Protein alignment took 20 mins per result. Vector search now scans 5 billion sequences in <1 minute, linking DNA to cellular images.
A life sciences AI company developed a 210-billion parameter foundation model family to unify proteins, DNA, RNA, and scientific text into a single framework for drug development.
Protein structure prediction relied on alignment methods that took 10–20 minutes per result, making it impossible to scale to billions of sequences....
“If it’s not open source, there’s likely no room for such customizations, which doesn’t fit our scenarios.”
Life science AI foundation models for drug discovery and protein design.
Vector database platform for building and scaling AI applications.
BioMap's Biological data search is part of this use case:
Related implementations across industries and use cases
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
Standard parsers couldn't read scientific charts. AI now extracts visuals into text, making hidden data searchable.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Insurance bottlenecks delayed critical therapies for months. Now, AI verifies coverage, freeing care teams to guide patients to treatment.
Manual scheduling trapped recruiters in logistics. AI now automates interview coordination, freeing teams for strategic work.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Protein alignment took 20 mins per result. Vector search now scans 5 billion sequences in <1 minute, linking DNA to cellular images.
A life sciences AI company developed a 210-billion parameter foundation model family to unify proteins, DNA, RNA, and scientific text into a single framework for drug development.
Protein structure prediction relied on alignment methods that took 10–20 minutes per result, making it impossible to scale to billions of sequences....
“If it’s not open source, there’s likely no room for such customizations, which doesn’t fit our scenarios.”
Life science AI foundation models for drug discovery and protein design.
Vector database platform for building and scaling AI applications.
BioMap's Biological data search is part of this use case:
Related implementations across industries and use cases
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
Standard parsers couldn't read scientific charts. AI now extracts visuals into text, making hidden data searchable.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Insurance bottlenecks delayed critical therapies for months. Now, AI verifies coverage, freeing care teams to guide patients to treatment.
Manual scheduling trapped recruiters in logistics. AI now automates interview coordination, freeing teams for strategic work.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.