Computational Biology / 2025
Decoding
the Language
of Life
We predict how proteins fold from their amino acid sequences—unlocking the architecture of biology at atomic resolution.
Next-generation
structure prediction
0M+
Structures Predicted
and counting
0
GDT Score
median accuracy
< 0Å
RMSD
atomic precision
0ms
Inference Time
per sequence
Mission
Mission
Mission
Proteins are the machinery of life. Their function is determined by their shape. We are building the tools to understand that shape.
For fifty years, predicting how a protein folds from its genetic sequence was an unsolved grand challenge of biology. Our models have changed that—achieving experimental accuracy without experimental cost.
Press & Recognition
Selected Coverage
"The most significant advancement in computational biology since the sequencing of the human genome."
Dr. Sarah Chen
Editor, Computational Biology
"Helix Lab has fundamentally altered our understanding of what is computationally tractable in structural biology."
James Rothwell
Senior Editor
"A rare convergence of theoretical rigor and engineering excellence. This is what the future of science looks like."
Technology Quarterly
Special Report
Active Research
Multi-Sequence Alignment Transformer
Evolutionary covariance captured through attention mechanisms over aligned homologous sequences.
Diffusion-Based Refinement
Iterative denoising for atomic coordinate generation with learned priors over protein geometry.
Protein-Ligand Co-Folding
Joint prediction of protein structure and small molecule binding poses for drug discovery.
Insights & Analysis
Helix Lab Research
The $400B Opportunity in Computational Drug Discovery
As traditional pharmaceutical R&D faces diminishing returns, computational approaches are emerging as the primary lever for productivity gains. We estimate that AI-driven protein structure prediction will reduce average drug development timelines by 40% within the decade.
$0B
Market Opportunity
0%
Timeline Reduction
Beyond AlphaFold: The Next Frontier of Structural Biology
While single-chain structure prediction has reached near-experimental accuracy, the industry's attention is shifting toward protein dynamics, multi-state ensembles, and the prediction of transient interactions—problems that require fundamentally different approaches.
0%
Accuracy Achieved
∞
Unsolved Problems
Key Findings
Structure prediction accuracy has plateaued at ~92% GDT-TS for single chains, shifting focus to dynamics.
Pharma R&D spending reached $238B globally in 2024; computational methods capture only 4% of this budget.
Time from target identification to clinical candidate has compressed from 4.5 years to 2.8 years for AI-native pipelines.
The protein design market is projected to grow at 34% CAGR through 2030, driven by therapeutic applications.
The Helix Journal
Research Notes & Technical Writing
View ArchiveID
Title
Authors
Category
Date
On the Geometry of Learned Representations in Protein Space
We investigate the topological properties of embedding spaces learned by large protein language models, revealing unexpected structure in how evolutionary information is encoded.
E. Nakamura, M. Lindqvist, A. Petrov
Engineering Thermostability Through Computational Mutation Scanning
A systematic approach to identifying stabilizing mutations using physics-informed neural networks, validated across 47 enzyme families with average Tm improvements of 12°C.
S. Okonkwo, J. Park, R. Vasseur
The Attention Landscape of Multi-Chain Complexes
Visualizing and interpreting cross-chain attention patterns in protein complex prediction reveals biologically meaningful interface residue identification.
A. Petrov, E. Nakamura
Benchmarking Diffusion Models for Antibody Design
A comprehensive evaluation of generative approaches for CDR loop generation, establishing new baselines for developability prediction and binding affinity.
M. Lindqvist, S. Okonkwo, J. Park
On the Geometry of Learned Representations in Protein Space
We investigate the topological properties of embedding spaces learned by large protein language models, revealing unexpected structure in how evolutionary information is encoded.
E. Nakamura, M. Lindqvist, A. Petrov
Engineering Thermostability Through Computational Mutation Scanning
A systematic approach to identifying stabilizing mutations using physics-informed neural networks, validated across 47 enzyme families with average Tm improvements of 12°C.
S. Okonkwo, J. Park, R. Vasseur
The Attention Landscape of Multi-Chain Complexes
Visualizing and interpreting cross-chain attention patterns in protein complex prediction reveals biologically meaningful interface residue identification.
A. Petrov, E. Nakamura
Benchmarking Diffusion Models for Antibody Design
A comprehensive evaluation of generative approaches for CDR loop generation, establishing new baselines for developability prediction and binding affinity.
M. Lindqvist, S. Okonkwo, J. Park
Showing 4 of 47 publications. All papers are open access under CC BY 4.0.
Events & Talks
Upcoming Appearances
ICML 2025: Workshop on Geometric Deep Learning
Dr. Vasquez presents our latest work on equivariant neural networks for protein structure prediction.
July 21-27, 2025
Vancouver, Canada
Structural Biology Summit
Join us for a deep dive into the future of computational approaches to understanding molecular machinery.
September 12, 2025
Cambridge, UK
Open Office Hours: AI in Drug Discovery
Monthly virtual sessions where our research team discusses applications of structure prediction in pharmaceutical R&D.
Monthly, First Thursday
Virtual
Leadership
Dr. Elena Vasquez
Director, Structural Prediction
Cambridge
Dr. James Chen
Lead, ML Infrastructure
San Francisco
Dr. Amara Okafor
Head, Drug Discovery
London
Dr. Kenji Tanaka
Principal Scientist
Tokyo
We are hiring.
Join a team of world-class researchers working at the frontier of computational biology. Remote-first, research-driven, impact-focused.