‑frequency normal‑mode dynamics, achieving stable, novel structures that faithfully reproduce target vibrational amplitudes and demonstrating high‑precision, diverse, and novel protein engineering capabilities.

Deep LearningVibeGenlanguage diffusion model
0 likes · 13 min read
MIT Introduces VibeGen: The First End‑to‑End Dynamic Protein Generator Linking Sequence and Vibration
HyperAI Super Neural
HyperAI Super Neural
Jan 28, 2026 · Artificial Intelligence

EDEN Models Leverage a Million Species and 10‑Billion‑Scale Gene Data to Reach SOTA Genome & Protein Prediction

The EDEN series of foundation models, trained on the massive BaseData macro‑genomic dataset covering over one million species and 9.7 trillion nucleotides, achieve state‑of‑the‑art genome and protein prediction while enabling functional recombinase design, antimicrobial peptide generation, and synthetic microbiome construction with minimal task‑specific data.

AISynthetic Biologyfoundation-models
0 likes · 15 min read
EDEN Models Leverage a Million Species and 10‑Billion‑Scale Gene Data to Reach SOTA Genome & Protein Prediction
HyperAI Super Neural
HyperAI Super Neural
Nov 28, 2025 · Artificial Intelligence

Weekly AI paper roundup: protein design, open‑source agent, HunyuanOCR, Olmo 3

This weekly roundup highlights five recent AI papers—including HumanSense for multimodal LLM evaluation, JAM‑2 for de novo antibody design, the open‑source Olmo 3 language models, the Lumine generalist 3D agent, and the lightweight HunyuanOCR vision‑language model—summarizing their core contributions, results, and links.

Multimodal LLMOCRgeneralist agents
0 likes · 6 min read
Weekly AI paper roundup: protein design, open‑source agent, HunyuanOCR, Olmo 3
Data Party THU
Data Party THU
Oct 11, 2025 · Artificial Intelligence

How RFdiffusion2 Revolutionizes Protein Design with Sequence‑Independent Active Sites

RFdiffusion2 introduces a novel deep generative approach that eliminates residue enumeration and sequence indexing, enabling atom‑level protein backbone generation from simple chemical reaction descriptions, achieving a 100% success rate across 41 benchmark cases and providing a step‑by‑step demo on the OpenBayes platform.

RFdiffusion2benchmarkbioinformatics
0 likes · 5 min read
How RFdiffusion2 Revolutionizes Protein Design with Sequence‑Independent Active Sites
Data Party THU
Data Party THU
Aug 28, 2025 · Artificial Intelligence

BackFlip & FliPS: Neural Models for Predicting and Generating Protein Flexibility

BackFlip is an equivariant neural network that predicts per‑residue flexibility from protein backbone structures, while the derived FliPS conditional flow‑matching model generates novel backbones matching desired flexibility distributions, demonstrated through diverse designs validated by molecular dynamics simulations, with code available on GitHub.

bioinformaticsconditional flow modelequivariant neural network
0 likes · 3 min read
BackFlip & FliPS: Neural Models for Predicting and Generating Protein Flexibility
Data Party THU
Data Party THU
Aug 21, 2025 · Artificial Intelligence

How AI Is Unlocking the Design of Proteins That Target Disordered Regions

This article reviews the AI‑driven Logos strategy for designing proteins that can bind intrinsically disordered protein regions, detailing scaffold generation, pocket specialization, RFdiffusion‑based assembly, threading, experimental validation, and its broader impact on drug discovery for diseases such as cancer and Alzheimer’s.

AIProteinMPNNRFdiffusion
0 likes · 14 min read
How AI Is Unlocking the Design of Proteins That Target Disordered Regions
AI Frontier Lectures
AI Frontier Lectures
Mar 11, 2025 · Artificial Intelligence

How Stochastic Differential Equations Power Modern Generative AI Models

This article explains how recent MIT research uses stochastic differential equations to model diffusion and flow processes, defines training objectives, explores conditional guidance, compares U‑Net and diffusion transformers, addresses memory challenges with latent diffusion, and surveys applications ranging from robotics to protein design.

Diffusion ModelsLatent DiffusionRobotics
0 likes · 26 min read
How Stochastic Differential Equations Power Modern Generative AI Models
DataFunSummit
DataFunSummit
Jun 26, 2023 · Artificial Intelligence

Advances in Graph Neural Networks and Graph Representation Learning for Protein Modeling

This article reviews the fundamentals of graph neural networks and graph representation learning, explains why proteins can be modeled as graphs, and surveys recent GNN‑based applications such as structure prediction, function annotation, protein design, and self‑supervised representation learning, concluding with future research directions.

AlphaFold2bioinformaticsgraph neural networks
0 likes · 12 min read
Advances in Graph Neural Networks and Graph Representation Learning for Protein Modeling