get more out of the next run than the last one.
practical notes from people actually running these workflows, grouped by task. add one if you have found something that works.
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use batch processing for multiple structures
when analyzing multiple proteins or compounds, submit them as a batch job rather than individual submissions. this reduces queue time and provides better resource utilization. you can upload up to 100 structures in a single batch job.
always check confidence scores in predictions
protein folding and other ai predictions include confidence scores. regions with low confidence (<70) may need experimental validation. use these scores to prioritize which predictions to trust for downstream analysis.
optimize docking parameters for your target
default docking parameters work well for most cases, but fine-tuning can improve results. adjust grid size based on binding site dimensions, and consider using multiple conformations for flexible ligands.
hand repetitive work to an agent
if you're running similar analyses repeatedly, drive them from something other than the browser. Ivy answers a curl post to /api/v1/orchestrate, the ivybloom cli, and any mcp client alike — so a coding agent can run a parameter sweep or a high-throughput screen and hand back the same artifacts you'd get by hand. signed-in runs from the web app, the terminal, curl or MCP all write to the same hash-chained record.
set appropriate collaboration permissions
use granular permissions when sharing projects. give 'view' access for consultants, 'edit' for active collaborators, and 'admin' only to co-pis. this protects your data while enabling collaboration.
cross-validate results with multiple methods
when possible, validate computational predictions with multiple approaches. for example, use both esmfold and alphafold for critical structures, or compare docking results from different algorithms.
your parameters are already recorded — use the record
you don't need a lab notebook for run settings. every job stores its parameters, its result and a manifest hash alongside the output. when you write the methods section, pull the run record rather than retyping what you remember — add notes for the reasoning, not for the parameters.