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Deep contrastive learning of molecular conformation for efficient property prediction

Abstract

Data-driven deep learning algorithms provide accurate prediction of high-level quantum-chemical molecular properties. However, their inputs must be constrained to the same quantum-chemical level of geometric relaxation as the training dataset, limiting their flexibility. Adopting alternative cost-effective conformation generative methods introduces domain-shift problems, deteriorating prediction accuracy. Here we propose a deep contrastive learning-based domain-adaptation method called Local Atomic environment Contrastive Learning (LACL). LACL learns to alleviate the disparities in distribution between the two geometric conformations by comparing different conformation-generation methods. We found that LACL forms a domain-agnostic latent space that encapsulates the semantics of an atom’s local atomic environment. LACL achieves quantum-chemical accuracy while circumventing the geometric relaxation bottleneck and could enable future application scenarios such as inverse molecular engineering and large-scale screening. Our approach is also generalizable from small organic molecules to long chains of biological and pharmacological molecules.

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Fig. 1: Comparison of molecular prediction methodologies between the previous approach and our proposed method.
Fig. 2: Overall LACL schematic.

Data availability

The preprocessed data in this work for reproducing the results are available on figshare at https://doi.org/10.6084/m9.figshare.24445129 (ref. 52). The model checkpoints used in this work for reproducing the results are available on GitHub at https://github.com/parkyjmit/LACL and figshare at https://doi.org/10.6084/m9.figshare.24456802 (ref. 53). Source data are provided with this paper.

Code availability

The Python code capsule of this work including the training script for reproducing the results is available on GitHub at https://github.com/parkyjmit/LACL and figshare at https://doi.org/10.6084/m9.figshare.24456802 (ref. 53).

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Acknowledgements

Y.J.P. was supported by a grant from the National Research Foundation of Korea (NRF) funded by the Korean government, Ministry of Science and ICT (MSIT) (no. 2021R1A6A3A01086766). The 05-Neuron supercomputer was provided by the Korea Institute of Science and Technology Information (KISTI) National Supercomputing Center for Y.J.P. Y.J.P., H.K., J.J. and S.Y. were supported by Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) (2021-0-01343: Artificial Intelligence Graduate School Program (Seoul National University)), National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2022R1A3B1077720) and the BK21 FOUR program of the Education and Research Program for Future ICT Pioneers, Seoul National University in 2023. We express our gratitude to J. Im at the Chemical Data-driven Research Center in the Korea Research Institute of Chemical Technology (KRICT) for his valuable insights and discussion on the content of this paper.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea

    Yang Jeong Park, HyunGi Kim, Jeonghee Jo & Sungroh Yoon

  2. Institute of New Media and Communications, Seoul National University, Seoul, Republic of Korea

    Yang Jeong Park, Jeonghee Jo & Sungroh Yoon

  3. Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yang Jeong Park

  4. Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, Republic of Korea

    Sungroh Yoon

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Contributions

Y.J.P. conceived the study. Y.J.P. and S.Y. supervised the research. Y.J.P. designed and implemented the deep learning framework. Y.J.P., H.K. and J.J. conducted benchmarks and case studies. All authors participated in the preparation (writing and drawing) of the paper and the analysis of experimental results. All authors reviewed and edited the submitted version of the paper.

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Yang Jeong Park or Sungroh Yoon.

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Extended data

Extended Data Fig. 1 Comparison between a backbone ALIGNN and the LACL model from the point of view of accuracy and inference time.

(a) Parity plot of trained ALIGNN and LACL model for various regression targets in QM9 dataset. Molecular conformation data from both the DFT domain and the CGCF domain is used as a test dataset. μ is the dipole moment. (b) Comparison of computation time between an ALIGNN model with DFT geometric relaxation and the LACL model with MMFF geometric relaxation. Geometric relaxations running on two 24-core Intel Cascade Lake i9 CPUs and GNN architectures running on a single NVIDIA RTX3090 graphics processing unit (GPU). The bar indicates the mean of computation time and the error bar indicates the standard deviation. Five samples were collected for runtime calculation, except When the number of heavy atoms was one (three samples).

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Extended Data Fig. 2 2-D t-SNE visualization of trained node and graph representations from both ALIGNN and LACL model for bandgap and internal energy at 0 K (U0) regression in QM9 dataset.

(a) 2-D t-SNE visualization of trained representations of the local atomic environment from LACL model for bandgap regression in QM9 dataset. Molecular conformation data from both DFT and CGCF domains are used as the test dataset. Orange, sky blue, green, yellow, and blue-colored point indicates hydrogen, carbon, nitrogen, oxygen, and fluorine, respectively. To visualize the node representation of different molecules, several example molecules are shown. The atom surrounded by a green circle is a nitrogen atom belonging to the cyanyl group. The atom surrounded by the purple circle is the oxygen atom included in the ring. The hydrogen, carbon, nitrogen, and oxygen atoms in molecules are indicated as white, gray, purple, and red color, respectively. (b) t-SNE visualization for trained node (atom-level) and graph (molecule-level) representations. Representations are visualized for each level, feature, model, and domain.

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Supplementary Sections 1–9, Figs. 1–10 and Tables 1–4.

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Park, Y.J., Kim, H., Jo, J. et al. Deep contrastive learning of molecular conformation for efficient property prediction.
Nat Comput Sci (2023). https://doi.org/10.1038/s43588-023-00560-w

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  • Received: 20 March 2023

  • Accepted: 31 October 2023

  • Published: 04 December 2023

  • DOI: https://doi.org/10.1038/s43588-023-00560-w

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