SmileyLlama: Modifying Large Language Models for Directed Chemical Space Exploration

[2409.02231] SmileyLlama: Modifying Large Language Models for Directed Chemical Space Exploration Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Physics > Chemical Physics arXiv:2409.02231 (physics) [Submitted on 3 Sep 2024 ( v1 ), last revised 10 Apr 2026 (this version, v5)] Title: SmileyLlama: Modifying Large Language Models for Directed Chemical Space Exploration Authors: Joseph M. Cavanagh , Kunyang Sun , Andrew Gritsevskiy , Dorian Bagni , Yingze Wang , Thomas D. Bannister , Teresa Head-Gordon View a PDF of the paper titled SmileyLlama: Modifying Large Language Models for Directed Chemical Space Exploration, by Joseph M. Cavanagh and 6 other authors View PDF HTML (experimental) Abstract: We show that large language model (LLMs) can be transformed via supervised fine-tuning (SFT) of engineered prompts into SmileyLlama for exploring the chemical space of drug molecules. We benchmark SmileyLlama against pre-trained LLMs and chemical language models (CLM) trained from scratch for generating valid and novel drug-like molecules, and use direct preference optimization (DPO) to both improve SmileyLlama's adherence to a prompt and as part of the iMiner reinforcement learning framework to predict molecules with optimized 3D conformations and high binding affinity to drug targets. By training an LLM to speak directly as a CLM, while retaining most of its natural language capabilities, we show that we can reliably generate molecules with user-specified properties rather than acting only as a chatbot with knowledge of chemistry or as a virtual assistant. While SmileyLlama is geared toward drug discovery, the SFT/DPO/LLM framework can be extended to other chemical, biological, and materials applications. Subjects: Chemical Physics (physics.chem-ph) ; Machine Learning (cs.LG) Cite as: arXiv:2409.02231 [physics.chem-ph] (or arXiv:2409.02231v5 [physics.chem-ph] for this version) https://doi.org/10.48550/arXiv.2409.02231 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Teresa Head-Gordon [ view email ] [v1] Tue, 3 Sep 2024 18:59:20 UTC (3,405 KB) [v2] Mon, 9 Sep 2024 18:07:02 UTC (3,407 KB) [v3] Mon, 30 Jun 2025 10:36:58 UTC (3,527 KB) [v4] Wed, 12 Nov 2025 00:51:46 UTC (5,676 KB) [v5] Fri, 10 Apr 2026 22:29:59 UTC (1,978 KB) Full-text links: Access Paper: View a PDF of the paper titled SmileyLlama: Modifying Large Language Models for Directed Chemical Space Exploration, by Joseph M. Cavanagh and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: physics.chem-ph < prev | next > new | recent | 2024-09 Change to browse by: cs cs.LG physics References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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