Advance Framework for
Research Intelligence
and Unified Model.
Home to research in brain-computer interfaces, neural decoding, genome and protein synthesis, applied machine learning, and cognitive intelligence.
BUILT ON WORLD-CLASS RESEARCH INFRASTRUCTURE
The Bar We Hold
Held to the standard the field itself is judged by
Every researcher at Afrium is trained against the same rigor, methodology, and review standard used at these venues, not a simplified version of it. What counts as real work here is what would count as real work there.
Neural Information Processing Systems
Intl. Conference on Learning Representations
Association for Computational Linguistics
Computer Vision and Pattern Recognition
International Conference on Machine Learning
Additionally, all research preprints are published on arXiv and submitted to Papers With Code for maximum global visibility.
Our Model
The Afrium
Research Plan.
From an ambitious undergraduate to a globally recognized AI researcher.
Rigorous Selection
We filter through hundreds of applications to handpick the top 1% of raw undergraduate talent across universities based purely on technical merit.
The Hackathon
Selected students enter our intensive, high-pressure AI hackathons. Real-world problem statements, strict timelines, and top-tier mentorship.
The Core Cohort
Top performers are chosen. They don't just win a prize; they earn a permanent spot in the Afrium Research Core.
International Research Publication
Top researchers do not just win — they get their work submitted to international AI conferences including NeurIPS, ICLR, and ACL workshops. Pakistan's name on global AI research for the first time.
Research Ecosystem
Everything required to push the boundaries of modern machine learning, provided to our researchers.
Research Infrastructure
We guide researchers to access free compute through Google Colab, Kaggle, and Hugging Face Spaces. Our mentors help researchers maximize available resources and build with the best open-source tools.
The 1% of Undergraduates
We are searching for specific profiles of raw, undeniable talent.
AminoGrapes: Structure-Weighted Evolutionary Scoring
Afrium BioIntelligence Lab has released its first computational biology model. Evaluated on the 31 viral deep mutational scanning assays in ProteinGym v1.3, AminoGrapes outperforms Meta's ESM2-650M on 25 of 31 viral assays with zero fitted parameters on viral data.
Viral proteins evolve rapidly and are sparsely sampled in general sequence repositories, causing large general protein language models to face steep prediction degradation on viral fitness assays.
AminoGrapes introduces an independent-site closed-form scoring formulation that scales sequence-weighted log-odds conservation from multiple sequence alignments by residue solvent burial computed from AlphaFold2 predicted 3D structures.
Spearman ρ across 31 viral targets
Official ProteinGym baseline (rerun 0.2693)
80.6% win rate vs Meta ESM2-650M
Zero fitted weights on viral targets