mlsanityData to 14 Oct 2026

Small Molecule Optimization with Large Language Models

arXiv 2407.18897 ↗First posted 26 Jul 2024cs.LG cs.NE q-bio.QMcited by 6

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Abstract (arXiv)

Molecular optimization, the process of designing molecules with desirable properties, represents a critical challenge in drug discovery. The recent advancements in large language models (LLMs) have opened new opportunities for their integration with traditional molecular optimization algorithms to improve performance. In this work, we propose Molecular Language Model powered Evolutionary Algorithm (Mol-E), an evolutionary algorithm that relies on the generative capabilities of LLMs trained on molecules and molecular properties. Scientific Contribution. Mol-E obtains the highest aggregate Top-10 AUC among the comparable full-23-task results considered here, scoring 17.500 in the task-agnostic regime, in which the oracle is treated strictly as a black box, and 20.551 in the task-informed regime, in which the optimizer receives a fixed semantic description of the objective. Mol-E also improves over the evaluated baselines on multi-property optimization with docking against DRD2, MK2, and AChE.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 50 of its 107 references that are in the map. First posted on arXiv 26 Jul 2024.

Builds on

50 of 107 references.

References · 50

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  5. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  6. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  7. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  8. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,100
  9. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  10. 2023Mistral 7Bcited by 3,164
  11. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
  12. 2022Training Compute-Optimal Large Language Modelscited by 3,019
38 more
  1. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
  2. 2023FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioningcited by 2,598
  3. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,998
  4. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,984
  5. 2017Mixed Precision Trainingcited by 1,156
  6. 2023LIMA: Less Is More for Alignmentcited by 1,100
  7. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,051
  8. 2024Gemma: Open Models Based on Gemini Research and Technologycited by 916
  9. 2023Large Language Models as Optimizerscited by 864
  10. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 765
  11. 2019CTRL: A Conditional Transformer Language Model for Controllable Generationcited by 762
  12. 2022Galactica: A Large Language Model for Sciencecited by 744
  13. 2023PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallelcited by 660
  14. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 652
  15. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 392
  16. 2017Molecular De Novo Design through Deep Reinforcement Learningcited by 267
  17. 2023EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizerscited by 193
  18. 2022Translation between Molecules and Natural Languagecited by 177
  19. 2022Sample Efficiency Matters: A Benchmark for Practical Molecular Optimizationcited by 155
  20. 2023EvoPrompting: Language Models for Code-Level Neural Architecture Searchcited by 131
  21. 2022ChemBERTa-2: Towards Chemical Foundation Modelscited by 127
  22. 2023NEFTune: Noisy Embeddings Improve Instruction Finetuningcited by 100
  23. 2019SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discoverycited by 90
  24. 2020Guiding Deep Molecular Optimization with Genetic Explorationcited by 51
  25. 2022Retrieval-based Controllable Molecule Generationcited by 44
  26. 2023SELFormer: Molecular Representation Learning via SELFIES Language Modelscited by 40
  27. 2024Genetic-guided GFlowNets for Sample Efficient Molecular Optimizationcited by 39
  28. 2024On the Scalability of GNNs for Molecular Graphscited by 36
  29. 2024LICO: Large Language Models for In-Context Molecular Optimizationcited by 26
  30. 2023De novo Drug Design using Reinforcement Learning with Multiple GPT Agentscited by 19
  31. 2022BARTSmiles: Generative Masked Language Models for Molecular Representationscited by 18
  32. 2023Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Designcited by 17
  33. 2024Saturn: Sample-efficient Generative Molecular Design using Memory Manipulationcited by 15
  34. 2024ACEGEN: Reinforcement learning of generative chemical agents for drug discoverycited by 14
  35. 2023Deep Learning Methods for Small Molecule Drug Discovery: A Surveycited by 10
  36. 2024Diagnosing and fixing common problems in Bayesian optimization for molecule designcited by 9
  37. 2023Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Designcited by 8
  38. 2023Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Designcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2024SmileyLlama: Modifying Large Language Models for Directed Chemical Space Explorationcited by 14
  2. 2025A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solvingcited by 11
  3. 2025ExLLM: Experience-Enhanced LLM Optimization for Molecular Design and Beyondcited by 8
  4. 2025Coder as Editor: Code-driven Interpretable Molecular Optimizationcited by 1
  5. 2026FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimizationcited by 0
  6. 2026PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimizationcited by 0