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.

Incentive Version

The commercial GPUSimilarity product ("FPSim GPU") with additional enhancements, maintenance and support is available from Schrödinger. 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See RDKit Presentation for much more in depth benchmarks (that are slightly out of date).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:

Using GPUSimilarity

It is highly recommended that you use docker for building/running.

See Our Docker Readme

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 <fingerprint fsim file>

For testing (insecure):

From build directory: python3 ${SRC_DIR}/python/gpusim_server.py <fingerprint fsim file> --http_interface

For generating databases:

Easiest from rdkit conda with pyqt installed:

From source python directory: python3 gpusim_createdb.py <input smi.gz file> <fingerprint fsim file>

For debugging Cuda server, avoiding python/http server altogether:

From build directory:
./gpusimserver <dbname>.fsim
python3 python ${SRC_DIR}/python/gpusim_search.py <dbname>

Note: No .fsim extension is used for gpusim_search.py

This may be useful to determine if the backend is having Cuda/GPU problems.

schrodinger/gpusimilarity