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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 Examples

Amino Acid Sequence*

MMseqs2 Multiple Sequence Alignment

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

Cascaded Search (all)

Singular database

Uniref30_2303

PDB70_220313

colabfold_envdb_202108

Inclusion Threshold

E-value*

Iterations*

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 Examples

Molecule Sequence*

Number of Molecules*

100

Temperature*

0.01

1.01

2.01

3.01

4.01

5.01

6.01

7.01

8.01

9.01

Noise*

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Diffusion Step Size*

Property to 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 Protein

Molecule

Generated Poses*

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 blueprint chemistry docking nim nvidia bionemo drug discovery

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