Score libraries up to 1,000 compounds, triage HTS hits by disease indication, run multi-target selectivity panels, visualize binding pockets in 3D, generate novel molecules with AI, and get instant scientific insights — all in one browser-based platform. No installation required.
Built for computational chemists, medicinal chemists, and drug discovery teams who need speed and accuracy.
Upload CSV libraries (up to 1,000 compounds) with compound IDs and series tags. Validate SMILES upfront, batch-score with progress, filter by potency and drug-likeness, export ranked results, and bridge generated hits back from the Generate tab.
Screen the same library against every target in an indication — e.g. FLT3, JAK2, BTK for AML. Heatmap with pKd color coding and selectivity gap (Δ) per compound. Export panel CSV for team review.
Score any SMILES string against 4,541 proteins (19,134 pocket structures). Get predicted pKd and binding probability in under a second. Predictions currently derive from the ligand graph and the pocket sequence embedding; the geometric pathway is inactive (see Methods). On 516 structures released after the training data, MAE is 1.22 pKd after recalibration and rank correlation is ρ = 0.39 — useful for coarse triage, not for ordering close analogs. See Validation for the full external benchmark.
Scoring runs through a backend interface with two implementations. Stage 6 is what serves every request today: fast (~0.1 s/compound), externally benchmarked at MAE 1.22 and ρ = 0.39, suited to screening a large library down to a shortlist.
A second backend for Boltz-2 (MIT-licensed, open weights, jointly predicts structure and affinity, approaches free-energy-perturbation accuracy) is implemented but not enabled. It requires GPU hardware we do not currently run, so it reports unavailable and every request falls back to Stage 6. No prediction you have ever received came from it.
We mention it because the two are complementary rather than competing: a co-folding model is too slow to screen a million compounds, and a coarse filter is too weak to rank the survivors. Screening cheaply and then scoring the shortlist accurately is the arrangement that makes both useful. If accuracy matters more than throughput for your work, enabling it is a configuration change rather than a rewrite — get in touch and we will stand it up.
Every scored compound is also matched against 765 curated structural alerts (PAINS, BRENK, NIH). Flagged compounds carry the matching alert names and can be filtered out of a library in one click. This is exact substructure matching on parsed molecules, not a prediction — you can check any individual flag against the published catalogs.
Visualize binding pockets in cartoon, surface, or stick mode. After batch scoring, the top hit overlays in the pocket with contact annotations. Export PNG snapshots, use keyboard shortcuts, or control the view from the pocket assistant.
Use REINVENT4 reinforcement learning to design novel molecules optimized for your target. The generative model learns what VectaBind predicts as high-affinity and explores that chemical space.
Ask questions about the binding pocket, get insights on scored compounds, or say "zoom to active site" and "highlight pocket" to control the 3D viewer with natural language. Powered by Claude.
VectaBind fits directly into your existing drug discovery workflow as a fast computational filter before expensive assays.
Use the Indication dropdown in the target bar to filter by disease area — oncology, hematology, CNS — then select a target. Gene symbols and PDB IDs shown for each entry.
Upload CSV/TSV with SMILES and optional metadata, or paste SMILES directly. Save named libraries in your browser.
Single-target ranking with sortable table and SAR scatter, or multi-target heatmap for selectivity triage across an indication.
Download CSV for Excel or your ELN. Generate RL-optimized analogs and push top hits back into the library for re-scoring.
Every feature is designed around how medicinal chemists actually work.
Every binding pocket is loaded from actual crystallographic data. Switch between cartoon, surface, and stick views instantly. The electrostatic surface shows you where positive and negative charges cluster — critical for understanding binding selectivity.
Built for HTS triage and SAR series ranking — not generic "paste SMILES." Import compound libraries with IDs and series tags, validate structures before burning quota, and export ranked CSVs your team can paste into Slack or Excel.
Paste or upload a library and get ranked predictions in seconds. Each compound shows predicted pKd, binding probability, molecular weight, LogP, QED drug-likeness score, and ChEMBL similarity to known drugs.
VectaBind AI knows your current target, your scored compounds, and the 3D structure. Ask scientific questions and get expert-level answers. Control the viewer with natural language commands.
Free tier includes 2,000 scores/month, 150 docking runs, and access to every scoreable target. No credit card required.