Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery

Abstract

Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge. Advances in natural language processing (NLP) methodologies in the processing of spoken languages accelerated the application of NLP to elucidate hidden knowledge in textual representations of these biochemical entities and then use it to construct models to predict molecular properties or to design novel molecules. This review outlines the impact made by these advances on drug discovery and aims to further the dialogue between medicinal chemists and computer scientists.

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Cited by in corpus (8)

• Natural Language Processing for Smart Healthcare
• ChemBoost: A chemical language based approach for protein-ligand binding affinity prediction
• A Large Encoder-Decoder Family of Foundation Models For Chemical Language
• A Hitchhiker's Guide to Deep Chemical Language Processing for Bioactivity Prediction
• Artificial Intelligence in Drug Discovery: Applications and Techniques
• Property-guided Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processing
• An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design
• Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties