Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network-Reference-Cited by-同舟云学术

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Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network

Published:2022-02-24 Issue:10 Volume:43 Page:728-739
ISSN:0192-8651
Container-title:Journal of Computational Chemistry
language:en
Short-container-title:J Comput Chem

Author:

Nikolaienko Tymofii¹²^ORCID,Gurbych Oleksandr³⁴,Druchok Maksym¹⁵^ORCID

Affiliation:

1. SoftServe, Inc. Lviv Ukraine

2. Faculty of Physics Taras Shevchenko National University of Kyiv Kyiv Ukraine

3. Blackthorn AI Ltd. London UK

4. Department of Artificial Intelligence Systems Lviv Polytechnic National University Lviv Ukraine

5. Institute for Condensed Matter Physics NAS of Ukraine Lviv Ukraine

Publisher

Wiley

Subject

Computational Mathematics,General Chemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcc.26831

Reference107 articles.

1. Applications of machine learning in drug discovery and development

2. COMPARISON OF ARTIFICIAL INTELLIGENCE METHODS FOR MODELING PHARMACEUTICAL QSARS

3. Drug Discovery Using Support Vector Machines. The Case Studies of Drug-likeness, Agrochemical-likeness, and Enzyme Inhibition Predictions

4. Virtual Screening of Molecular Databases Using a Support Vector Machine

5. Toward more realistic drug-target interaction predictions

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1. AI's role in pharmaceuticals: Assisting drug design from protein interactions to drug development;Artificial Intelligence Chemistry;2024-06

2. GraphMHC: Neoantigen prediction model applying the graph neural network to molecular structure;PLOS ONE;2024-03-27

3. Machine Learning for Sequence and Structure-Based Protein–Ligand Interaction Prediction;Journal of Chemical Information and Modeling;2024-02-22

4. An ensemble‐based approach to estimate confidence of predicted protein–ligand binding affinity values;Molecular Informatics;2024-02-15

5. Machine learning‐assisted search for novel coagulants: When machine learning can be efficient even if data availability is low;Journal of Computational Chemistry;2024-01-04

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