Molecular similarity: Theory, applications, and perspectives
Molecular similarity pervades much of our understanding and rationalization of chemistry. This has become particularly evident in the current data-intensive era of chemical research, with similarity measures serving as the backbone of many Machine Learning (ML) supervised and unsupervised procedures. Here, we present a discussion on the role of molecular similarity in drug design, chemical space exploration, chemical "art" generation, molecular representations, and many more. We also discuss more recent topics in molecular similarity, like the ability to efficiently compare large molecular libraries.
Authors
- K. Eurídice Juárez‐Mercado (ORCID: https://orcid.org/0000-0002-3283-8284)
- Edgar López‐López (ORCID: https://orcid.org/0000-0002-7422-6059)
- Ramón Alain Miranda‐Quintana (ORCID: https://orcid.org/0000-0003-2121-4449)
- Lexin Chen (ORCID: https://orcid.org/0000-0002-9528-942X)
- José L. Medina‐Franco (ORCID: https://orcid.org/0000-0003-4940-1107)
- Juan F. Avellaneda-Tamayo (ORCID: https://orcid.org/0009-0003-1819-6187)
- Kenneth López Pérez
Institutions
- University of Florida (US)
- Instituto Politécnico Nacional (MX)
- Universidad Nacional Autónoma de México (MX)
Publication Details
- Journal
- Artificial Intelligence Chemistry
- Published
- 2024-08-31
- DOI
- https://doi.org/10.1016/j.aichem.2024.100077
- Citations
- 35
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 12.65
Funders
- National Institutes of Health
- Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México
- National Institute of General Medical Sciences