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High-throughput property-driven generative design of functional organic molecules

A preprint version of the article is available at arXiv.


The design of molecules and materials with tailored properties is challenging, as candidate molecules must satisfy multiple competing requirements that are often difficult to measure or compute. While molecular structures produced through generative deep learning will satisfy these patterns, they often only possess specific target properties by chance and not by design, which makes molecular discovery via this route inefficient. In this work, we predict molecules with (Pareto-)optimal properties by combining a generative deep learning model that predicts three-dimensional conformations of molecules with a supervised deep learning model that takes these as inputs and predicts their electronic structure. Optimization of (multiple) molecular properties is achieved by screening newly generated molecules for desirable electronic properties and reusing hit molecules to retrain the generative model with a bias. The approach is demonstrated to find optimal molecules for organic electronics applications. Our method is generally applicable and eliminates the need for quantum chemical calculations during predictions, making it suitable for high-throughput screening in materials and catalyst design.

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Fig. 1: Workflow of the proposed method and distribution of molecules in the dataset.
Fig. 2: Distribution of electronic properties and structures of generated molecules.
Fig. 3: Cluster analysis for molecules with small ΔE.
Fig. 4: Multiproperty biasing.

Data availability

The OE62 dataset is available in ref. 24 and the OE62 + 340k G-SchNet molecule dataset is uploaded on (ref. 64). Quantum chemistry calculations carried out in this study are uploaded to NOMAD under DOI 10.17172/NOMAD/2022.07.02-1 (ref. 65). A supplementary data file showing the number of molecules predicted and used for training in each experiment and each loop is included as Supplementary Data 1.

Code availability

The modified G-SchNet version is available on GitHub ( and tagged as version v0.1 (minted version under DOI 10.5281/zenodo.7430248)66. The GitHub repository includes scripts to analyze the data and carry out PCA. SchNet + H is published in ref. 23 and available on (minted version under DOI 10.5281/zenodo.7424017)67. We include a tutorial for using SchNet + H and G-SchNet models for OE62 on figshare (, including instructions for installation64. Original tutorials for training and using G-SchNet and SchNet + H are available on GitHub with the original code of G-SchNet ( and SchNarc (, respectively.


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This work was funded by the Austrian Science Fund (FWF; J 4522-N) (J.W.), the EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems (EP/S022848/1) (R.J.M.), the EPSRC-funded Network+ on Artificial and Augmented Intelligence for Automated Scientific Discovery (EP/S000356/10) (R.J.M.) and the UKRI Future Leaders Fellowship program (MR/S016023/1) (R.J.M.). Computational resources have been provided by the Scientific Computing Research Technology Platform of the University of Warwick, the EPSRC-funded Northern Ireland High Performance Computing service (EP/T022175/1) via access to Kelvin2, the EPSRC-funded HPC Midlands+ computing service (EP/P020232/1) via access to Athena and Sulis and the EPSRC-funded High End Computing Materials Chemistry Consortium (EP/R029431/1) for access to the ARCHER2 UK National Supercomputing Service ( We thank N. Gebauer (TU Berlin) for fruitful discussions on the G-SchNet model. For the purpose of open access, we have applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising from this submission.

Author information

Authors and Affiliations



R.J.M. conceived the original idea and supervised the research project. R.J.M. and J.W. designed the research project. R.B. and J.W. trained the deep learning models and created the property-guided design workflow. J.G. and J.W. performed the dataset curation, predictions, model validation and data analysis. J.W. performed the quantum chemistry calculations. J.W. and R.J.M. wrote the manuscript with the help of the other authors. The manuscript reflects the contributions of all authors.

Corresponding authors

Correspondence to Julia Westermayr or Reinhard J. Maurer.

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Competing interests

R.J.M. is an editorial board member of the journal Communications Materials. All other authors declare no competing interests.

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Peer review information

Nature Computational Science thanks Camille Bilodeau and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team.

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Supplementary information

Supplementary Information

Supplementary Sections 1–9, Figs. 1–11 and Table 1.

Supplementary Data

Number of molecules predicted and used for training. Number of molecules used initially, obtained either from OE62 (initial loop), from OE62 + G-SchNet (initial loop for multiproperty biasing) or from G-SchNet alone (remaining loops). Molecules that were generated with G-SchNet are already sorted, hence the number of valid molecules is shown. The number of generated molecules was set to 200,000 for EA, ΔE and multiproperty biasing and to 100,000 for IP and ΔE (knockout) biasing. The third column shows the number of molecules that were selected for biasing G-SchNet. The fourth column shows the percentage of selected molecules with respect to the number of predicted molecules at this iteration.

Source data

Source Data for all Figures

Data depicted in Figs. 1–4.

Source Data Fig. 3

ChemDraw file of molecules depicted in Fig. 3.

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Westermayr, J., Gilkes, J., Barrett, R. et al. High-throughput property-driven generative design of functional organic molecules. Nat Comput Sci 3, 139–148 (2023).

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