Overview of molecular simulation using graph neural networks
Molecular simulation using graph neural networks is an approach that is expected to show higher accuracy and efficiency than conventional methods, and will be particularly noteworthy for its ability to capture the complexity of molecular structures and interactions, and its ability to learn from large data sets.
An overview is given below.
1. graphical representation of molecules: molecules are modelled as graphs where atoms are represented by nodes and bonds by edges. Each node represents an atom and each edge represents a bond between atoms.
2. GNN model construction: a GNN model is constructed using the graphical representation of the molecule as input, which is used to extract features of atoms and bonds and to model the properties and behaviour of the molecule.
3. prediction of molecular properties: GNNs are used to predict the properties and structure of molecules. For example, it is possible to predict the physical and chemical properties of a molecule, such as its energy, stability, reactivity and solubility.
4. generation and optimisation of molecules: GNNs can be used to generate new molecules or optimise existing ones. For example, it is possible to search for the design of a molecule with specific properties, or iteratively optimise the structure of a molecule.
5. learning and optimisation: the GNN model is trained using a large molecular dataset. They learn from datasets of molecular structures and properties and optimise parameters to help predict and optimise new molecules.
6. applications: molecular simulations using GNNs have been widely applied in areas such as drug discovery, materials design, catalyst development and chemical synthesis prediction. In particular, they can be useful to understand complex molecular structures and interactions and to assist in the design and synthesis of new molecules.
Algorithms related to molecular simulation using graph neural networks.
Algorithms related to molecular simulation using graph neural networks (GNNs) are used to predict the properties and behaviour of specific molecules from their graphical representation. Typical algorithms are described below.
1. Graph Convolutional Networks (GCNs): GCNs can be neural networks for performing convolutional operations on graph structure data; GCNs can aggregate the information of each node’s neighbouring nodes and update the features of that node; and 2. graph of molecules For more information on GCNs, see Graph Convolutional Neural Networks (GCNs): overview, algorithms and implementation examples.
2. Message Passing Neural Networks (MPNNs): MPNNs are a method of applying neural networks on graph-structured data using a technique called message passing. MPNNs are widely used to model molecular structures and interactions. See also “Overview of message passing in machine learning with algorithms and implementation examples” for more information.
3. Graph Isomorphism Networks (GINs): GINs are a type of GNN designed to preserve graph isomorphism: they learn the properties of a graph by updating the features of each node as a combination of features of its neighbouring nodes, taking the graph representation of the molecule as input. For more information on GINs, see Graph Isomorphism Network (GIN) Overview, Algorithm and Implementation Examples.
4. Neural Message Passing for Quantum Chemistry (NMPQC): NMPQC is a method developed to predict the quantum mechanical properties of molecules by performing message passing on graph structure data. The NMPQC is used to predict the electronic structure and energies of molecules by performing message passing on graph structure data.
Applications of molecular simulation using graph neural networks.
Molecular simulation using graph neural networks has many applications. They are described below.
1. drug discovery: molecular simulation using GNNs is widely used in drug discovery to find new drug candidates; GNNs are used to model molecular structures and interactions and to predict the biological activity and side effects of drugs, thereby identifying effective drug candidates and accelerating drug design This enables the identification of effective drug candidates and the acceleration of drug design.
2. materials design: molecular simulation has also been applied to the design of new materials, where GNNs can be used to model the structure and composition of molecules and predict the properties and performance of materials, thereby facilitating the development of new materials such as photoelectric, catalytic and battery materials.
3. drug discovery: molecular simulations using GNNs also play an important role in the drug discovery process. Interactions between drugs and target proteins can be modelled to predict the biological effects and side effects of drugs, thereby enabling the development of effective drugs and optimisation of the drug discovery process.
4. prediction of chemical synthesis: molecular simulations using GNNs have also been used to predict chemical synthesis. Molecular reactivity, stereo-conformation and product properties can be modelled to predict the efficiency and selectivity of chemical synthesis, thereby enabling the optimisation of organic synthesis processes and the design of chemical reactions.
Example implementation of a molecular simulation using a graph neural network.
The following is an example of a molecular simulation implementation using a simple graph neural network (GNN) using Python and the PyTorch library. In this example, the GNN is used to predict the energy of a molecule from a graphical representation of the molecule.
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from torch_geometric.nn import GCNConv
from torch_geometric.data import Data
# Definition of graph neural networks
class GNN(nn.Module):
def __init__(self):
super(GNN, self).__init__()
self.conv1 = GCNConv(75, 32) # GCN layer with 75 dimensionality of input features and 32 dimensionality of output features
self.conv2 = GCNConv(32, 1) # GCN layer with 32 dimensionality of input features and 1 dimensionality of output features
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = torch.relu(self.conv1(x, edge_index))
x = torch.sigmoid(self.conv2(x, edge_index)) # Predicting energy using sigmoidal functions.
return x
# Data preparation
# As a simple example, a graph representation of a randomly generated molecule is used here
x = torch.randn(100, 75) # Random tensor of molecular node features (100 molecules, each with 75 dimensional features)
edge_index = torch.randint(0, 100, (2, 500)) # Randomly generated index of edges (100 nodes, 500 edges)
data = Data(x=x, edge_index=edge_index)
# Initialising the model and preparing for training
model = GNN()
optimizer = optim.Adam(model.parameters(), lr=0.01)
criterion = nn.MSELoss()
# Performing learning
for epoch in range(100):
optimizer.zero_grad()
output = model(data)
target = torch.randn_like(output) # Generates random target energy
loss = criterion(output, target)
loss.backward()
optimizer.step()
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 100, loss.item()))In this example, the PyTorch Geometric library is used to process the graph structure. The model consists of two GCN layers, which receive a graph representation of the molecule and predict the final energy. During training, the model is trained to minimise the mean square error with a random target energy.
Challenges and Solution for molecular simulation using graph neural networks
Molecular simulations using graph neural networks (GNNs) present the following challenges and corresponding measures to address them
1. data imbalance: molecular datasets often show unbalanced distributions across classes. This may lead to biased model learning and incorrect prediction of minority classes. Countermeasures include data expansion and the use of sampling techniques to balance classes.
2. adequacy of the graphical representation: the choice of graphical representation of the molecules has a significant impact on the performance of the model. If the graphical representation does not adequately capture the information in the molecule, the prediction accuracy of the model may be reduced. Countermeasures include designing more sophisticated graph representations and features.
3. overlearning: overlearning refers to a situation where the model shows high performance on training data but cannot generalise to unknown data. In particular, the graphical representation of molecules is very complex and tends to be prone to over-learning. Countermeasures include the use of appropriate model regularisation, drop-outs and data cross-validation to suppress over-learning.
4. computational cost: molecular simulations can be highly computationally resource intensive. Computational costs tend to be particularly high when dealing with large molecular data sets and complex models. Countermeasures include controlling model complexity and using computational resources efficiently, as well as using GPUs and distributed processing to increase computational speed.
Reference Information and Reference Books
For more information on graph data, see “Graph Data Processing Algorithms and Applications to Machine Learning/Artificial Intelligence Tasks. Also see “Knowledge Information Processing Techniques” for details specific to knowledge graphs. For more information on deep learning in general, see “About Deep Learning.
Reference book is
“Graph Neural Networks: Foundations, Frontiers, and Applications“等がある。
“Introduction to Graph Neural Networks“
“Graph Neural Networks in Action“
“Machine Learning in Chemistry: The Impact of Artificial Intelligence“
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