AI for Biomolecular Structure Prediction Services

Reduce target-to-structure timelines from months to days by integrating our customized AlphaFold2/RoseTTAFold pipelines with your proprietary assay data, enabling rapid functional hypothesis generation.

Achieve atomic-level accuracy for membrane proteins, RNA complexes, and antibody-antigen interfaces through advanced fine-tuning on domain-specific data and multi-template homology modeling.

Generate high-confidence structural models to inform rational drug design and enzyme engineering, reducing costly experimental dead-ends. Integrates directly with our Generative Protein Design Engineering services.

Deploy a managed, version-controlled prediction environment with automated data ingestion, featurization, and result tracking. Ensures full reproducibility for regulatory submissions. Built on our Bio-AI Data Pipeline and MLOps Engineering expertise.

Correlate predicted structures with genomic variants, expression data, and phenotypic screens using our Graph Neural Network Solutions for Biological Networks, uncovering mechanistic drivers of disease or function.

Receive documented model validation reports, uncertainty quantification, and standard operating procedure (SOP) frameworks aligned with emerging FDA/EMA guidelines for AI/ML in drug development, supported by our Bio-AI Regulatory Compliance and Validation team.

Predict antibody-antigen binding interfaces and engineer protein therapeutics with enhanced stability and affinity. Accelerate lead optimization by modeling complex biologics like bispecifics and fusion proteins.

Key Outcome: Reduce experimental screening cycles by 40-60% through prioritized in-silico candidate selection.

Design and optimize enzyme variants for improved catalytic activity, thermal stability, and novel substrate specificity. Enable sustainable manufacturing processes in chemicals, agriculture, and biofuels.

Key Outcome: Engineer enzymes for non-natural reactions, opening new pathways for green chemistry and bioprocessing.

Model viral protein structures, including spike proteins and antigenic variants, to predict immune escape and guide epitope-focused vaccine design. Support rapid response to emerging pathogens.

Key Outcome: Identify conserved epitopes and predict mutational impact to inform next-generation vaccine platforms.

Predict plant protein structures involved in stress response, nutrient uptake, and disease resistance. Enable the design of resilient crops and bio-based crop protection solutions.

Key Outcome: Accelerate the development of traits for drought tolerance and pathogen resistance, shortening breeding cycles.

Design highly specific protein and nucleic acid scaffolds for biosensors and diagnostic assays. Model aptamer-target and nanobody-antigen interactions for point-of-care devices.

Key Outcome: De-risk diagnostic reagent development by ensuring high-affinity, specific binding prior to costly wet-lab synthesis.

Provide scalable, cloud-based prediction pipelines for research consortia and core facilities. We handle the complex MLOps, allowing scientists to focus on biological interpretation and validation.

Key Outcome: Democratize access to state-of-the-art prediction tools without requiring in-house AI engineering expertise. Learn about our robust Bio-AI Data Pipeline and MLOps Engineering services.