Prioritizes likely protein-ligand interactions for drug discovery, repurposing, and target exploration.

Atlas PLI

Atlas PLI helps researchers prioritize likely relationships between proteins and small molecules. It is designed for early screening, hypothesis generation, and follow-up planning in drug discovery workflows.

What It Does

Atlas PLI helps teams:

• Rank candidate ligands for a protein target.
• Explore possible target relationships for a molecule of interest.
• Screen drug libraries against proteins or protein neighborhoods.
• Surface repurposing hypotheses.
• Add ligand context to protein interaction networks.

The output is a prioritization signal, not a binding assay.

Why It Matters

Protein-ligand screening is expensive when every candidate must move directly to the lab. Atlas PLI gives teams a fast computational layer for narrowing a large search space before committing to synthesis, purchase, or experimental testing.

This is especially useful when paired with Atlas PPI. A protein target rarely acts alone. Combining protein-ligand and protein-protein context helps researchers reason about target biology, possible pathway effects, and downstream experimental priorities.

Open Research Foundation

Atlas PLI follows the same product philosophy as Atlas PPI: Synthyra products build on open research directions, then extend them with additional model development, calibration, serving infrastructure, validation workflows, and analysis layers.

It should be understood as a Synthyra capability, not a simple port of an open-source model.

Intended Use

Use Atlas PLI for early-stage triage:

• Drug discovery and repurposing screens.
• Target engagement hypothesis generation.
• Ligand-focused pathway exploration.
• Prioritizing protein-ligand pairs for orthogonal assays.

For best results, combine Atlas PLI with structural modeling, known chemistry, assay data, medicinal chemistry review, and wet-lab validation.

Limitations

Atlas PLI does not prove physical binding, estimate clinical efficacy, or replace biochemical assays. Scores can be affected by chemistry outside the model's useful domain, target conformational state, cofactors, allostery, cellular context, and assay conditions.

Predictions should be treated as ranked hypotheses.