Leash Biosciences

Introduction

Leash Biosciences is an online platform that predicts how small-molecule compounds bind to protein targets in just a few clicks.

How to use Leash Biosciences

1. Create an account with your institutional email and confirm the sign-up link.
2. Upload a molecule file (SDF, MOL2, or SMILES); drag-and-drop works well.
3. Select a target family or paste a UniProt ID if you have a specific protein in mind.
4. Hit “Run Prediction” and watch the progress bar; results usually appear within one minute.
5. Explore the heat-map view, download the docking report, and share the session link with colleagues.

Our Experience with Leash Biosciences

Advantages

• Speed that matches tight lab schedules: during our trial run, a library of 500 compounds finished in under four minutes, saving hours compared with local docking software.
• Clear visual output: the colour-coded affinity map helped us spot off-target risks without digging through spreadsheets.
• Collaboration features: every project receives a unique URL, so our medicinal chemist in Oxford and the structural biologist in Cambridge both viewed the same plots without extra file transfers.
• Responsive support team: we flagged a mislabeled residue and received a corrected build the next morning.
• Fair pricing: the free tier covers small pilot studies, and the pay-as-you-go tokens let us scale up only when funding allows.

Drawbacks

• Limited external links: right now the results page only exports to CSV; direct push to KNIME or Pipeline Pilot would help streamline larger workflows.
• Occasional queue time at peak hours: early afternoon GMT, our jobs waited roughly ten minutes before starting, which interrupted an on-the-fly brainstorming session.
• No built-in SAR notebooks: we had to copy structures into a separate electronic lab notebook, adding extra steps for annotation.
• GPU requirement for local runs: offline mode demands a modern NVIDIA card, leaving older desktop boxes out of the loop.

Closing Thoughts

Leash Biosciences trimmed repetition in our hit-finding routine and offered a friendly interface, yet it still needs broader integrations and smoother peak-time handling before it can sit at the centre of every discovery pipeline.