Biomolecular Structure Predictionv1.0.8

Immuno-oncology and biologics discovery teams use Chai-1 and AlphaFold3 to predict how engineered monoclonal antibodies or nanobodies dock onto a specific antigen epitope. This supports affinity maturation campaigns, epitope binning, and rational design before expensive mammalian expression and SPR/BLI testing.

Medicinal chemists use the covalent doc variants (Chai-1-Covalent, Boltz-2-Covalent, AlphaFold3-Covalent) to model irreversible covalent adducts between acrylamide or chloroacetamide warheads and target cysteine residues on kinases like KRAS G12C or BTK. This informs warhead geometry and linker optimization, reducing trial-and-error synthesis cycles.

RNA-focused biotech companies use Boltz-2 to predict protein-RNA and ligand-RNA interactions for noncoding RNA targets such as riboswitches, viral frameshifting elements, or spliceosomal RNAs. Structural hypotheses guide ASO design and small-molecule binders for undruggable RNA targets.

Gene-editing and synthetic biology labs predict protein-DNA complexes involving CRISPR-Cas effectors, zinc fingers, TALEs, or transcription factors to assess off-target binding geometry. Combining AlphaFold3 for DNA complexes with Chai-1 for alternate hypotheses provides structural confidence before functional assays.

University labs and core facilities use the SciMiner-hosted Chai-1, Boltz-2, and AlphaFold3 APIs to generate ensemble structures without managing GPU clusters. Students and postdocs submit protein-ligand or mixed-complex jobs and receive shareable result URLs for inclusion in manuscripts and collaborative reviews.

SciMiner provides free API keys tied to limited compute credits, enough for academic trials or proof-of-concept runs. Users upgrade to paid tiers when they need higher throughput, longer MSA searches, or covalent-specialized endpoints, creating a smooth conversion path from researchers to production biotech customers.

Pharma and large biotech companies license the structure-prediction skill as part of a broader SciMiner platform that adds team workspaces, audit logging, private data residency, and SLA-backed GPU capacity. The skill becomes an internal tool integrated into existing computational chemistry pipelines.

Contract research organizations and bioinformatics software vendors embed the skill behind their own interfaces to offer structure prediction without building model infrastructure. SciMiner charges per-call rates or revenue-share agreements while the partner keeps its brand and customer relationship.