https://github.com/choderalab/espaloma

Extensible Surrogate Potential of Ab initio Learned and Optimized by Message-passing Algorithm 🍹https://arxiv.org/abs/2010.01196

Science Score: 46.0%

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Links to: arxiv.org
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2 of 12 committers (16.7%) from academic institutions
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Low similarity (13.9%) to scientific vocabulary

Keywords

forcefield-parameterization graph-learning

Keywords from Contributors

molecular-dynamics free-energy-calculations molecular-simulations openmm graph-theory molecular-modeling tensorflow-gpu

Last synced: 5 months ago · JSON representation

Repository

Extensible Surrogate Potential of Ab initio Learned and Optimized by Message-passing Algorithm 🍹https://arxiv.org/abs/2010.01196

Basic Info

Host: GitHub
Owner: choderalab
License: mit
Language: Python
Default Branch: main
Homepage: https://docs.espaloma.org/en/latest/
Size: 13.4 MB

Statistics

Stars: 239
Watchers: 14
Forks: 27
Open Issues: 64
Releases: 9

Topics

forcefield-parameterization graph-learning

Created about 6 years ago · Last pushed 9 months ago

Metadata Files

Readme License

espaloma: Extensible Surrogate Potential Optimized by Message-passing Algorithms 🍹

Source code for Wang Y, Fass J, and Chodera JD "End-to-End Differentiable Construction of Molecular Mechanics Force Fields."

abstract

Documentation: https://docs.espaloma.org

Paper Abstract

Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules atom types for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process---spanning chemical perception to parameter assignment---is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn and extend existing molecular mechanics force fields, construct entirely new force fields applicable to both biopolymers and small molecules from quantum chemical calculations, and even learn to accurately predict free energies from experimental observables.

Installation

We recommend using mamba which is a drop-in replacement for conda and is much faster.

bash $ mamba create --name espaloma -c conda-forge "espaloma=0.3.2"

Example: Deploy espaloma 0.3.2 pretrained force field to arbitrary MM system

```python

imports

import os import torch import espaloma as esp

define or load a molecule of interest via the Open Force Field toolkit

from openff.toolkit.topology import Molecule molecule = Molecule.from_smiles("CN1C=NC2=C1C(=O)N(C(=O)N2C)C")

create an Espaloma Graph object to represent the molecule of interest

molecule_graph = esp.Graph(molecule)

load pretrained model

espalomamodel = esp.getmodel("latest")

apply a trained espaloma model to assign parameters

espalomamodel(moleculegraph.heterograph)

create an OpenMM System for the specified molecule

openmmsystem = esp.graphs.deploy.openmmsystemfromgraph(molecule_graph) ```

If using espaloma from a local .pt file, say for example espaloma-0.3.2.pt, then you would need to run the eval method of the model to get the correct inference/predictions, as follows:

```python import torch ...

load local pretrained model

espalomamodel = torch.load("espaloma-0.3.2.pt") espalomamodel.eval() ... ```

The rest of the code should be the same as in the previous code block example.

Compatible models

Below is a compatibility matrix for different versions of espaloma code and espaloma models (the .pt file).

| Model 🧪 | DOI 📝 | Supported Espaloma version 💻 | Release Date 🗓️ | Espaloma architecture change 📐? | |---------------------|-------|------------------------------|----------------|----------------------------------| | espaloma-0.3.2.pt | | 0.3.1, 0.3.2, 0.4.0 | Sep 22, 2023 | ✅ No | | espaloma-0.3.1.pt | | 0.3.1, 0.3.2, 0.4.0 | Jul 17, 2023 | ⚠️ Yes | | espaloma-0.3.0.pt | | 0.3.0 | Apr 26, 2023 | ⚠️Yes |

[!NOTE]
espaloma-0.3.1.pt and espaloma-0.3.2.pt are the same model.

Using espaloma to parameterize small molecules in relative free energy calculations

An example of using espaloma to parameterize small molecules in relative alchemical free energy calculations is provided in the scripts/perses-benchmark/ directory.

Manifest

espaloma/ core code for graph-parametrized potential energy functions.
- graphs/ data objects that contain various level of information we need.
  - graph.py base modules for graphs.
  - molecule_graph.py provide APIs to various molecular modelling toolkits.
  - homogeneous_graph.py simplest graph representation of a molecule.
  - heterogeneous_graph.py graph representation of a molecule that contains information regarding membership of lower-level nodes to higher-level nodes.
  - parametrized_graph.py graph representation of a molecule with all parameters needed for energy evaluation.
- nn/ neural network models that facilitates translation between graphs.
  - dgl_legacy.py API to dgl models for atom-level message passing.
- mm/ molecular mechanics functionalities for energy evaluation.
  - i/ energy terms used in Class-I force field.
    - bond.py bond energy
    - angle.py angle energy
    - torsion.py torsion energy
    - nonbonded.py nonbonded energy
  - ii/ energy terms used in Class-II force field.
    - coupling.py coupling terms
    - polynomial.py higher order polynomials.

License

This software is licensed under MIT license.

