IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-41967-3.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-41967-3
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-41967-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhiyuan Han & An Chen & Zejian Li & Mengtian Zhang & Zhilong Wang & Lixue Yang & Runhua Gao & Yeyang Jia & Guanjun Ji & Zhoujie Lao & Xiao Xiao & Kehao Tao & Jing Gao & Wei Lv & Tianshuai Wang & Jinji, 2024. "Machine learning-based design of electrocatalytic materials towards high-energy lithium||sulfur batteries development," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Gang Wang & Shinya Mine & Duotian Chen & Yuan Jing & Kah Wei Ting & Taichi Yamaguchi & Motoshi Takao & Zen Maeno & Ichigaku Takigawa & Koichi Matsushita & Ken-ichi Shimizu & Takashi Toyao, 2023. "Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. Xiaoqian Wang & Yang Huang & Xiaoyu Xie & Yan Liu & Ziyu Huo & Maverick Lin & Hongliang Xin & Rong Tong, 2023. "Bayesian-optimization-assisted discovery of stereoselective aluminum complexes for ring-opening polymerization of racemic lactide," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    4. Zi-Jing Zhang & Shu-Wen Li & João C. A. Oliveira & Yanjun Li & Xinran Chen & Shuo-Qing Zhang & Li-Cheng Xu & Torben Rogge & Xin Hong & Lutz Ackermann, 2023. "Data-driven design of new chiral carboxylic acid for construction of indoles with C-central and C–N axial chirality via cobalt catalysis," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    5. Wenhao Gao & Priyanka Raghavan & Connor W. Coley, 2022. "Autonomous platforms for data-driven organic synthesis," Nature Communications, Nature, vol. 13(1), pages 1-4, December.
    6. Adarsh Dave & Jared Mitchell & Sven Burke & Hongyi Lin & Jay Whitacre & Venkatasubramanian Viswanathan, 2022. "Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    7. Hongyuan Sheng & Jingwen Sun & Oliver Rodríguez & Benjamin B. Hoar & Weitong Zhang & Danlei Xiang & Tianhua Tang & Avijit Hazra & Daniel S. Min & Abigail G. Doyle & Matthew S. Sigman & Cyrille Costent, 2024. "Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    8. Yilei Wu & Chang-Feng Wang & Ming-Gang Ju & Qiangqiang Jia & Qionghua Zhou & Shuaihua Lu & Xinying Gao & Yi Zhang & Jinlan Wang, 2024. "Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    9. Zhenxing Wang & Yunjun Yu & Kallol Roy & Cheng Gao & Lei Huang, 2023. "The Application of Machine Learning: Controlling the Preparation of Environmental Materials and Carbon Neutrality," IJERPH, MDPI, vol. 20(3), pages 1-4, January.
    10. Kelsey L. Snapp & Benjamin Verdier & Aldair E. Gongora & Samuel Silverman & Adedire D. Adesiji & Elise F. Morgan & Timothy J. Lawton & Emily Whiting & Keith A. Brown, 2024. "Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    11. Nathan J. Szymanski & Pragnay Nevatia & Christopher J. Bartel & Yan Zeng & Gerbrand Ceder, 2023. "Autonomous and dynamic precursor selection for solid-state materials synthesis," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    12. Jiaru Bai & Sebastian Mosbach & Connor J. Taylor & Dogancan Karan & Kok Foong Lee & Simon D. Rihm & Jethro Akroyd & Alexei A. Lapkin & Markus Kraft, 2024. "A dynamic knowledge graph approach to distributed self-driving laboratories," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    13. Artem I. Leonov & Alexander J. S. Hammer & Slawomir Lach & S. Hessam M. Mehr & Dario Caramelli & Davide Angelone & Aamir Khan & Steven O’Sullivan & Matthew Craven & Liam Wilbraham & Leroy Cronin, 2024. "An integrated self-optimizing programmable chemical synthesis and reaction engine," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41967-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.