De Novo Design of Molecules Towards Biased Properties via a Deep Generative Framework and Iterative Transfer Learning

10 April 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


De Novo design of molecules with targeted properties represents a new frontier in molecule development. Despite enormous progress, two main challenges remain, i.e., (i) generation of novel molecules with targeted and quantifiable properties; (ii) generated molecules having property values beyond the range in the training dataset. To tackle these challenges, we propose a novel reinforced regressional and conditional generative adversarial network (RRCGAN) to generate chemically valid, small molecules with targeted heat capacity (Cv) values as a proof-of-concept study. As validated by DFT, ~80% of the generated samples have a relative error (RE) of < 20% of the targeted Cv values. To bias the generation of molecules with the Cv values beyond the range of the original training molecules, transfer learning was applied to iteratively retrain the RRCGAN model. After only two iterations of transfer learning, the mean Cv of the generated molecules increases to 44.0 cal/(mol·K) from the mean value of 31.6 cal/(mol·K) shown in the initial training dataset. This demonstrated computation methodology paves a new avenue to discovering small molecules with biased properties.


reinforced regressional and conditional generative adversarial network
targeted properties
transfer learning
Inverse design

Supplementary materials

supplementary information for inverse discovery of small molecules
Complementary notes and figures that were referred in the main manuscript.


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.