# Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery

arXiv: https://arxiv.org/abs/2002.06053 · HTML: https://mlsanity.com/paper/2002.06053/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2020.

## Built on by (most cited first, 24 of 24)

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- [Geometric Deep Learning on Molecular Representations](https://mlsanity.com/paper/2107.12375/) (2021)
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- [Efficient Evolutionary Search Over Chemical Space with Large Language Models](https://mlsanity.com/paper/2406.16976/) (2024)
- [Artificial Intelligence in Drug Discovery: Applications and Techniques](https://mlsanity.com/paper/2106.05386/) (2021)
- [Bayesian Optimization of Catalysis With In-Context Learning](https://mlsanity.com/paper/2304.05341/) (2023)
- [Natural Language Processing for Smart Healthcare](https://mlsanity.com/paper/2110.15803/) (2021)
- [ACEGEN: Reinforcement learning of generative chemical agents for drug discovery](https://mlsanity.com/paper/2405.04657/) (2024)
- [Transformers and Large Language Models for Chemistry and Drug Discovery](https://mlsanity.com/paper/2310.06083/) (2023)
- [Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry](https://mlsanity.com/paper/2401.03369/) (2024)
- [Molecular Joint Representation Learning via Multi-modal Information](https://mlsanity.com/paper/2211.14042/) (2022)
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- [Graph-in-Graph Network for Automatic Gene Ontology Description Generation](https://mlsanity.com/paper/2206.05311/) (2022)
- [A Large Encoder-Decoder Family of Foundation Models For Chemical Language](https://mlsanity.com/paper/2407.20267/) (2024)
- [MolVision: Molecular Property Prediction with Vision Language Models](https://mlsanity.com/paper/2507.03283/) (2025)
- [Pushing the Limits of Machine Design: Automated CPU Design with AI](https://mlsanity.com/paper/2306.12456/) (2023)
- [An Introduction to Natural Language Processing Techniques and Framework for Clinical Implementation in Radiation Oncology](https://mlsanity.com/paper/2311.02205/) (2023)
- [SAFE setup for generative molecular design](https://mlsanity.com/paper/2410.20232/) (2024)
- [MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design](https://mlsanity.com/paper/2504.15587/) (2025)
- [An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design](https://mlsanity.com/paper/2110.05478/) (2021)
- [Hierarchical Structure Enhances the Convergence and Generalizability of Linear Molecular Representation](https://mlsanity.com/paper/2402.02164/) (2024)
- [Property-guided Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processing](https://mlsanity.com/paper/2405.11783/) (2024)
- [Expanding Chemical Representation with k-mers and Fragment-based Fingerprints for Molecular Fingerprinting](https://mlsanity.com/paper/2403.19844/) (2024)
- [A Hitchhiker's Guide to Deep Chemical Language Processing for Bioactivity Prediction](https://mlsanity.com/paper/2407.12152/) (2024)

## Abstract (arXiv)

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.