Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties

TL;DR

This paper investigates chemical word segmentation methods for molecules to identify key substructures linked to protein-ligand binding, enhancing understanding of chemical features relevant to drug discovery.

Contribution

It introduces a pipeline to analyze chemical vocabularies generated by subword tokenization algorithms and identifies target-specific key chemical words associated with binding affinity.

Findings

Key chemical words are specific to protein targets.

Identified chemical words correspond to known pharmacophores.

Analysis reveals the chemical significance of subword units in binding.

Abstract

Machine learning models have found numerous successful applications in computational drug discovery. A large body of these models represents molecules as sequences since molecular sequences are easily available, simple, and informative. The sequence-based models often segment molecular sequences into pieces called chemical words (analogous to the words that make up sentences in human languages) and then apply advanced natural language processing techniques for tasks such as $\textit{de novo}$ drug design, property prediction, and binding affinity prediction. However, the chemical characteristics and significance of these building blocks, chemical words, remain unexplored. This study aims to investigate the chemical vocabularies generated by popular subword tokenization algorithms, namely Byte Pair Encoding (BPE), WordPiece, and Unigram, and identify key chemical words associated with protein-ligand binding. To this end, we build a language-inspired pipeline that treats high affinity ligands of protein targets as documents and selects key chemical words making up those ligands based on tf-idf weighting. Further, we conduct case studies on a number of protein families to analyze the impact of key chemical words on binding. Through our analysis, we find that these key chemical words are specific to protein targets and correspond to known pharmacophores and functional groups. Our findings will help shed light on the chemistry captured by the chemical words, and by machine learning models for drug discovery at large.