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Neighborhood Complex Based Machine Learning (NCML) Models for Drug Design

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Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data (IMIMIC 2021, TDA4MedicalData 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12929))

Abstract

The importance of drug design cannot be overemphasized. Recently, artificial intelligence (AI) based drug design has begun to gain momentum due to the great advancement in experimental data, computational power and learning models. However, a major issue remains for all AI-based learning models is efficient molecular representations. Here we propose Neighborhood complex (NC) based molecular featurization (or feature engineering), for the first time. In particular, we reveal deep connections between NC and Dowker complex (DC) for molecular interaction based bipartite graphs, for the first time. Further, NC-based persistent spectral models are developed and the associated persistent attributes are used as molecular descriptors or fingerprints. To test our models, we consider protein-ligand binding affinity prediction. Our NC-based machine learning (NCML) models, in particular, NC-based gradient boosting tree (NC-GBT), are tested on three most-commonly used datasets, i.e., including PDBbind-v2007, PDBbind-v2013 and PDBbind-v2016, and extensively compared with other existing state-of-the-art models. It has been found that our NCML models can achieve state-of-the-art results.

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Authors and Affiliations

  1. Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, 637371, Singapore

    Xiang Liu & Kelin Xia

  2. Chern Institute of Mathematics and LPMC, Nankai University, Tianjin, 300071, China

    Xiang Liu

  3. Center for Topology and Geometry Based Technology, Hebei Normal University, Hebei, 050024, China

    Xiang Liu

Authors
  1. Xiang Liu
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  2. Kelin Xia
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Correspondence to Kelin Xia .

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Editors and Affiliations

  1. University of Bern, Bern, Switzerland

    Mauricio Reyes

  2. CISUC, FCTUC, Coimbra, Portugal

    Pedro Henriques Abreu

  3. INESC, FEUP, Porto, Portugal

    Jaime Cardoso

  4. Santa Clara University, Santa Clara, CA, USA

    Mustafa Hajij

  5. National Institutes of Health, Bethesda, MD, USA

    Ghada Zamzmi

  6. Massachusetts General Hospital, Harvard, Boston, MA, USA

    Paul Rahul

  7. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Lokendra Thakur

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Liu, X., Xia, K. (2021). Neighborhood Complex Based Machine Learning (NCML) Models for Drug Design. In: Reyes, M., et al. Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data. IMIMIC TDA4MedicalData 2021 2021. Lecture Notes in Computer Science(), vol 12929. Springer, Cham. https://doi.org/10.1007/978-3-030-87444-5_9

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  • DOI: https://doi.org/10.1007/978-3-030-87444-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87443-8

  • Online ISBN: 978-3-030-87444-5

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