https://github.com/choderalab/timemachine_hydration_paper

Differentiate all the things!

https://github.com/choderalab/timemachine_hydration_paper

Repository

Differentiate all the things!

https://github.com/choderalab/timemachine_hydration_paper/blob/master/

# Time Machine

A high-performance differentiable molecular dynamics and forcefield engine, forked from `https://github.com/proteneer/timemachine`.

Navigate to the `data` directory for submission scripts and analysis notebooks pertaining to the ["Fine-tuning molecular mechanics force fields to experimental free energy measurements"](https://doi.org/10.1101/2025.01.06.631610) manuscript.

## Features

1. Interpolated nonbonded softcore potentials are implemented via 4D decoupling.
2. Computes derivatives of the observables with respect to forcefield parameters.

## Deficiencies

1. No constraints (HMR is needed to increase step size)
2. Explicit solvent is supported by reaction field, not PME

## Functional Forms

We currently support the following functional forms. Parameters that can be optimized are listed in parentheses.

1. HarmonicBond (force constant, bond length)
2. HarmonicAngle (force constant, ideal angle)
3. PeriodicTorsion (force constant, phase, periodicity)
4. PBC LennardJones 612 (sigma, epsilon)
5. PBC reaction field electrostatics (charge)

## Installation

### Pre-requisites

* RDKit
* OpenEye Toolkits
* OpenMM
* Cuda 11.5+
* CMake 3.24.3

### Setup using Anaconda/Install TimeMachine
The CUDA extension module implementing custom ops is only supported on Linux.


```shell
# install appropriate timemachine dependencies
conda env create -f environment.yml -n tm_off
conda activate tm_off

# make and build timemachine after `git clone`
git clone https://github.com/choderalab/timemachine_hydration_paper
cd timemachine_hydration_paper
pip install -e .
make clean
make build
```

### Running Tests

To run tests that use `openeye`, ensure that either `OE_LICENSE` or `OE_DIR` are set appropriately.

For example, starting from a clean environment with the openeye license file in `~/.openeye`:

```shell
OE_DIR=~/.openeye pytest -xsv tests/
```

Note: we currently only support and test on python 3.10, use other versions at your own peril.

## Forcefield Gotchas

Most of the training is using the correctable charge corrections [ccc forcefield](https://github.com/proteneer/timemachine/blob/1a721dd3f05d6011cf028b0588e066682d38ba59/ff/params/smirnoff_1_1_0_ccc.py), which is SMIRNOFF 1.1.0 augmented with BCCs ported via the [recharge](https://github.com/openforcefield/openff-recharge) project. There are some additional modifications:

1. The charges have been multiplied by sqrt(ONE_4PI_EPS0) as an optimization.
2. The eps parameter in LJ have been replaced by an alpha such that alpha^2=eps in order to avoid negative eps values during training.
3. We use a consistent 0.5 scaling for the 1-4 terms across LJ and electrostatics.
4. The reaction field used is the real part of PME with a beta (alpha) coefficient of 2.0
5. The recharge BCC port is not yet complete, as there are some missing types that will cause very large errors (eg. P=S moieties).

# License

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

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