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Predict three-dimensional structures of complexes containing proteins, RNA, DNA, and small molecules with Chai-1. Compare candidate structures using confidence scores and clash indicators, or run repeated predictions from Python to evaluate designed binders.

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• Predicting antibody–antigen complex structures
• Evaluating protein–ligand binding structures
• Reassessing designed binders

Review a paper’s complete figure set against its manuscript and captions. Develop a supported central claim, propose figure ordering and content changes, and pass human-reviewed revisions to figure-composer while preserving figures that need no changes.

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• Reviewing the narrative across a paper’s full figure set
• Strengthening Figure 1 within the available evidence
• Identifying panel moves, missing analyses, and supplement material

Design protein sequences while accounting for bound small molecules, nucleic acids, or metals. Use LigandMPNN to redesign binding pockets or metal-coordinating sites, and export sequence files alongside PDB structures with the designed sequences applied.

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• Redesign a binding pocket around a small molecule or cofactor
• Design residues around a metal-coordinating site
• Export designed sequences as FASTA and PDB files

Predict patterns of gene expression and other genomic measurements from DNA sequences with Borzoi. Compare reference and altered sequences to estimate regulatory effects, generate predicted coverage tracks, and prioritize variants in regions that do not encode proteins.

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• Comparing predicted effects before and after a DNA variant
• Generating predicted coverage tracks for a genomic region
• Prioritizing variants in non-coding regions

Prepare scientific figures for papers, reports, and saved deliverables by checking data fidelity, chart choice, labels, axes, and color consistency. Verify exported figures for overlapping text and readability while keeping fonts, palettes, and framing configurable.

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• Preparing scientific figures for a paper or report
• Checking excluded data and summary statistics
• Improving labels, axes, and color consistency

Use scGPT to convert single-cell gene expression data into numerical representations of cells and genes. These representations support clustering, data integration, cell-type annotation, and analyses of gene perturbations or regulatory relationships.

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• Creating cell representations from AnnData
• Preparing single-cell data for clustering and integration
• Annotating cell types with or without fine-tuning

Convert protein sequences into numerical features with Meta AI's ESM-2 for machine learning. Extract features for individual amino acids or whole sequences, score mutations, and predict which positions in a protein may contact each other.

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• Extracting protein features for machine learning
• Scoring the effects of amino acid mutations
• Predicting contacts from protein sequences

Manage Python and R packages for notebook work. Resolve missing-package errors, check installed versions, install into the selected runtime, and restart the kernel when needed, with guidance on using separate environments for different dependency requirements.

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• Resolve missing-package errors in a notebook
• Check installed package versions
• Separate project dependencies into a named environment

Prepare reproducible setup and removal instructions for software environments on Open-Science SSH hosts, including Slurm clusters. Check available tools, define named activation settings, and validate the environment where jobs run after the user installs it.

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• Preparing a remote environment for required packages
• Making job environment activation repeatable
• Checking software and GPU operation on Slurm nodes

Inspect available APIs in Open-Science and find files, session status, and agent conversations within the current project. Check which host features are available before using them, and trace the inputs behind generated file versions.

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• Check which host APIs are available
• Find generated files and uploaded versions
• Inspect session status and conversation history

Use Boltz-2 to predict structures of complexes containing proteins, DNA, RNA, and small molecules. Obtain structure files and confidence scores to assess designed binders, with optional predictions of binding strength between a protein and a small molecule.

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• Assessing a designed binder against its target
• Predicting a protein and small-molecule complex
• Predicting complexes containing DNA or RNA

Predict structures of protein, nucleic acid, and small-molecule complexes with ESMFold2, using single sequences or aligned related sequences. The skill also covers ESMC models for numerical sequence representations, mutation scoring, and predictions of contacts between residues.

