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Drug discovery using DiffDock

Our Mission

Deep learning models such as AlphaFold2 are powerful for accelerating biological research pipelines, but using them comes with a steep learning curve and can require hiring specialized personnel or lengthy training programs, making it challenging to produce viable results.

Neurosnap is a fully end-to-end suite of machine learning models and services designed to easily integrate into your synthetic biology, pharmaceutical, and medical pipelines. Our team of dedicated specialists designed our platform to be as simple, efficient, and secure as possible.

Designed for researchers in biotech,
by researchers in biotech

Sample use cases

Protein Folding

Determine the structure of proteins from their peptide sequences alone. Our AlphaFold2 implementation allows you to accurately predict the structure of proteins as well as complexes.

We provide access to all settings and even exposes internal parameters like recycling steps and ensembling that base AlphaFold2 doesn't.

No coding skills required, just configure your settings, run the job, and grab a coffee while we do the hard parts for you.

Structure prediction with AlphaFold2

In-silico Mutagenesis

Create high-fidelity mutants and functional homologs of your favorite proteins using our in-silico based techniques.

We provide access to models such ProteinMPNN and AlphaFold2 so that you can generate variants of virtually any protein.

Our platform can sample thousands of variants within a span of minutes, screening with AlphaFold2 can be done within hours to days.

In-silico mutagenesis with proteinMPNN

Protein Engineering

Neurosnap makes designing novel proteins a breeze with our suite of optimized deep learning models. All our models come with simplified dashboards that you can use to fine-tune our services to best fit your needs.

We provide state of the art visualizations for all our tools, at no additional cost. We know bioinformatics can be tricky at times, that's why our team of scientists are always ready to help.

Protein engineering with ProGen2

Drug Discovery

Simplify your drug discovery pipelines and make finding potential drug targets a breeze with our pharmaceutical oriented models.

Neurosnap provides access to state of the art models such as DiffDock which are able to accurately and effectively predict ligand docking and protein-ligand interactions.

Drug discovery using DiffDock

Antibody Design

Our in-silico antibody design pipelines allows for rapid generation of potential antibodies from preliminary sequences.

Models such as our ProGen2 OAS implementation (ProGen2 trained on the Observed Antibody Space dataset) can be used to generate hundreds of antibodies within mere minutes, allowing for rapid exploration of the antibody sequence space.

Antibody design with progen2-oas

How it works

Discover Neurosnap's collection of tools and technical services for biology.

Supported Models & Services

We support a number of different services across a wide variety of different tasks to suit your needs. Currently we support 7 different task types spanning objectives such as protein structure prediction to ligand binding prediction. Can't find the service you're looking for? Contact our support team and we'll add it to our platform!

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A protein structure prediction model that takes an amino acid sequence, MSA, and a template structure as inputs. Both the MSA and template structure are optional, and inference can be done without them. Additionally, options are available for automating generation of both.


Around $1.20 for a medium sized protein (~250 AA)

ProteinMPNN is a powerful inverse folding model that is capable of not only predicting the amino acids of a protein structure, but also certain chains, and complexes. Additionally, ProteinMPNN can be used as a way to create functional homologs / mutants of existing proteins by inverse folding their structures and sampling the sequence space.


Around $0.10 for 100 samples of a medium sized protein (~250 AA)

DiffDock is a new state of the art method for molecular docking and drug binding structure prediction. This model takes in a protein structure and ligand as input and returns the ligand docked onto the protein as an output.


Around $0.15 for 50 predictions

ProGen2 is generative protein language model capable of generating de novo proteins and protein variants given an input sequence. Additionally, another version of ProGen2, progen2-oas can be used to generate antibody sequences.


Around $3.87 for 100 samples of a medium-large sized protein (~512 AA)

The ESM-IF1 inverse folding model predicts protein sequences from their backbone atom coordinates, trained with 12M protein structures predicted by AlphaFold2.


Around $2.27 for 100 samples of a medium sized protein (~250 AA)

Takes in raw transcriptome data from an RNAseq experiment in and automatically assembles and quantifies transcripts. No reference genome required!

Transcript Assembly

Cost varies depending on input size, (usually $20-$30)

Why Researchers Trust Neurosnap


Neurosnap enforces the latest in enterprise-grade security standards.


When we designed our platform, we kept simplicity at the top of our priority list.


We provide the most competitive pricing while minimizing our carbon footprint.


Designed to be scalable and straight forward to integrate into existing research pipelines.




Maximum of 50 concurrent jobs

Access to standard hardware (nvidia v100s, T4s)

Standard Support

Access to all services

Access to custom visualizations

Access to all file downloads

Access to programmatic API

Register Standard



Maximum of 500 concurrent jobs

Access to premium hardware (nvlinked A100s)

Premium Support

Access to all services

Access to custom visualizations

Access to all file downloads

Access to programmatic API

Register Premium


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Run on your own cluster

Premium Support

Access to all all services

Access to custom visualizations

Access to all file downloads

Access to programmatic API

Contact us for more information!

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