rotconML

Identifying molecules with probabilistic deep learning

The paper is now published and online! You can find the JPCA here, and a preprint version on ArXiV.

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Aim

This project builds a series of deep learning models to help identify molecules based on their rotational spectroscopic parameters.

Context

• Spectral features from rotational spectra fit to rotational Hamiltonians
• Spectroscopic parameters alone do not uniquely identify a molecule
• Molecular identification usually done by matching parameters to molecular structure
• Parameters are highly encoded: chemical and structural information is deeply embedded and not easily retrieved

Solution

This project implements a series of deep learning models that map spectroscopic parameters to identifying information about a molecule. In order to ensure that the full breadth of possible structures are explored, the models are constructed in a probabilistic context using dropout layers as an approximation to Bayesian sampling.

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File structure details

src

This folder contains all of the backend Python code. In the latest iteration, I used PyTorch more or less exclusively, and you can find that under src.models.torch_models.

The pipeline module contains all of the routines I wrote to perform data cleaning and formatting for analysis. The code basically parses Gaussian 16 output files, and extracts all of the relevant data and puts them into HDF5 files for analysis.

The visualzation module contains a few quick routines for plotting results, which I used early on for analyzing performance. For figure making I did not rely on these routines as much.

models

This folder, specifically models/torch/, is where all the model training is performed. These scripts utilize GPUs to train the models, and the wandb Python package to track experiments. The two subfolders, tensorflow and torch, are implementations with those respective libraries.

The product of these scripts are a series of PyTorch model weights that are saved as pickle files, which are the state_dict objects contained within PyTorch models. For every model, four models are produced corresponding to each of the compositions.

production

This is where the demonstrations were done after the models are trained. There is a script, unified_model_test.py, which shows how the models can be used.

scripts

This folder is where the preprocessing is done. The scripts will do all of the relevant data parsing, and put data into the right locations for subsequent model training and analysis. The two main scripts are prepare_newset.py and prepare_demo.py; the former generates the datasets for the main bulk of the work and the latter is for demonstration purposes.

A more recent and important script is fix_undersampling.py, which does what the number suggests. This script will check all of the newset dataset entries and perform SMARTS substructure searches to determine which functional groups are undersampled, and uses this information to augment the final dataset by boosting creating noise-perturbed copies of existing examples.

Usage

This git repository contains the bare code: due to the excessive data set sizes none of the data is stored on git.

The Makefile is pretty self-explanatory, and streamlines a fair amount of the foundation work, along with conda environments.

The core focus is actually in the PyTorch models - implementations described in the paper are actually based on these, instead of the Tensorflow ones. I kept these in for reference reasons, but these are not expected to run in production.

There are four main models that are considered in the paper:

1. EightPickEncoder = Spectroscopy decoder
2. EigenSMILESLSTMDecoder = SMILES LSTM decoder
3. EigenFormulaDecoder = Formula decoder
4. FunctionalGroupConv = Functional group classifier

These are trained independently, and for inference the "fifth" model is defined that controls the flow of everything; ChainModel. This class has several higher level methods compared to the other models, which loads the model parameters specific to one composition.

License

rotconML - a project on probabilistic molecule identification with PyTorch

Copyright (C) 2019-2020 Kin Long Kelvin Lee

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see https://www.gnu.org/licenses/.