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• Predicting complex structures from single sequences
• Assessing designed binders with ESMFold2-Fast
• Predicting structures using aligned related sequences

Use Evo 2 to score DNA sequences, turn genomic regions into numerical representations for classification, and generate sequences from a supplied prefix. Compare reference and variant sequences or assess coding and regulatory regions across species.

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• Comparing scores before and after a DNA variant
• Preparing numerical features for genomic region classification
• Generating DNA from a supplied prefix

Design amino acid sequences from a protein backbone stored in a PDB file using ProteinMPNN. Generate multiple candidates, redesign selected chains while preserving specified residues, and vary sampling settings to prepare sequences for downstream structure prediction.

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• Designing sequences for RFdiffusion backbones
• Redesigning a chain while keeping interface residues fixed
• Generating candidates across multiple sampling temperatures

Design amino acid sequences from a protein backbone using SolubleMPNN, a model trained on soluble protein structures. Generate candidates with a solubility bias for redesigning aggregation-prone proteins or preparing an E. coli expression screen.

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• Redesign protein candidates that aggregate
• Adapt membrane-adjacent folds for soluble expression
• Generate candidates for an E. coli expression screen

Predict how a small molecule may sit within a protein using structural inputs. Generate ranked 3D poses without defining a search region, then use those poses as starting points for further evaluation.

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• Predicting ligand poses from a protein structure and SMILES
• Generating ranked poses for a small fragment library
• Preparing starting poses for downstream rescoring

Analyze single-cell RNA sequencing data with scVI and scANVI. Combine experimental batches while correcting technical differences, produce compact cell features for clustering, assign cell types using reference labels, and assess differences in gene expression between groups.

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• Integrating single-cell RNA sequencing batches
• Preparing cell features for clustering
• Assigning cell types from reference annotations

Find scientific papers, check their citation details and retraction notices, and synthesize the evidence into a readable review. Identify landmark studies, compare findings or methods, and explain disagreements and research gaps with links to supporting papers.

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• Finding the original paper behind a research topic
• Synthesizing evidence across multiple studies
• Comparing research methods and their trade-offs

Predict structures for single proteins or protein complexes with AlphaFold2 through ColabFold. Use confidence scores and comparisons with a designed structure to assess whether a designed sequence folds as intended.

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• Predicting a single protein's structure from its sequence
• Predicting structures of protein complexes
• Checking designed sequences against a target structure

Create and manage focused Specialist agents and Skills through conversation. Draft agent roles, review available Skills and Connectors, configure access, and manage changes, with Skill creation and publishing handled by the application's internal skill-creator.

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• Creating a Specialist for a focused role
• Updating an agent's Skills and Connectors
• Creating, revising, and publishing Skills

Predict structures of complexes containing proteins, DNA, RNA, and ligands with OpenFold3, an AlphaFold3 reproduction with publicly available model weights. Prepare JSON inputs, generate candidate structures, and inspect confidence scores and molecular clashes.

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• Predicting structures of protein–ligand complexes
• Predicting complexes containing DNA or RNA
• Comparing confidence scores across candidate structures

Build a structured research dossier for a defined patient population, covering epidemiology, disease biology, current treatment, regulatory precedents, and major clinical trials. Receive a cited report and structured data to inform therapeutic development and trial planning.

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• Characterizing a target patient population
• Reviewing current treatments and unmet needs
• Researching regulatory precedents and landmark trials

Run demanding scientific work on SSH compute hosts using direct execution or Slurm, a cluster job scheduler. Choose an enabled host, submit background jobs, check progress, collect outputs, and continue with analysis and artifact publication.

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• Running long GPU inference jobs on a remote host
• Submitting scientific workloads through Slurm
• Collecting and analyzing completed job outputs

Turn a recurring task into a reusable Open-Science Skill, or revise an existing one. Guide package creation, optional testing, improvements to when the Skill activates, and publication after the user reviews and accepts the draft.

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• Turning recurring tasks into reusable Skills
• Revising an existing Skill's instructions and files
• Evaluating outputs with realistic test prompts