Pytorch Tensor Optimizer

This is a parallelized version of the lbfgs optimization algorithm for molecular conformations. A whole set of conformations can be optimized in parallel significantly reducing the time required for conformational searches.

This code was developed based on the lbfgs optimization algorithm as implemented in pytorch lbfgs.py. The intended use is for conformational searches using Nerual Net Potentials. An example interface to the ANI potential and a dummy implementation of a potential in pytorch is included.

This code is released under the MIT license.

Installation

To use this with the ANI Neural net Potential (NNP):

install ASE ANI and ensure that it runs correctly including the pyton interface (nerurochem package)
install the cddlib package
download(git clone) this source code
install it into your python environment: cd into thee root direcotry of this package
```
pip install .
```

setup the environment as necessary. Note: ASE_ANI requires CUDA.

export ASE_ANI_DIR=<your ASE_ANI_DIR>
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:$ASE_ANI_DIR/lib
export PYTHONPATH=${PYTHONPATH}:$ASE_ANI_DIR/lib

run on a test case:

 sdfANIOptimizer.py -in data/C2H6.sdf -out C2H6.ani.sdf -conf $ANICONF -computeForce

Use with other pytorch based NNP's

This requires the implementation of three wrapper classes:

a pytorch module that extends from CoordinateModelInterface
a NNPComputerFactoryInterface class
a tiny command line wrapper like sdfANIMOptimizer

1. Pytorch module

This encapsulates an actual NNP implementation. The forward() method takes an argument of type SameSizeCoordsBatch that provides access to the information on the conformations to minimize:

n_confs: the number of conformations
n_atom_per_conf: the number of atoms per conformation (all conformations have the same number of atoms)
coords: the coordinates
atom_types: the atomic numbers of the atoms. note: the conformations may vary in the type of atoms.

A very simplified NNP that pulls all atoms to the coordinates (-0.703,-0.703, -0.703) is given below:

class DummyNet(CoordinateModelInterface):
    """
        A dummy pytoch module that computes a potential that pulls all atoms
        towards having coordinate = -0.703 
    """
    def __init__(self):
        super().__init__()
        
    def forward(self, same_size_coords_batch:SameSizeCoordsBatch):
        c = same_size_coords_batch.coords
        c = c*5
        e = c.pow(2) + c.exp()
        e = e.reshape(c.shape[0],-1).sum(-1)
        # min (y=(5x)^2 + e^(5x)) ~ y(-0.703) = 0.8272
        return e, e   # fake stdev with e, will not affect tests

2. The NNP Computer Factory

This Interface takes a string parameter and provides a factory for the NeuralNet Potential. The String parameter can be a directory name as used to point to the configuration directory for the ANI_computer, a filename or any other string that will be used to constuct your PyTorch Module.

An Example can be found in ExampleNNPComputerFactory (NNPComputerFactoryInterface)

3. Wrapper for command line interface

To create a command line program that you can call to minimize conformations with your NNP you have to create a tiny wrapper that constructs the NNP Computer Factory and passes it to the sdf_multi_optimizer.

An Example can be found in SDFANIMOptimizer.

Acknowledgments

I would like to thank Justin S. Smith and Adrian Reutberg for making the ASE_ANI available and for help to set it up.

I would like to thanks Man-Ling Lee, the Genentech Incubator project and the Genentech Computational Chemistry group for allowing me to work on this.

References:

ASE_ANI

Original implementation of the lbfgs optimization algorithm in pytorch

License

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plin1112/tOpt