3. Data, Science & AI

Intelligently parses Excel/CSV files by analyzing structure to select optimal processing mode (Pandas for standard tables, HTML for complex reports) balancing speed and accuracy.

Generates de novo protein backbones using RFdiffusion, a diffusion-based generative AI model for protein structure design.

Designs protein sequences using ProteinMPNN inverse folding AI, optimizing for stability, expression, and residue-specific requirements.

Validates protein designs and predicts structures using AlphaFold2, including confidence metrics and multi-chain complexes for scientific research.

Provides experimental guidance for SPR and BLI binding kinetics studies, including troubleshooting, data interpretation, and platform selection.

End-to-end protein binder design with AlphaFold2 validation, enabling joint backbone and sequence optimization for high experimental success.

Ranks protein binder designs using ipSAE for experimental testing, prediction, and comparison of structure predictions.

Guides selection of protein binder design tools (e.g., BoltzGen, BindCraft, RFdiffusion) by analyzing trade-offs and target-specific requirements for scientific campaigns.

Optimizes protein sequences for solubility and high-yield E. coli expression, reducing aggregation and inclusion body formation.

Retrieves protein sequences, annotations, domain boundaries, homologs, and variants from UniProt for scientific research and analysis.

Fetches and analyzes protein structures from RCSB PDB, enabling tasks like downloading by ID, searching similar structures, and extracting chains for scientific research.

Generates all-atom protein structures using a diffusion model, enabling side-chain aware design, ligand binding, and precise geometries via YAML configuration.

Initializes protein design tool environment, resolving common setup issues like missing files, authentication errors, and command not found for new users.

Provides protein sequence embeddings and scoring using ESM2 for PLL computation, clustering, and variant effect prediction.

Provides end-to-end guidance for protein design projects, including tool selection, QC thresholds, and resource planning.

AI-driven protein sequence design for ligand binding, active site optimization, and cofactor interactions.

Predicts molecular structures including protein-protein and protein-ligand complexes using Chai-1, an AI foundation model, as an alternative to AlphaFold2.

Predicts protein complex and protein-ligand structures using open-source Boltz AI models as an alternative to AlphaFold2.

Enables structural similarity searches in PDB/AFDB databases using Foldseek for homology detection, clustering, and remote homolog identification in protein structures.

Provides research-backed quality control metrics and thresholds for protein design evaluation, including binding, expression, and structural metrics.

Switches between quality, balanced, and budget model profiles for kata agents to optimize AI performance and cost.

Generates usage reports and token optimization insights for MiniMax AI services, enabling efficient resource management.

Sets up Databricks agent development environment including authentication configuration and initial project setup for data science and AI workflows.

Discovers available tools and resources in Databricks workspace, including vector search indexes, Unity Catalog functions, and MCP servers for data and AI workflows.