BioDeck Pipeline
Follow our streamlined computational workflow from data preparation to precise dynamic simulations.
Available Tools17

ZINC22 CartBlanche
Fast parallel download from ZINC-22 (54B+ compounds) with custom MW, LogP, and charge parameters. Outputs files 100% compatible with Ligand Prep.

RDKit
Open-source cheminformatics software for molecule manipulation, descriptor calculation, and fingerprint generation.

Ligand Prep
Prepare small molecule ligands for docking simulations by correcting structures and generating 3D conformers.

Protein Prep
Prepare PDB receptor structures for docking - smart chain selection, solvent cleanup, metal ion preservation, and pH 7.4 protonation.

ADMET Screening
Predict ADMET properties swiftly and accurately using BioDeck.

Sequence Crawler
Fetch DNA and Protein sequences from NCBI and UniProt using universal sequence crawling modules.

ESM Phylo
Protein sequence phylogenetic tree analysis powered by ESM-C (EvolutionaryScale). Upload a FASTA file to generate embeddings, distance matrices, and interactive evolutionary trees.

Auto GraphPad
Automated scientific data visualization. Upload CSV or Excel files to instantly generate publication-ready charts - no backend required.

GNINA Screening
Upload receptor and ligand files in BioDeck, then run GNINA on a local GPU or a user-launched Colab T4 worker.

DiffDock-L
Latest iteration of diffusion-based docking for blind docking with high accuracy and improved sampling efficiency.

DynamicBind v2
Deep generative model for flexible protein-ligand docking with conformational sampling and animations.

GNINA Optimization
Refine poses and scores of docked ligands using CNN scoring functions for precise binding affinity prediction.

Molecular Dynamics
Run OpenMM molecular dynamics with optional GNINA pose QC before simulation to inspect docked candidates safely.

MM-GBSA
Calculate the free energy of binding for protein-ligand complexes using molecular mechanics and generalized Born surface area continuum solvation.

RFdiffusion
Design novel protein structures, binders, and scaffolds using the deep learning model from RosettaCommons.

ProteinMPNN
Generate high-quality sequence designs for given protein backbones with thermodynamic stability.

BoltzGen
No-code YAML Builder for BoltzGen - design peptide, protein, and nanobody binders targeting specific residues with a generative AI.