diff --git a/rmgpy/ml/estimator.py b/rmgpy/ml/estimator.py
index 547cbe25fc1..8e120dd8e8a 100644
--- a/rmgpy/ml/estimator.py
+++ b/rmgpy/ml/estimator.py
@@ -30,11 +30,11 @@
 from argparse import Namespace
 from typing import Callable, Union
 
+try:
+    import chemprop
+except ImportError as chemprop_exception:
+    chemprop = None
 import numpy as np
-from chemprop.data import MoleculeDatapoint, MoleculeDataset
-from chemprop.parsing import update_checkpoint_args
-from chemprop.train import predict
-from chemprop.utils import load_args, load_checkpoint, load_scalers
 
 from rmgpy.molecule import Molecule
 from rmgpy.species import Species
@@ -112,16 +112,21 @@ def load_estimator(model_dir: str) -> Callable[[str], np.ndarray]:
     """
     Load chemprop model and return function for evaluating it.
     """
+    if chemprop is None:
+        # Delay chemprop ImportError until we actually try to use it
+        # so that RMG can load successfully without chemprop.
+        raise chemprop_exception
+
     args = Namespace()  # Simple class to hold attributes
 
     # Set up chemprop predict arguments
     args.checkpoint_dir = model_dir
     args.checkpoint_path = None
-    update_checkpoint_args(args)
+    chemprop.parsing.update_checkpoint_args(args)
     args.cuda = False
 
-    scaler, features_scaler = load_scalers(args.checkpoint_paths[0])
-    train_args = load_args(args.checkpoint_paths[0])
+    scaler, features_scaler = chemprop.utils.load_scalers(args.checkpoint_paths[0])
+    train_args = chemprop.utils.load_args(args.checkpoint_paths[0])
 
     # Update args with training arguments
     for key, value in vars(train_args).items():
@@ -131,12 +136,14 @@ def load_estimator(model_dir: str) -> Callable[[str], np.ndarray]:
     # Load models in ensemble
     models = []
     for checkpoint_path in args.checkpoint_paths:
-        models.append(load_checkpoint(checkpoint_path, cuda=args.cuda))
+        models.append(chemprop.utils.load_checkpoint(checkpoint_path, cuda=args.cuda))
 
     # Set up estimator
     def estimator(smi: str):
         # Make dataset
-        data = MoleculeDataset([MoleculeDatapoint(line=[smi], args=args)])
+        data = chemprop.data.MoleculeDataset(
+            [chemprop.data.MoleculeDatapoint(line=[smi], args=args)]
+        )
 
         # Normalize features
         if train_args.features_scaling:
@@ -148,7 +155,7 @@ def estimator(smi: str):
             # Predict with each model individually and sum predictions
             sum_preds = np.zeros((len(data), args.num_tasks))
             for model in models:
-                model_preds = predict(
+                model_preds = chemprop.train.predict(
                     model=model,
                     data=data,
                     batch_size=1,  # We'll only predict one molecule at a time