🔬 Scientific Discovery with PLGL

PLGL for Drug Discovery

Instead of explicitly defining molecular properties, researchers can rate generated molecules based on multiple criteria, and PLGL learns the complex trade-offs automatically.

Implementation for Molecular Design

1. Molecular VAE with Graph Neural Networks

Encode molecular structures into a continuous latent space:

import torch
import torch.nn as nn
from rdkit import Chem
from rdkit.Chem import Descriptors

class MolecularVAE(nn.Module):
    """VAE for molecular generation using SMILES representation"""
    
    def __init__(self, vocab_size, latent_dim=256):
        super().__init__()
        self.latent_dim = latent_dim
        
        # Encoder: SMILES → Latent
        self.encoder = nn.LSTM(
            input_size=vocab_size,
            hidden_size=512,
            num_layers=3,
            batch_first=True
        )
        
        self.fc_mu = nn.Linear(512, latent_dim)
        self.fc_logvar = nn.Linear(512, latent_dim)
        
        # Decoder: Latent → SMILES
        self.decoder = nn.LSTM(
            input_size=latent_dim,
            hidden_size=512,
            num_layers=3,
            batch_first=True
        )
        
        self.output = nn.Linear(512, vocab_size)
    
    def encode(self, x):
        _, (h, _) = self.encoder(x)
        h = h[-1]  # Last hidden state
        return self.fc_mu(h), self.fc_logvar(h)
    
    def decode(self, z):
        # Expand z for sequence generation
        z = z.unsqueeze(1).repeat(1, self.max_length, 1)
        output, _ = self.decoder(z)
        return self.output(output)

2. Multi-Objective Preference Learning

Learn complex trade-offs between molecular properties:

class MolecularPreferenceLearner:
    def __init__(self, molecular_vae):
        self.vae = molecular_vae
        self.samples = []
        
        # Multi-output preference model
        self.preference_model = nn.Sequential(
            nn.Linear(molecular_vae.latent_dim, 512),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(512, 256),
            nn.ReLU(),
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, 1),
            nn.Sigmoid()
        )
    
    def compute_molecular_properties(self, smiles):
        """Compute key molecular properties"""
        mol = Chem.MolFromSmiles(smiles)
        if mol is None:
            return None
            
        properties = {
            'molecular_weight': Descriptors.MolWt(mol),
            'logp': Descriptors.MolLogP(mol),  # Lipophilicity
            'qed': Descriptors.qed(mol),       # Drug-likeness
            'sa_score': self.synthetic_accessibility(mol),
            'num_rings': Descriptors.RingCount(mol),
            'num_hbd': Descriptors.NumHDonors(mol),
            'num_hba': Descriptors.NumHAcceptors(mol)
        }
        
        return properties
    
    def collect_preferences(self, n_samples=100):
        """Collect preferences with property visualization"""
        for i in range(n_samples):
            # Generate molecule
            z = torch.randn(1, self.vae.latent_dim)
            smiles = self.vae.decode_to_smiles(z)
            
            # Compute properties
            props = self.compute_molecular_properties(smiles)
            
            # Display to chemist for rating
            print(f"\nMolecule {i+1}:")
            print(f"SMILES: {smiles}")
            print(f"MW: {props['molecular_weight']:.1f}")
            print(f"LogP: {props['logp']:.2f}")
            print(f"QED: {props['qed']:.2f}")
            print(f"Synthetic accessibility: {props['sa_score']:.2f}")
            
            # Get rating (0-1) based on overall desirability
            rating = get_chemist_rating()
            
            self.samples.append({
                'latent': z,
                'smiles': smiles,
                'properties': props,
                'rating': rating
            })

3. Property-Guided Optimization

Navigate latent space considering multiple objectives:

def optimize_molecule(self, target_properties=None, n_steps=1000):
    """Find molecules with desired property profile"""
    
    # Start from promising region if we have samples
    if self.samples:
        # Find best-rated sample as starting point
        best_idx = np.argmax([s['rating'] for s in self.samples])
        z = self.samples[best_idx]['latent'].clone()
    else:
        z = torch.randn(1, self.vae.latent_dim)
    
    z.requires_grad = True
    optimizer = torch.optim.Adam([z], lr=0.01)
    
    for step in range(n_steps):
        # Generate molecule
        smiles = self.vae.decode_to_smiles(z)
        props = self.compute_molecular_properties(smiles)
        
        if props is not None:
            # Score with preference model
            score = self.preference_model(z)
            
            # Add property constraints if specified
            if target_properties:
                property_loss = 0
                for prop, target in target_properties.items():
                    if prop in props:
                        # Squared error from target
                        property_loss += (props[prop] - target) ** 2
                
                # Combined objective
                loss = -score + 0.1 * property_loss
            else:
                loss = -score
            
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
            # Constrain to valid latent space
            with torch.no_grad():
                z.clamp_(-3, 3)
    
    return self.vae.decode_to_smiles(z.detach())

4. Scaffold-Constrained Generation

Generate variations while preserving core structure:

def generate_analogs(self, scaffold_smiles, n_analogs=20):
    """Generate molecules with same scaffold but different properties"""
    
    # Encode scaffold
    scaffold_z = self.vae.encode_smiles(scaffold_smiles)
    
    analogs = []
    for i in range(n_analogs):
        # Add controlled noise
        noise = torch.randn_like(scaffold_z) * 0.1
        z_variant = scaffold_z + noise
        
        # Decode to molecule
        smiles = self.vae.decode_to_smiles(z_variant)
        
        # Check if scaffold is preserved
        if self.contains_scaffold(smiles, scaffold_smiles):
            score = self.preference_model(z_variant).item()
            props = self.compute_molecular_properties(smiles)
            
            analogs.append({
                'smiles': smiles,
                'score': score,
                'properties': props
            })
    
    # Sort by preference score
    analogs.sort(key=lambda x: x['score'], reverse=True)
    return analogs

Applications in Material Science

class MaterialPreferenceLearner:
    """PLGL for material property optimization"""
    
    def __init__(self, crystal_vae):
        self.vae = crystal_vae  # VAE for crystal structures
        self.preference_model = self.build_preference_model()
    
    def optimize_for_properties(self, preferences):
        """
        Find materials matching property preferences:
        - Mechanical: strength, ductility, hardness
        - Electrical: conductivity, band gap
        - Thermal: melting point, thermal expansion
        - Chemical: stability, reactivity
        """
        
        # Start from random crystal structure
        z = torch.randn(1, self.vae.latent_dim)
        
        for step in range(1000):
            # Generate crystal structure
            structure = self.vae.decode(z)
            
            # Predict properties (using ML or DFT)
            properties = self.predict_properties(structure)
            
            # Score based on preferences
            score = self.preference_model(z)
            
            # Update latent code
            z = self.gradient_step(z, score)
            
        return structure

Real-World Impact

Key Advantages for Science

• Multi-Objective Optimization: Balance complex trade-offs without explicit weights
• Expert Knowledge Integration: Captures implicit knowledge through ratings
• Exploration of Novel Space: Discover unexpected solutions
• Iterative Refinement: Continuously improve as understanding evolves
• Reduced Trial and Error: Focus experiments on high-preference regions