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Limitations of representation learning in small molecule property prediction

Author

Listed:
  • Ana Laura Dias

    (Universidade de Lisboa)

  • Latimah Bustillo

    (Universidade de Lisboa)

  • Tiago Rodrigues

    (Universidade de Lisboa)

Abstract

Representation learning is making inroads into drug discovery. A study in Nature Communications emphasizes multiple limitations in property prediction. The results suggest that continued research and improvements are required for this specific area that coalesces machine learning and molecular medicine.

Suggested Citation

  • Ana Laura Dias & Latimah Bustillo & Tiago Rodrigues, 2023. "Limitations of representation learning in small molecule property prediction," Nature Communications, Nature, vol. 14(1), pages 1-2, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41967-3
    DOI: 10.1038/s41467-023-41967-3
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    References listed on IDEAS

    as
    1. Jianyuan Deng & Zhibo Yang & Hehe Wang & Iwao Ojima & Dimitris Samaras & Fusheng Wang, 2023. "A systematic study of key elements underlying molecular property prediction," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    2. Benjamin J. Shields & Jason Stevens & Jun Li & Marvin Parasram & Farhan Damani & Jesus I. Martinez Alvarado & Jacob M. Janey & Ryan P. Adams & Abigail G. Doyle, 2021. "Bayesian reaction optimization as a tool for chemical synthesis," Nature, Nature, vol. 590(7844), pages 89-96, February.
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