mlsanityData to 7 Oct 2026

Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery

arXiv 2002.06053 ↗First posted 10 Feb 2020q-bio.BM cs.CL cs.LGcited by 24

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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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Feb 2020.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 5; 2024: 9; 2025: 2.

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  1. 2023Does Synthetic Data Generation of LLMs Help Clinical Text Mining?cited by 151
  2. 2021Geometric Deep Learning on Molecular Representationscited by 113
  3. 2022Local Latent Space Bayesian Optimization over Structured Inputscited by 78
  4. 2024Efficient Evolutionary Search Over Chemical Space with Large Language Modelscited by 59
  5. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
  6. 2023Bayesian Optimization of Catalysis With In-Context Learningcited by 20
  7. 2021Natural Language Processing for Smart Healthcarecited by 14
  8. 2024ACEGEN: Reinforcement learning of generative chemical agents for drug discoverycited by 14
  9. 2023Transformers and Large Language Models for Chemistry and Drug Discoverycited by 13
  10. 2024Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometrycited by 10
  11. 2022Molecular Joint Representation Learning via Multi-modal Informationcited by 8
  12. 2021Unassisted Noise Reduction of Chemical Reaction Data Setscited by 5
12 more
  1. 2022Graph-in-Graph Network for Automatic Gene Ontology Description Generationcited by 5
  2. 2024A Large Encoder-Decoder Family of Foundation Models For Chemical Languagecited by 5
  3. 2025MolVision: Molecular Property Prediction with Vision Language Modelscited by 5
  4. 2023Pushing the Limits of Machine Design: Automated CPU Design with AIcited by 2
  5. 2023An Introduction to Natural Language Processing Techniques and Framework for Clinical Implementation in Radiation Oncologycited by 2
  6. 2024SAFE setup for generative molecular designcited by 2
  7. 2025MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Designcited by 2
  8. 2021An In-depth Summary of Recent Artificial Intelligence Applications in Drug Designcited by 1
  9. 2024Property-guided Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processingcited by 1
  10. 2024Hierarchical Structure Enhances the Convergence and Generalizability of Linear Molecular Representationcited by 0
  11. 2024Expanding Chemical Representation with k-mers and Fragment-based Fingerprints for Molecular Fingerprintingcited by 0
  12. 2024A Hitchhiker's Guide to Deep Chemical Language Processing for Bioactivity Predictioncited by 0