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Empirical Evidence for the Fragment level Understanding on Drug Molecular Structure of LLMs

arXiv 2401.07657 ↗First posted 15 Jan 2024cs.LG cs.CE q-bio.BMcited by 0

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Abstract (arXiv)

AI for drug discovery has been a research hotspot in recent years, and SMILES-based language models has been increasingly applied in drug molecular design. However, no work has explored whether and how language models understand the chemical spatial structure from 1D sequences. In this work, we pre-train a transformer model on chemical language and fine-tune it toward drug design objectives, and investigate the correspondence between high-frequency SMILES substrings and molecular fragments. The results indicate that language models can understand chemical structures from the perspective of molecular fragments, and the structural knowledge learned through fine-tuning is reflected in the high-frequency SMILES substrings generated by the model.

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References · 21

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 384
  3. 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 363
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  6. 2020Hierarchical Generation of Molecular Graphs using Structural Motifscited by 204
  7. 2018Optimization of Molecules via Deep Reinforcement Learningcited by 199
  8. 2020MoFlow: An Invertible Flow Model for Generating Molecular Graphscited by 183
  9. 2017Deep Reinforcement Learning for De-Novo Drug Designcited by 161
  10. 2022Sample Efficiency Matters: A Benchmark for Practical Molecular Optimizationcited by 151
  11. 2023Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systemscited by 118
  12. 2017Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Modelscited by 116
9 more
  1. 2023The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4cited by 112
  2. 2021Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generationcited by 68
  3. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  4. 2017SMILES Enumeration as Data Augmentation for Neural Network Modeling of Moleculescited by 58
  5. 2020Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learningcited by 57
  6. 2021Molecule Generation by Principal Subgraph Mining and Assemblingcited by 53
  7. 2023De Novo Molecular Generation via Connection-aware Motif Miningcited by 45
  8. 2023De novo Drug Design using Reinforcement Learning with Multiple GPT Agentscited by 19
  9. 2022Probabilistic Generative Transformer Language models for Generative Design of Moleculescited by 8

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