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Build A Generative Virtual Screening Pipeline Blueprint by NVIDIA | NVIDIA NIM

Input

OpenFold2Virtual screening will be performed against the protein entered here. Enter the amino acid sequence of the protein or select an example to predict its 3D structure.

Protein ExamplesProtein

Amino Acid SequenceAmino Acid Sequence. Sequence must be at least 50 characters long for docking to work.

MMseqs2 Multiple Sequence Alignment

The predictions of OpenFold2 use a multiple sequence alignment (MSA). Specify here how to perform the MSA search.

MSA Databases

Uniref30_2303

PDB70_220313

colabfold_envdb_202108

Inclusion Threshold

E-value

2345678910

Iterations

123456

GenMol

This module uses GenMol to generate molecules to screen for activity against the protein. Choose how to generate optimized molecules for the virtual screening.

Molecule ExamplesMolecule Examples

Molecule SequenceMolecule Sequence

Number of Molecules

1112131415161718191100

Temperature

0.0112345678910

Noise

00.10.20.30.40.50.60.70.80.911

Diffusion Step Size

12345678910

Property to ComputeSelect one of the available properties to be compute.

DiffDock InputsThe final step of virtual screening uses DiffDock to predict how each generated molecule will interact with the target protein. Choose how the generated molecules and the protein will be docked.

Target ProteinPDB Generated by OpenFold2

MoleculeMolecule Input Generated by GenMol

Generated Poses

111213140

Using free API for development

GOVERNING TERMS: Your use of this API is governed by the NVIDIA API Trial Service Terms of Use; and the use of this blueprint is governed by the NVIDIA AI Foundation Models Community License.

Output

Click generateAdjust the settings and click generate to see your outcome here.

nvidia

Build A Generative Virtual Screening Pipeline

This blueprint shows how generative AI and accelerated NIM microservices can design optimized small molecules smarter and faster.

openfold2•msa-search•genmol-generate•diffdock

BioNemo Chemistry Docking NIM

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