Agents-A1

Predict the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the input molecules.

Predict binding affinity between target protein sequence and small molecule SMILES using Boltz-2.

Predict protein structures with Chai-1 from sequence or FASTA input and return model scoring summaries.

Chroma toolkit skill covering chroma_monomer for single-chain generation, chroma_complex for multi-chain assembly generation, and chroma_symmetry for symmetry-constrained protein design.

Retrieve SMILES strings from PubChem database using compound names.

Generate entirely new drug-like molecules from scratch without any starting molecule, using REINVENT4's de novo prior.

Run automated DiffDock protein-ligand docking and return confidence-based result summaries.

Calculate disease reversal scores for the provided molecules relative to a specific disease.

High-level large-scale virtual screening workflow (10+ ligands) combining property filtering, QuickVina docking, EquiScore rescoring, and consensus ranking for target prioritization.

Compute the drug-likeness metrics (QED score and Number of violations of Lipinski's Rule of Five) of the input candidate molecules (SMILES format).

End-to-end docking-score ranking using EquiScore for candidate molecules against a target protein.

Unified EquiScore skill for pocket extraction, pocket scoring, and end-to-end docking-to-score pipeline execution.

Use ESMFold model to predict 3D structure of the input protein sequence.

Design linear or cyclic peptide binders from receptor FASTA sequences using EvoBind2 with structured result outputs.

Extract protein sequence of each chain from the protein structure file (pdb format).

Implement data transmission between the local computer and the MCP Server using Base64 encoding

Repair and clean PDB files with PDBFixer, returning repaired file path and topology counts.

Use fpocket to detect binding pockets and output their detailed properties for the input protein. This offers a more concise approach to pocket identification.

Detect binding pockets with fpocket_toolkit and return parsed pocket descriptors and run artifacts.

Run GoCa coarse-grained protein MD pipeline and collect key simulation artifacts from a unified run directory.

Run HDOCKlite docking for protein complexes and return run directories with ranked models.

Run KarmaDock graph generation and virtual screening to produce ranked ligand poses and summary metrics.

Generate linker molecules connecting two warhead fragments, for applications such as PROTAC design, bivalent ligands, and fragment merging.

Compute a set of basic molecular properties for a given list of SMILES strings, returning the molecular formula, exact and average molecular weights, counts of heavy and total atoms, number of bonds, valence electrons, and formal charge for each input molecule.

Compute Gasteiger partial charges and formal charge for a list of SMILES strings, returning the minimum, maximum, average, and range of the Gasteiger charges alongside the formal charge for each molecule.

Compute custom molecular complexity-related descriptors for a given list of SMILES strings, returning the molecular complexity score, aromatic proportion, and asphericity value for each input molecule.

Compute hydrogen bonding-related properties for a list of SMILES strings, specifically determining the number of hydrogen bond donors and acceptors for each input molecule.

Computes hydrophobicity-related molecular descriptors for a given list of SMILES strings, returning the octanol-water partition coefficient (logP) and molar refractivity for each input molecule.

Integrating molecular property calculation tools with the reasoning capabilities of Large Language Models (LLMs) to optimize key physicochemical properties of drug molecules, such as LogP, QED, and solubility.

Optimize drug molecular structures to enhance binding activity against specific protein targets, using binding assessment tools, interaction analysis, and LLM-guided molecular design.

Calculate both Tanimoto similarities and the count of shared structural fragments between a target molecule and a list of candidate molecules via Morgan fingerprints.

Compute a set of molecular structure complexity descriptors for a list of SMILES strings, returning detailed metrics for each molecule including the number of rotatable bonds, total/aromatic/aliphatic/saturated rings, heteroatoms, and bridgehead atoms, as well as the fraction of sp³-hybridized carbon atoms (Fsp³).

Compute a comprehensive set of topological descriptors for a list of SMILES strings, returning the Topological Polar Surface Area (TPSA), a series of valence and non-valence molecular connectivity indices (Chi0–Chi4), the Hall–Kier alpha value, and Kappa shape indices (Kappa1–Kappa3) for each input molecule.

Generate new molecules by transforming an input molecule using different priors for scaffold-aware, similarity-controlled molecular optimization.

Runs OpenAWSEM simulations and extracts representative trajectory frames for downstream ensemble analysis.

Use P2Rank to locate binding pockets in the input protein. Unless specified by the user, prioritize using fpocket.

Predicts full-atom sidechain conformations from backbone PDBs using AttnPacker for structure preparation workflows.

Repair a protein PDB file with PDBFixer: fix missing atoms/residues, add hydrogens, remove heterogens, etc.

Generate peptide molecules using PepInvent, supporting template-based generation, custom peptide sequence modification, and info queries for available templates and amino acids.

ProLIF docking-pose analysis skill for batch interaction fingerprints and interaction count summaries.