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Warning!

Puhti and Mahti computing services have been decommissioned and no new jobs are accepted or executed on its compute nodes. Puhti and Mahti login nodes and storage services are planned to remain available until 15 October 2026. Clean up unnecessary files and move any data you need to keep by 31 August 2026. See the Roihu data migration guide for instructions on transferring your data to Roihu.

Maestro

Online Schrödinger certification courses offered by CSC

Users in scope of CSC's Schrödinger license are eligible to apply for free-of-charge access to participate in online certification courses organized by Schrödinger. The offered introductory and intermediate online courses focus on a range of topics related to drug discovery and materials science.

Read more and apply here

Schrödinger Maestro is a versatile molecular modeling environment. It has modules for drug design and materials science. It can be used to build, edit, run and analyze chemical model systems.

Schrödinger Maestro provides access to the Desmond molecular dynamics engine, which runs very well on GPUs.

See also the bottom of this page for links to further self-learning materials.

Available

  • Roihu-CPU: 2025.1, 2025.2, 2025.3, 2025.4, 2026.1, 2026.2, 2026.3
  • LUMI: 2026.1, 2026.2, 2026.3

A two-year cleaning cycle is applied on the Maestro modules on CSC supercomputers. Specifically, this means that module versions older than two years will be removed. This policy is enforced to free up disk space and encourage use of the latest versions which tend to be more performant and have less bugs.

Desmond MD simulations cannot be run on Roihu!

Schrödinger currently only ships x86 builds of their software suite. This means that any Schrödinger modules that require GPUs, most notably Desmond, cannot be run on Roihu.

CSC provides Maestro modules only on Roihu-CPU for purely CPU-based workloads such as virtual screening (Glide).

License

Maestro is available for all academic users in Finland: staff and students, for academic purposes. Please consult the EULA for the exact definition. Using Maestro means that you accept the EULA linked to above. The Maestro license consists of floating licenses and tokens. If licenses run out, contact us via ServiceDesk.

Usage

It is recommended to download and install Maestro on your own computer, see below.

Local installation

Maestro can be installed on a Linux, Mac or Windows computer. Download the appropriate files from the Schrödinger website. You don't need a license to download the software, although you do need to register at the Schrödinger website first. Note that getting access may take up to 24 hours, so please be patient.

After you've downloaded and installed Maestro, you'll need to configure licensing to be able to run the software. See these instructions on how to configure licensing (logging in to Eduuni requires Haka authentication). Accessing the license requires that your computer is in the FUNET network, i.e. you're at the university or connected to it via VPN from home.

Standalone usage on Roihu and LUMI

Roihu vs. LUMI

Note that Roihu is not suitable for running Desmond MD simulations on GPUs. Please use LUMI-D if you need to run Desmond simulations. If you need support, contact us.

It is possible to run heavier computations on Roihu and LUMI. Here, a brief overview is given. Additional details and some diagnostics tips are explained in our Maestro power usage tutorial.

First, you need to get a CSC account and create or be added to a project that has access to Roihu or LUMI.

For actual simulations we recommend using Roihu-CPU or LUMI as follows:

  1. Set up your simulations on your local computer.

  2. Write the GUI-generated input files to disk:

    1. Instead of selecting Run, open the nearby drop-down menu marked with a cogwheel.
    2. Select Write to write the input files to disk.

  3. Copy the input files to the supercomputer with e.g. scp or using the web interfaces.

  4. Important! The <job-name>.sh file contains the command to launch your simulation. To make it work on CSC supercomputers, ensure that:

    1. -HOST localhost is set.
    2. -WAIT is appended at the end of the command.
    3. Windows users should replace the forward-slashes \ with back-slashes / in the path to the Schrödinger binary (right after ${SCHRODINGER} in the command).