Copyright

Owner

Name: Chodera lab // Memorial Sloan Kettering Cancer Center
Login: choderalab
Kind: organization
Email: john.chodera@choderalab.org
Location: Memorial Sloan-Kettering Cancer Center, Manhattan, NY

Website: http://choderalab.org
Repositories: 269
Profile: https://github.com/choderalab

GitHub Events

Total

Create event: 4
Release event: 1
Issues event: 7
Watch event: 27
Delete event: 4
Issue comment event: 9
Push event: 12
Pull request review comment event: 1
Pull request review event: 2
Pull request event: 7
Fork event: 4

Last Year

Create event: 4
Release event: 1
Issues event: 7
Watch event: 27
Delete event: 4
Issue comment event: 9
Push event: 12
Pull request review comment event: 1
Pull request review event: 2
Pull request event: 7
Fork event: 4

Committers

Last synced: almost 3 years ago

All Time

Total Commits: 613
Total Committers: 12
Avg Commits per committer: 51.083
Development Distribution Score (DDS): 0.261

Past Year

Commits: 24
Committers: 6
Avg Commits per committer: 4.0
Development Distribution Score (DDS): 0.625

Top Committers

Name	Email	Commits
Yuanqing Wang	w**q@w**t	453
Josh Fass	j**s@c**g	102
John Chodera	j**a@c**g	17
kaminow	b**w@c**g	15
Mike Henry	1**y@u**m	14
Josh Horton	J**n@n**k	3
kt	k**r@g**m	3
Yuanqing Wang	w**1@l**g	2
kaminow	5**w@u**m	1
yuanqing-wang	w**q@u**u	1
Kenichiro Takaba	t**k@l**e	1
Yuanqing Wang	w**h@w**t	1

Committer Domains (Top 20 + Academic)

choderalab.org: 3 wangyq.net: 2 lilac-ln02.hpc.private: 1 umich.edu: 1 lski2863.mskcc.org: 1 newcastle.ac.uk: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 50
Total pull requests: 84
Average time to close issues: 4 months
Average time to close pull requests: about 1 month
Total issue authors: 26
Total pull request authors: 14
Average comments per issue: 1.9
Average comments per pull request: 2.68
Merged pull requests: 64
Bot issues: 0
Bot pull requests: 1

Past Year

Issues: 2
Pull requests: 2
Average time to close issues: 14 days
Average time to close pull requests: less than a minute
Issue authors: 2
Pull request authors: 1
Average comments per issue: 1.5
Average comments per pull request: 0.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

kntkb (8)
mikemhenry (7)
jchodera (6)
LeifSeute (3)
GhermanProteic (3)
ijpulidos (3)
maxentile (2)
exYuan (1)
tueboesen (1)
simonaxelrod (1)
mganahl (1)
niccolo93 (1)
chrisiacovella (1)
yuanqing-wang (1)
chabi-fin (1)

Pull Request Authors

yuanqing-wang (39)
mikemhenry (18)
kntkb (7)
jchodera (5)
maxentile (4)
ijpulidos (2)
gianscarpe (2)
kaminow (2)
jstaker7 (1)
mattwthompson (1)
amin-sagar (1)
jthorton (1)
pmorerio (1)
jeherr (1)
lgtm-com[bot] (1)

Top Labels

Issue Labels

question :question: (3) enhancement :magic_wand: (2) bug (1) installation :hammer_and_wrench: (1) reproducibility :microscope: (1) paper :roll_of_paper: (1) testing :test_tube: (1) bug :bug: (1)

Pull Request Labels

bug (1) high-priority (1) installation :hammer_and_wrench: (1) bug :bug: (1)

Packages

Total packages: 1
Total downloads: unknown

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 3

conda-forge.org: espaloma

Homepage: https://github.com/choderalab/espaloma
License: MIT
Latest release: 0.2.4
published over 3 years ago

Versions: 3
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Stargazers count: 30.3%

Dependent repos count: 34.0%

Average: 38.9%

Forks count: 40.0%

Dependent packages count: 51.2%

Last synced: 6 months ago

Dependencies

requirements.txt pypi

dgl *
matplotlib *
numpy *
pandas *
qcportal *
torch *

.github/workflows/CI.yaml actions

actions/checkout v2 composite
codecov/codecov-action v1 composite
conda-incubator/setup-miniconda v2 composite

.github/workflows/clean_cache.yaml actions

actions/checkout v3 composite

.github/workflows/docker.yaml actions

actions/checkout v3 composite
docker/build-push-action ad44023a93711e3deb337508980b4b5e9bcdc5dc composite
docker/build-push-action v4 composite
docker/login-action f054a8b539a109f9f41c372932f1ae047eff08c9 composite
docker/metadata-action 98669ae865ea3cffbcbaa878cf57c20bbf1c6c38 composite
eWaterCycle/setup-apptainer v2 composite

devtools/conda-recipe/meta.yml cpan

docker/Dockerfile docker

mambaorg/micromamba 1.4.9 build

setup.py pypi

https://github.com/choderalab/espaloma

Science Score: 46.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

espaloma: Extensible Surrogate Potential Optimized by Message-passing Algorithms 🍹

Paper Abstract

Installation

Example: Deploy espaloma 0.3.2 pretrained force field to arbitrary MM system

imports

define or load a molecule of interest via the Open Force Field toolkit

create an Espaloma Graph object to represent the molecule of interest

load pretrained model

apply a trained espaloma model to assign parameters

create an OpenMM System for the specified molecule

load local pretrained model

Compatible models

Using espaloma to parameterize small molecules in relative free energy calculations

Manifest

License

Copyright

Owner

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

conda-forge.org: espaloma

Rankings

Dependencies