Predicting UV-Vis absorption spectra by using graph neural network models
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In recent years, predicting absorption spectra by using different types of models
utilizing deep neural networks has become an increasingly popular topic within
spectroscopy. These models can be trained on datasets consisting of molecules represented
as SMILES (Simplified Molecular Input Line Entry System), as well as
intensities for a range of wavelengths. The resulting models can accurately predict
excitation spectra for molecules. The capabilities of these models have proven useful
in the drug industry, such as identifying harmful molecules or detecting substances.
Two popular models implementing graph neural networks (GNNs) and predict absorption
spectra are AttentiveFP and Chemprop-IR. AttentiveFP uses a graph attention
mechanism and message passing neural network to form a graph convolutional
network. Chemprop-IR uses a directed message passing neural network,
originally designed to predict IR spectra. These models were chosen due to both
using the same type of data and molecular representation, outperforming regular
regression models, and the ability of capturing complex patterns of diverse spectra
as well as predicting these. The goal of this project was to set up, modify, and train
the GNNs AttentiveFP and Chemprop-IR, which were constructed for other spectra
prediction purposes, to predict UV-Vis absorption spectra within the range of 150
nm to 450 nm, with a 6 nm discretization, from the chemical structures of molecules.
Both models were trained on an identical dataset consisting of 10,502,904 molecules
obtained from Oak Ridge National Laboratory with the same split for training, testing,
and validation. The models used SMILES as the input of the molecules, as well
as the intensities at each corresponding wavelength. AttentiveFP was trained with
three different different implementations of the attention mechanism, GAT, GATv2,
and DenseGAT. GAT is proven to compute static attention while GATv2 computes
dynamic attention. DenseGAT is based on GAT but instead considers the molecule
as a fully connected graph. Chemprop-IR was trained for three different choices of
FFN-hidden size (feedforward neural network) and hidden size of 2200, 2800, and
3400.
The best-performing model of AttentiveFP used the GATv2 attention mechanism
and 8 attentive layers. For Chemprop-IR, the best-performing model used an FFN
hidden size of 2800. Both models show promise predicting UV-Vis absorption spectra.
AttentiveFP proved to be the better-performing model of the two based on
predictions and validation loss. The model was able to make accurate predictions
on a large set of molecules, correctly identifying the number of peaks and their positions. However, it failed for some molecules, where the predicted spectra were
completely different from the true spectra. The exact reason is hard to determine.
However, the coexistence of larger molecules with high-frequency components and
low prediction error, along with small molecules with low-frequency components and
high prediction error, suggests that the attention mechanism is working. Chemprop-
IR resulted in an increased accuracy of predicting multiple peaks for larger sizes of
FFN-hidden size and hidden size. Predicted spectra of lowest accuracies were mostly
ones with multiple peaks. One theory is that predicting IR spectra differs greatly
from UV.
Beskrivning
Ämne/nyckelord
Attention-based mechanism, AttentiveFP, Backpropagation, Chemprop- IR, D-MPNN, GNN, MPNN, SMILES, UV-Vis absorption spectra