    Additionally, the <job-name>.sh file should be edited into a proper Slurm batch script. Examples for different platforms:

    #!/bin/bash
#SBATCH --account=project_2001234
#SBATCH --partition=small
#SBATCH --time=00:30:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=16
#SBATCH --mem-per-cpu=2000M

module load maestro/2026.3

# Start a local job server
${SCHRODINGER}/jsc local-server-start

# Run a Glide job distributed over 16 cores (16 subjobs running concurrently)
"${SCHRODINGER}/glide" cdk2_dock.in -OVERWRITE -NJOBS ${SLURM_CPUS_PER_TASK} -new -JOBNAME cdk2_dock -HOST localhost:${SLURM_CPUS_PER_TASK} -WAIT

# Stop the local job server
${SCHRODINGER}/jsc local-server-stop

    #!/bin/bash
#SBATCH --account=project_462000123
#SBATCH --partition=small
#SBATCH --time=00:30:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=16
#SBATCH --mem-per-cpu=2000M

module use /appl/local/csc/modulefiles
module load maestro/2026.3

export SCHRODINGER_LICENSE_SERVERS='<server:port>' # See instructions at https://wiki.eduuni.fi/x/XbbHBw

# Start a local job server
${SCHRODINGER}/jsc local-server-start

# Run a Glide job distributed over 16 cores (16 subjobs running concurrently)
"${SCHRODINGER}/glide" cdk2_dock.in -OVERWRITE -NJOBS ${SLURM_CPUS_PER_TASK} -new -JOBNAME cdk2_dock -HOST localhost:${SLURM_CPUS_PER_TASK} -WAIT

# Stop the local job server
${SCHRODINGER}/jsc local-server-stop

    #!/bin/bash
#SBATCH --account=project_462000123
#SBATCH --partition=lumid
#SBATCH --time=00:30:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --gpus=1
#SBATCH --mem-per-cpu=2000M

module use /appl/local/csc/modulefiles
module load maestro/2026.3

export SCHRODINGER_LICENSE_SERVERS='<server:port>' # See instructions at https://wiki.eduuni.fi/x/XbbHBw

# Start a local job server
${SCHRODINGER}/jsc local-server-start

# Run a Desmond MD simulation using a single GPU
"${SCHRODINGER}/utilities/multisim" -JOBNAME desmond_job -HOST localhost -maxjob 1 -cpu 1 -m desmond_job.msj -c desmond_job.cfg -description 'Molecular Dynamics' desmond_job.cms -mode umbrella -o desmond_job-out.cms -lic DESMOND_GPGPU:16 -WAIT

# Stop the local job server
${SCHRODINGER}/jsc local-server-stop

  5. Submit the job to the queue with sbatch <job-name>.sh.

  6. Copy the results back to your local workstation for analysis.

Important notes

  • Please observe that the ${SCHRODINGER}/jsc local-server-start and ${SCHRODINGER}/jsc local-server-stop commands are mandatory. Schrödinger has deprecated the old Job Control system and schrodinger.hosts files that were used on Puhti and Mahti. The new Job Server system must be used on Roihu and LUMI.
  • No long/heavy tasks should be done on the login nodes! Please consult the Usage policy page for more details.

Monitoring running jobs

Running Slurm jobs can be monitored as usual using the squeue command. Additionally, running Schrödinger jobs and the log files they produce can be monitored using tools provided by the Job Server command-line utility jsc:

# Replace <node> with the id of the node your job is running on, e.g. rc4136.
# Note that the Schrödinger job id is different from the Slurm job id.

ssh <node> "${SCHRODINGER}/jsc list" # List jobs and their status
ssh <node> "${SCHRODINGER}/jsc tail-file <schrodinger-jobid>" # Tail log file for a running job

# Note that the Schrödinger job id is different from the Slurm job id.

srun -n 1 --overlap --pty --jobid=<slurm-jobid> ${SCHRODINGER}/jsc list
srun -n 1 --overlap --pty --jobid=<slurm-jobid> ${SCHRODINGER}/jsc tail-file <schrodinger-jobid>

References

Please cite the Maestro modules in all published work as described in the Module manuals.

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