Predicting Medicine into Existence - Part III
As mentioned in Part I and Part II our current mission is to use molecular data to predict potential compounds. We’re doing this to predict medicines.
Last time out we got our prediction pipeline flowing and submitted the first official competition submission.
Now we are optimizing.
Here's a quick overview of what we have working today:
Data Input: SMILES strings representing molecular structures.
Feature Transformation
Graph Representation: SMILES are converted into molecular graphs (nodes = atoms, edges = bonds) with node features (atomic number, mass, etc.). These are fed to the Graph Attention Network (GAT).
Fingerprint Representation: SMILES are converted into fixed-length molecular fingerprints. These are fed to the Gated Recurrent Unit (GRU).
Model Ensemble: A GAT and GRU model predict binding affinity (binary classification), and their outputs are averaged.
Cross-Validation: The models are evaluated using 5-fold cross-validation.
Generate Test Predictions
The system is working, and I’ve been able to ratchet up performance as you can see:
…but the leaders in the clubhouse are sporting a .58 and that means I’ll need to work on several fronts to close the gap:
Tune my two best models
Design 2 to 3 new architectures
Get each of them running (they take 5+ hours to run) in constant loops so I can iterate through progressive optimization
Let's break down my approach to optimize this machine learning pipeline for molecular compound prediction.
Optimization Strategies
There is a lot of surface area to manipulate in order to optimize machine learning systems. Let’s outline the major areas first (data, model architecture, training process, etc..) and then drill down from there.
1. Data Optimization
Data is the lifeblood — you must always focus first and foremost on data quality. Once you have your best data, then the fun begins. How can you engineer the most value out of that data?
Feature Engineering:
Additional Graph Features: Explore other molecular graph features that might be relevant (e.g., bond types, ring structures).
Fingerprint Types: Experiment with different fingerprint algorithms (e.g., MACCS, RDKit) and radii to see if they improve performance.
2. Model Optimization
Great features mean nothing if the model they flow through are not well-built.
These are the moves I am considering making on this front:
Hyperparameter Tuning
GAT: Number of layers, hidden dimensions, attention heads, dropout rate.
GRU: Number of layers, hidden size, dropout rate.
Optimizer: Learning rate, weight decay, other optimizer parameters
Architecture Variations
GAT: Consider attention variants like GATv2, or even transformer-based graph models.
GRU: Experiment with bidirectional GRUs or other RNN architectures (e.g., LSTMs).
Ensemble: Explore more sophisticated ways of combining model predictions (weighted averaging, stacking).
Regularization
Layer techniques like dropout, L1/L2 regularization, or batch normalization to prevent overfitting.
3. Training Process Optimization
Even if you have a Ferrari model… how do you drive it?
If the model is not well-trained, it will not perform in the open road.
I am using these methods to optimize my training:
Early Stopping: Monitor validation performance and stop training early if it doesn't improve for a certain number of epochs.
Learning Rate Schedule: Use techniques like learning rate decay or cyclical learning rates to fine-tune the learning process.
“You Can’t Fix an Upstream Problem Downstream”
The most important part of your model is the data that feeds it.
Since this task involves digesting SMILES data and making predictions, we can study the information that is conveyed (currently) by our fingerprint and graph representations… and then evaluate for underrepresented areas that may be predictive of molecule formation.
Then we can add something, a new representation, to our ML system in order to inject that important missing information.
One such representation is called a molecular descriptor. A molecular descriptor is a mathematical representation of a molecule's properties, using numerical values to describe information. They can be structural or physicochemical properties of a molecule or part of a molecule. Molecular descriptors are also known as chemical descriptors or molecular properties. Molecular descriptors are diverse and fall into categories like:
Constitutional Descriptors: Atom and bond counts, molecular weight.
Topological Descriptors: Molecular connectivity indices, Wiener index.
Electrostatic Descriptors: Partial charges, dipole moments.
Geometric Descriptors: 3D shape descriptors, surface area, volume.
Quantum Chemical Descriptors: HOMO/LUMO energies, polarizability.
Let's analyze the fingerprint and graph representations we currently have in our system to identify molecular descriptor categories that would provide valuable, complementary information for your protein binding affinity prediction model.
Fingerprint Analysis
The fingerprint I chose is a variant known as a Morgan fingerprint with a radius of 2. This captures:
Atom Environments: The arrangement of atoms and bonds within a radius of 2 bonds around each atom.
