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https://github.com/schrodinger/gpusimilarity
A Cuda/Thrust implementation of fingerprint similarity searching
https://github.com/schrodinger/gpusimilarity
cheminformatics chemistry gpu similarity-analysis
Last synced: 3 months ago
JSON representation
A Cuda/Thrust implementation of fingerprint similarity searching
- Host: GitHub
- URL: https://github.com/schrodinger/gpusimilarity
- Owner: schrodinger
- License: bsd-3-clause
- Created: 2018-05-14T20:13:19.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-01-24T19:08:08.000Z (10 months ago)
- Last Synced: 2024-06-14T01:44:13.244Z (5 months ago)
- Topics: cheminformatics, chemistry, gpu, similarity-analysis
- Language: C++
- Homepage:
- Size: 479 KB
- Stars: 91
- Watchers: 19
- Forks: 26
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# gpusimilarity
A brute-force GPU implementation of chemical fingerprint similarity searching. Its intended use is to be kept alive as a service with an entire library loaded into graphics card memory. It has python scripts included which use RDKit to generate fingerprints, but the C++/Cuda backend are agnostic to the data once it's been created.
Architecture and benchmarks were presented in [a presentation at the 2018 RDKit European UGM](gpusimilarity_rdkit_presentation.pdf).
## Incentive Version
The commercial GPUSimilarity product ("FPSim GPU") with additional enhancements, maintenance and support is available from [Schrödinger](https://www.schrodinger.com). Enhancements to the incentive version will be periodically merged into the open source version, similar to Incentive PyMOL.
## Basic Benchmark
On a machine with four Tesla V100, searching one billion compounds takes ~0.2 seconds.
See [RDKit Presentation](gpusimilarity_rdkit_presentation.pdf) for much more in depth benchmarks (that are slightly out of date).
## Example integration
Here is a video of this backend being utilized for immediate-response searching inside Schrödinger's LiveDesign application:
[](https://www.youtube.com/watch?v=T11UXEoF_rk)
## Using GPUSimilarity
**It is highly recommended that you use docker for building/running.**
See [Our Docker Readme](docker/)
## Dependencies for Building (recommended only for development)
* RDKit (At Python level, not compilation)
* Qt 5.2+ (including QtNetwork)
* PyQt
* Cuda SDK, CUDACXX env variable pointing to nvcc
* cmake 3.10.2+
* C++11 capable compiler
* Boost test libraries
* Optional: Doxygen for generating documents
## Building with CMake and running unit tests with CTest
**Recommended only for development, see Docker**
```
From parent directory of source:
mkdir bld
cd bld
ccmake ../gpusimilarity
make -j5
ctest
```
If Cuda, boost or doxygen are not found, start ccmake with the following
options:
```
ccmake -DCMAKE_CUDA_COMPILER=/path/to/nvcc -DBOOST_ROOT=/path/to/boost/directory -DDOXYGEN_EXECUTABLE=/path/to/doxygen
```
### Generate the documentation
Install doxygen on system
```
make doc_doxygen
```
The result is in bld/doc/html
## Running
**Recommended only for development, see Docker**
### For basic json-response http endpoint:
From build directory:
`python3 ${SRC_DIR}/python/gpusim_server.py `
### For testing (insecure):
From build directory:
`python3 ${SRC_DIR}/python/gpusim_server.py --http_interface`
### For generating databases:
Easiest from rdkit conda with pyqt installed:
From source python directory:
```python3 gpusim_createdb.py ```
### For debugging Cuda server, avoiding python/http server altogether:
```bash
From build directory:
./gpusimserver .fsim
python3 python ${SRC_DIR}/python/gpusim_search.py
```
Note: No .fsim extension is used for gpusim_search.py
This may be useful to determine if the backend is having Cuda/GPU problems.