added link to preprint

Author: Löffler, Hannes

README.md

@@ -9,8 +9,9 @@ REINVENT is a molecular design tool for de novo design, scaffold hopping,
 R-group replacement, linker design, molecule optimization, and other small
 molecule design tasks.  At its heart, REINVENT uses a Reinforcement Learning
 (RL) algorithm to generate optimized molecules compliant with a user defined
-property profile defined as a multi-component score. See AUTHORS.md for
-paper references.
+property profile defined as a multi-component score.
+
+A preprint is available on ChemRxiv: [REINVENT4: Modern AI-Driven Generative Molecule Design](https://chemrxiv.org/engage/chemrxiv/article-details/65463cafc573f893f1cae33a).  See AUTHORS.md for references to previous papers.
 
 
 Requirements

reinvent/runmodes/TL/learning.py

@@ -105,9 +105,10 @@ def __init__(
                     s = DataStructs.BulkTanimotoSimilarity(fps[n], fps[n + 1 :])
                     sim.extend(s)
 
-                self.tb_reporter.add_histogram(
-                    "Tanimoto input SMILES", np.array(sim), 0
-                )
+                # FIXME: TB broken
+                #self.tb_reporter.add_histogram(
+                #    "Tanimoto input SMILES", np.array(sim), 0
+                #)
 
         # FIXME: this is only available for Mol2mol
         if self._config.max_sequence_length:

reinvent/runmodes/TL/reports/tensorboard.py

@@ -59,7 +59,8 @@ def write_report(reporter, data, model: ModelAdapter, is_reinvent: bool, dataloa
 
         reporter.add_scalar("KL divergence", kl_div, data.epoch)
 
-        reporter.add_histogram("Tanimoto similarity on RDKitFingerprint", similarities, data.epoch)
+        # FIXME: TB broken
+        #reporter.add_histogram("Tanimoto similarity on RDKitFingerprint", similarities, data.epoch)
 
         # FIXME: rows and cols depend on sample_batch_size
         image_tensor = make_grid_image(smilies, sample_nlls, "NLL", ROWS * COLS, ROWS)