Substructure Information: The presence of specific functional groups and molecular fragments.
As you can see, this function is very thin.
By thin I mean not a lot of information is flowing across this surface area…
Missing from Fingerprints:
Constitutional Descriptors: Information about atom counts, bond counts, molecular weight, etc.
Topological Descriptors: Molecular connectivity indices that describe the branching and complexity of the molecule.
Electrostatic and Quantum Chemical Descriptors: Partial charges, dipole moments, frontier orbital energies, etc., which are important for understanding interactions with the protein.
Geometric Descriptors: Explicit 3D shape descriptors like surface area and volume.
Graph Analysis
The graph representation encodes a lot of the information that is missing from the fingerprints — such as:
Atom Features: Atomic number, mass, number of hydrogens, aromaticity, degree, H-bond donor/acceptor status.
Bond Information: The connectivity between atoms through the edge index.
Even with this thicker function there is plenty missing that will have an impact on protein bonding.
Missing from Graphs:
Higher-Order Interactions: Information about rings, ring systems, and other complex structural motifs that go beyond immediate atom connectivity.
3D Information: The graph is inherently 2D and lacks explicit 3D coordinates or shape information.
All the descriptor categories missing from fingerprints.
Adding Molecular Descriptors
Given all the gaps spotted in these observations, my plan is to be methodical about which descriptor information I pull through the model.
I’m going to use descriptors to plug holes in the model first, and then add high-value features to drive up the predictive accuracy. These are my planned injections:
Constitutional Descriptors: To capture basic molecular composition information (e.g., number of heavy atoms, number of heteroatoms).
Topological Descriptors: To capture the complexity and branching of the molecular structure (e.g., Wiener index, Balaban index).
Electrostatic Descriptors: To capture the charge distribution and polarity of the molecule (e.g., partial charges, dipole moment).
Quantum Chemical Descriptors: These provide valuable insights into electronic properties relevant to binding (e.g., HOMO/LUMO energies).
Example Descriptors:
Constitutional:
HeavyAtomCount,HeteroatomCount,NumAromaticRingsTopological:
BalabanJ,BertzCTElectrostatic:
MolLogP,TPSAQuantum Chemical:
HOMO,LUMO,DipoleMoment
Once I get the the descriptor pipeline flowing alongside the fingerprint and graph pipelines, I plan on enhancing the system at every other phase after data. Architecture, hyperparamaters, training, validation and model service.
…there are a LOT of enhancements we can make. That is one of my favorite things about ML, the endless configurations and possibilities for performance improvement. These are the ones I have circled for execution here:
Architectural Enhancements
Deeper Networks: Add more GATConv layers (e.g., 3 or 4 layers) to increase the model's capacity to learn complex patterns in the graph data.
Residual Connections: Introduce residual connections (skip connections) between layers to aid in training deeper networks and mitigate the vanishing gradient problem.
Multi-head Attention: Instead of a single attention mechanism, use multiple attention heads in each GATConv layer. This allows the model to capture different aspects of relationships between nodes.
Layer Normalization: Apply layer normalization after each GATConv layer to stabilize training and potentially improve performance.
Ensemble Methods: Combine multiple GAT models (or models with different architectures) to improve overall performance.
Hyperparameter Tuning
Learning Rate: Experiment with different learning rates (e.g., using a learning rate scheduler) to find the optimal balance between convergence speed and model performance.
Dropout: Apply dropout to the node features or attention weights to prevent overfitting, especially if you have a limited amount of training data.
Weight Initialization: Consider different initialization schemes for the model weights (e.g., Xavier/Glorot initialization) to improve training stability and convergence.
Optimizer: Try different optimization algorithms (e.g., Adam, RMSprop) to see which one works best for your specific GAT model and dataset.
Advanced Techniques
Attention Mechanism Variations: Explore alternative attention mechanisms like self-attention, multi-head attention with different aggregation functions (e.g., max, mean), or even transformer-based attention.
Graph Convolution Variations: Experiment with different graph convolution operations, such as GraphSAGE or GCN, to see if they better suit the problem domain.
Node Embedding: Pre-train node embeddings using techniques like DeepWalk or Node2Vec and use these as input features to the GAT model.
Graph Sampling: Consider using graph sampling techniques (e.g., neighbor sampling) to make training more computationally feasible —> will need this as I add complexity via the new representations.
What’s Next?
I’ve got work to do!
Excited to share the final competition results next week.
👋 Thank you for reading Life in the Singularity.
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