Improving AI Models for Chemical Synthesis

# Illustrative sketch (not from the paper)
import torch
from torch import nn, optim

# placeholder LLM model
class C3LM(nn.Module):
    def forward(self, input_ids, top_k=10):
        # generate top-k SMILES strings (mock)
        return ["C(C)O"] * top_k

model = C3LM()
optimizer = optim.Adam(model.parameters(), lr=1e-5)

def chemcensor_score(smiles):
    # proxy for chemical plausibility
    return 1.0

def reward_fn(preds, target):
    r = 0.0
    # syntax reward
    r += sum(1 for s in preds if s.startswith('C')) * 0.1
    # SMILES validity (mock)
    r += sum(1 for s in preds if len(s) > 0) * 0.1
    # uniqueness
    r += len(set(preds)) * 0.05
    # plausibility
    r += sum(chemcensor_score(s) for s in preds) * 0.2
    return r

# Top‑K generation and RL update (GRPO placeholder)
for batch in data_loader:  # assume pre‑loaded Top‑K prompts
    preds = model(batch["input_ids"], top_k=10)
    reward = reward_fn(preds, batch["target"])
    loss = -reward  # maximize reward
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

/* Illustrative sketch (not from the paper) */
const tf = require('@tensorflow/tfjs-node');

// mock LLM that returns top‑K SMILES strings
function generateTopK(inputIds, topK = 10) {
  return Array(topK).fill('C(C)O');
}

// placeholder reward components
function chemcensorScore(smiles) {
  return 1.0;
}
function reward(preds, target) {
  let r = 0;
  // syntax reward
  r += preds.filter(s => s.startsWith('C')).length * 0.1;
  // SMILES validity (mock)
  r += preds.filter(s => s.length > 0).length * 0.1;
  // uniqueness
  r += new Set(preds).size * 0.05;
  // plausibility
  r += preds.reduce((sum, s) => sum + chemcensorScore(s), 0) * 0.2;
  return r;
}

// simple RL loop (GRPO placeholder)
async function train(dataset, model, optimizer) {
  for (const batch of dataset) {
    const preds = generateTopK(batch.inputIds, 10);
    const rew = reward(preds, batch.target);
    const loss = tf.scalar(-rew);
    optimizer.minimize(() => loss);
  }
}