Refua

Boltz-2 predicts 3D structures for protein–ligand, protein–protein, and protein–nucleic-acid complexes from sequence alone. It supports small-molecule ligands, covalent modifications, and multi-chain assemblies.

The Refua wrapper provides a fluent builder pattern: define proteins by sequence, add ligands by SMILES, attach constraints, and call .fold() to obtain an all-atom 3D structure plus per-residue confidence scores, all without leaving Python.

BoltzGen is the design counterpart to Boltz-2. Given a target and structural constraints, it can sample peptide binders, antibody CDR loops, and linker sequences for downstream evaluation.

Refua exposes BoltzGen through the same composable API. You can specify chain lengths, add cyclic-peptide constraints or template insertions, and generate sequences ready for downstream affinity scoring and ADMET profiling.

The SM class wraps a SMILES string and computes a comprehensive panel of molecular descriptors, including molecular weight, LogP, topological polar surface area (TPSA), hydrogen-bond donors and acceptors, rotatable bond count, and Lipinski rule-of-five compliance backed by RDKit. Properties can be evaluated lazily on first access or eagerly with to_dict().

SM integrates with the ADMET module for full drug-likeness profiling and with SmilesView for instant 2D depictions inside notebooks.

ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) often determines whether a molecule can become a drug. Refua’s ADMETView widget takes a dictionary of predicted properties (LogP, Caco-2 permeability, hERG inhibition, AMES mutagenicity, bioavailability, plasma protein binding, and more) and renders a color-coded dashboard with optimal / warning / danger thresholds derived from medicinal-chemistry guidelines.

Supported categories span absorption (LogP, Caco-2, solubility, HIA, PAMPA), distribution (PPBR, VDss, BBB penetration), metabolism (clearance, half-life), excretion (P-gp substrate), and toxicity (hERG, AMES, DILI, LD50, carcinogenicity).

MolstarView embeds the Mol* molecular visualization framework directly inside Jupyter notebooks. Load structures from Binary CIF (the compact format produced by Boltz-2), PDB files, or public RCSB URLs. The viewer renders publication-quality 3D representations with interactive rotation, zoom, ligand highlighting, and surface display with no external software required.

Combined with Boltz-2, researchers can fold a complex and visualize it in a single cell, enabling rapid hypothesis testing and visual inspection of predicted binding poses.

The Complex class orchestrates multi-component assemblies. Combine proteins, small molecules, nucleic acids, and covalent modifications into a single specification, then fold the entire assembly with Boltz-2 and request binding-affinity predictions like IC50, Kd, and binding probability in one fluent call chain.

ComplexView renders the result as an interactive 3D scene annotated with affinity metrics, component labels, and confidence scores. It is useful for team presentations and notebook reports.