Exploring new ways to represent and discover organic molecules is critical to the development of new therapies. Fingerprinting algorithms are used to encode or machine-read organic molecules. Molecular encodings facilitate the computation of distance and similarity measurements to support tasks such as similarity search or virtual screening. Motivated by the ubiquity of carbon and the emerging structured patterns, we propose a parametric approach for molecular encodings using carbon-based multilevel atomic neighborhoods. It implements a walk along the carbon chain of a molecule to compute different representations of the neighborhoods in the form of a binary or numerical array that can later be exported into an image. Applied to the task of binary peptide classification, the evaluation was performed by using forty-nine encodings of twenty-nine data sets from various biomedical fields, resulting in well over 1421 machine learning models. By design, the parametric approach is domain- and task-agnostic and scopes all organic molecules including unnatural and exotic amino acids as well as cyclic peptides. Applied to peptide classification, our results point to a number of promising applications and extensions. The parametric approach was developed as a Python package (cmangoes), the source code and documentation of which can be found at https://github.com/ghattab/cmangoes and https://doi.org/10.5281/zenodo.7483771.
Exploring new ways to represent and discover organic molecules is critical for developing novel therapies. With recent advances in bioinformatics, virtual screening of databases is possible. However, biochemical data must be encoded using computer algorithms to make them machine-readable, taking into account distance and similarity measures to support tasks such as similarity searching. Motivated by the ubiquity of the carbon element and the structured patterns that emerge, we propose a parametric approach to molecular encodings of carbon-based multilevel atomic neighborhoods. It implements a walk along the carbon chain of an organic molecule to compute different representations of its feature encoding in the form of a binary or numerical array that can be exported later into an image. Resulting encodings are reproducible and readily formatted for various domain tasks including machine learning tasks. This approach was evaluated using a 10-fold stratified cross validation for binary classification with eight data sets and six different encodings (384 models) in the domain knowledge of cell-penetrating peptides. The parametric approach is built on open-source software and is implemented as a Python package (cmangoes). Source code and documentation are available at https://github.com/ghattab/cmangoes.
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