Chemical Topic Modeling: Exploring Molecular Data Sets Using a Common Text-Mining Approach
By Nadine Schneider, Nikolas Fechner, Gregory A. Landrum, N. Stiefl. 2017
24 citations recorded by the source corpus.
Big data is one of the key transformative factors which increasingly influences all aspects of modern life. Although this transformation brings vast opportunities it also generates novel challenges, not the least of which is organizing and searching this data deluge. The field of medicinal chemistry is not different: more and more data are being generated, for instance, by technologies such as DNA encoded libraries, peptide libraries, text mining of large literature corpora, and new in silico enumeration methods. Handling those huge sets of molecules effectively is quite challenging and requires compromises that often come at the expense of the interpretability of the results. In order to find an intuitive and meaningful approach to organizing large molecular data sets, we adopted a probabilistic framework called "topic modeling" from the text-mining field. Here we present the first chemistry-related implementation of this method, which allows large molecule sets to be assigned to "chemical topics" and investigating the relationships between those. In this first study, we thoroughly evaluate this novel method in different experiments and discuss both its disadvantages and advantages. We show very promising results in reproducing human-assigned concepts using the approach to identify and retrieve chemical series from sets of molecules. We have also created an intuitive visualization of the chemical topics output by the algorithm. This is a huge benefit compared to other unsupervised machine-learning methods, like clustering, which are commonly used to group sets of molecules. Finally, we applied the new method to the 1.6 million molecules of the ChEMBL22 data set to test its robustness and efficiency. In about 1 h we built a 100-topic model of this large data set in which we could identify interesting topics like "proteins", "DNA", or "steroids". Along with this publication we provide our data sets and an open-source implementation of the new method (CheTo) which will be part of an upcoming version of the open-source cheminformatics toolkit RDKit.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computational Theory and Mathematics
In Molecular Biology
Citations in the corpus
- Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition (2018, neutral): “13 Another pop ular NLP approach is Word2vec 14 which learns high dimensional embeddings of words where vectors of similar words end up near in vector space.”
- Ligand biological activity predicted by cleaning positive and negative chemical correlations (2019, background): “However, regardless of how one defines chemical motifs— common strategies include using pharmacological intuitions (15– 17), enumerating all linear or circular fragments below a certain size around every atom (17, 18), or unsupervised learning (19)— a molecule has many motifs, but only a few are imp”