title	date	author
Readme for the Mol-Infer project	March 15, 2021	Discrete Mathematics Lab, Kyoto University

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mol-infer: Molecular Inference

Mol-infer is a project developed by the Discrete Mathematics Lab at Kyoto Univerisity (ku-dml). After many years of research on original graph algorithms for inferring molecular structures, we decided to open-source our programs for public use. If you found it was useful in your work, please consider citing our paper(s) as well as this GitHub repository.

Overview of packages

We have uploaded our programs in the following packages, where each package is assigned one subfolder. All packages have a similar algorithmic structure and consist of four modules. However, modules are NOT compatible between packages, since they use different algorithms. You should think of different packages as different projects.

Notice: Please visit each package for details.

• Some packages may not be fully prepared.
• In addition to structural assumptions, we may make other assumptions on input chemical graphs. For example, in Cyclic and Cyclic_improved, the graphs should be 2-lean as well as cyclic.

Package 2LCC (August 2024)

• Input graphs: Arbitrary graphs (i.e., both cyclic and acyclic graphs can be treated at the same time) with some exceptions
• Reference:
  • B. Song, J. Zhu, N. A. Azam, K. Haraguchi, L. Zhao, T. Akutsu, Cycle-Configuration: A Novel Graph-theoteric Descriptor Set for Molecular ence, arXiv 2408.05136, 2024, https://arxiv.org/abs/2408.05136.

Package Polymer (May 2024)

• Input graphs: Arbitrary graphs (i.e., both cyclic and acyclic graphs can be treated at the same time)
• Reference:
  • R. Ido, S. Cao, J. Zhu, N. A. Azam, K. Haraguchi, L. Zhao, H. Nagamochi and T. Akutsu, A Method for ring Polymers Based on Linear Regression and Integer Programming, arXiv 2109.02628, 2021, https://arxiv.org/abs/2109.02628.

Package HPS (Mar 2024)

• Input graphs: Arbitrary graphs (i.e., both cyclic and acyclic graphs can be treated at the same time)
• Reference:
  • J. Zhu, N. A. Azam, K. Haraguchi, L. Zhao, H. Nagamochi and T. Akutsu, Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes, arXiv 2305.00801, 2023, https://arxiv.org/abs/2305.00801.

Package RMLRQ (May 2023)

• Input graphs: Arbitrary graphs (i.e., both cyclic and acyclic graphs can be treated at the same time)
• Reference:
  • J. Zhu, N. A. Azam, S. Cao, R. Ido, K. Haraguchi, L. Zhao, H. Nagamochi and T. Akutsu, Molecular Design Based on Integer Programming and Quadratic Descriptors in a Two-layered Model, arXiv 2209.13527, 2022, https://arxiv.org/abs/2209.13527.

Package Grid-neighbor-search (Dec 2021)

• Input graphs: Arbitrary graphs (i.e., both cyclic and acyclic graphs can be treated at the same time)
• Reference:
  • N. A. Azam, J. Zhu, K. Haraguchi, L. Zhao, H. Nagamochi and T. Akutsu. Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search, Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021.

Package ALR (Sep 2021)

• Input graphs: Arbitrary graphs (i.e., both cyclic and acyclic graphs can be treated at the same time)
• Reference:
  • J. Zhu, K. Haraguchi, H. Nagamochi and T. Akutsu: Adjustive Linear Regression and Its Application to the Inverse QSAR, Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS, 2021, https://doi.org/10.5220/0010853700003123.

Package 2LMM-LLR (Jul 2021)

• Input graphs: Arbitrary graphs (i.e., both cyclic and acyclic graphs can be treated at the same time)
• Reference:
  • J. Zhu, N.A. Azam, K. Haraguchi, L. Zhao, H. Nagamochi and T. Akutsu: An Inverse QSAR Method Based on Linear Regression and Integer Programming, 2021, http://arxiv.org/abs/2107.02381.

Package 2L-model (Mar 2021)

• Input graphs: Arbitrary graphs (i.e., both cyclic and acyclic graphs can be treated at the same time)
• Reference:
  • Y. Shi, J. Zhu, N.A. Azam, K. Haraguchi, L. Zhao, H. Nagamochi and T. Akutsu: An Inverse QSAR Method Based on a Two-Layered Model and Integer Programming, International Journal of Molecular Sciences, 22(6), 2021, https://doi.org/10.3390/ijms22062847.

Package Cyclic_improved (Jan 2021)

• Input graphs: Cyclic graphs.
• Reference:
  • J. Zhu, N.A. Azam, K. Haraguchi, L. Zhao, H. Nagamochi and T. Akutsu: An Improved Integer Programming Formulation for Inferring Chemical Compounds with Prescribed Topological Structures, Proceedings of IEA/AIE 2021 conference (https://ieaaie2021.wordpress.com), 2021, accepted.

Package Cyclic (Nov 2020)

• Input graphs: Cyclic graphs.
• References:
  • J. Zhu, N.A. Azam, F. Zhang, A. Shurbevski, K. Haraguchi, L. Zhao, H. Nagamochi and T. Akutsu: A Novel Method for Inferring of Chemical Compounds with Prescribed Topological Substructures Based on Integer Programming, 2020, submitted.
  • T. Akutsu and H. Nagamochi: A novel method for inference of chemical compounds with prescribed topological substructures based on integer programming, 2020, https://arxiv.org/abs/2010.09203.

Package Acyclic (Sep 2020)

• Input graphs: Graphs with no cycle (i.e., tree structured graphs)
• Reference:
  • N.A. Azam, J. Zhu, Y. Sun, Y. Shi, A. Shurbevski, L. Zhao, H. Nagamochi and T. Akutsu, A Novel Method for Inference of Acyclic Chemical Compounds with Bounded Branch-height Based on Artificial Neural Networks and Integer Programming, 2020, https://arxiv.org/abs/2009.09646.

Requirement

A standard C++ compiler and Python with some standard packages. See each package for detail please.

Acknowledgement

This project is partially supported by JSPS Grant (KAKENHI) 18H04113 and 22H00532.