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Discovering novel molecules with targeted properties remains a formidable challenge in materials science, often likened to finding a needle in a haystack. Traditional experimental approaches are slow, costly, and inefficient. In this study, we present an inverse design framework based on a molecular graph conditional variational autoencoder (CVAE) that enables the generation of new molecules with user-specified optical properties, particularly molar extinction coefficient ($$\varepsilon$$). Our model encodes molecular graphs, derived from SMILES strings, into a structured latent space, and then decodes them into valid molecular structures conditioned on a target $$\varepsilon$$ value. Trained on a curated dataset of known molecules with corresponding extinction coefficients, the CVAE learns to generate chemically valid structures, as verified by RDKit. Subsequent Density Functional Theory (DFT) simulations confirm that many of the generated molecules exhibit the electronic structures similar to those molecules with desired $$\varepsilon$$ values. We have also verified the $$\varepsilon$$ values of the generated molecules using a graph neural network (GNN) and the synthesizability of those molecules using an open-source module named ASKCOS. This approach demonstrates the potential of CVAEs to accelerate molecular discovery by enabling user-guided, property-driven molecule generation -- offering a scalable, data-driven alternative to traditional trial-and-error synthesis.
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De novo molecule design towards biased properties via a deep generative framework and iterative transfer learning
De novo design of molecules with targeted properties represents a new frontier in molecule development. Despite enormous progress, two main challenges remain: (i) generating novel molecules conditioned on targeted, continuous property values; (ii) obtaining molecules with property values beyond the range in the training data. To tackle these challenges, we propose a reinforced regressional and conditional generative adversarial network (RRCGAN) to generate chemically valid molecules with targeted HOMO–LUMO energy gap (ΔEH–L) as a proof-of-concept study. As validated by density functional theory (DFT) calculation, 75% of the generated molecules have a relative error (RE) of <20% of the targeted ΔEH–L values. To bias the generation toward the ΔEH–L values beyond the range of the original training molecules, transfer learning was applied to iteratively retrain the RRCGAN model. After just two iterations, the mean ΔEH–L of the generated molecules increases to 8.7 eV from the mean value of 5.9 eV shown in the initial training dataset. Qualitative and quantitative analyses reveal that the model has successfully captured the underlying structure–property relationship, which agrees well with the established physical and chemical rules. These results present a trustworthy, purely data-driven methodology for the highly efficient generation of novel molecules with different targeted properties.
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- Award ID(s):
- 2154428
- PAR ID:
- 10408410
- Publisher / Repository:
- RSC
- Date Published:
- Journal Name:
- Digital Discovery
- Volume:
- 3
- Issue:
- 2
- ISSN:
- 2635-098X
- Page Range / eLocation ID:
- DOI: 10.26434/chemrxiv-2023-0zv2f-v2
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D3DD00210A
