fragSMILES as a chemical string notation for advanced fragment and chirality representation
Open Access
- 29 January 2025
- journal article
- research article
- Published by Springer Nature in Communications Chemistry
- Vol. 8 (1) , 1-9
Abstract
Generative models have revolutionized de novo drug design, allowing to produce molecules on-demand with desired physicochemical and pharmacological properties. String based molecular representations, such as SMILES (Simplified Molecular Input Line Entry System) and SELFIES (Self-Referencing Embedded Strings), have played a pivotal role in the success of generative approaches, thanks to their capacity to encode atom- and bond- information and ease-of-generation. However, such ‘atom-level’ string representations could have certain limitations, in terms of capturing information on chirality, and synthetic accessibility of the corresponding designs. In this paper, we present fragSMILES, a novel fragment-based molecular representation in the form of string. fragSMILES encode fragments in a ‘chemically-meaningful’ way via a novel graph-reduction approach, allowing to obtain an efficient, interpretable, and expressive molecular representation, which also avoids fragment redundancy. fragSMILES contributes to the field of fragment-based representation, by reporting fragments and their ‘breaking’ bonds independently. Moreover, fragSMILES also embeds information of molecular chirality, thereby overcoming known limitations of existing string notations. When compared with SMILES, SELFIES and t-SMILES for de novo design, the fragSMILES notation showed its promise in generating molecules with desirable biochemical and scaffolds properties.Keywords
All Related Versions
This publication has 68 references indexed in Scilit:
- Impact of word embedding models on text analytics in deep learning environment: a reviewArtificial Intelligence Review, 2023
- Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block AssemblyAdvanced Science, 2023
- SELFIES and the future of molecular string representationsPatterns, 2022
- Natural language processing: state of the art, current trends and challengesMultimedia Tools and Applications, 2022
- Model complexity of deep learning: a surveyKnowledge and Information Systems, 2021
- Exploring chemical space using natural language processing methodologies for drug discoveryDrug Discovery Today, 2020
- Chiral Cliffs: Investigating the Influence of Chirality on Binding AffinityChemMedChem, 2018
- De Novo Design of Bioactive Small Molecules by Artificial IntelligenceMolecular Informatics, 2018
- Generative Recurrent Networks for De Novo Drug DesignMolecular Informatics, 2017
- The role of databases in support of computational chemistry calculationsJournal of Computational Chemistry, 1996