A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization １．This is the first focused survey on using large language models (LLMs) for molecule generation and optimization, introducing a novel taxonomy based on learning paradigms—covering both tuning-free (e.g., zero-shot, in-context learning) and tuning-based (e.g., supervised fine-tuning, preference tuning) methods. ２．The survey highlights how LLMs are uniquely positioned for molecular discovery due to their emergent capabilities—such as in-context learning, reasoning, and instruction following—which allow them to generalize across diverse chemical tasks without task-specific retraining. ３．In molecule generation, LLMs are deployed via prompting strategies (e.g., LLM4GraphGen, MolReGPT) or adapted through supervised datasets (e.g., Mol-Instructions, LlaSMol, ChatMol). Preference-tuned models like SmileyLlama and Mol-MoE show improved fidelity to molecular constraints. ４．For molecule optimization, the review examines how LLMs refine existing molecules through goal-directed editing. Strategies include zero-shot optimization (LLM-MDE), retrieval-augmented prompting (ChatDrug), and evolution-based in-context learning (MOLLM, LLM-EO). ５．The survey identifies a trend toward hybrid frameworks combining fine-tuned worker models with external reasoning agents (e.g., MultiMol, DrugAssist), often leveraging GPT-4o or domain-specific scoring functions to enhance candidate selection and validation. ６．Multi-modal modeling is a growing focus, with models like UniMoT and Molx-Enhanced LLM incorporating graph or 3D inputs into LLMs via specialized tokenizers and embedding schemes, enabling structurally-aware generation and optimization. ７．Benchmarking frameworks are categorized into structure-based (validity, uniqueness, diversity) and property-based (LogP, QED, synthetic accessibility, Pareto-optimality) metrics. The paper also provides a detailed summary of standard datasets for pretraining and evaluation. ８．The survey emphasizes the limitations of current LLMs: hallucinations, lack of transparency, and domain-incoherent outputs. Future work should prioritize trustworthy generation, interpretability, and error-aware prompting to enhance reliability. ９．Emerging directions include LLM-driven agent frameworks that integrate external tools (e.g., retrosynthesis engines, docking software) for iterative design, as well as cross-modal models that jointly encode chemical topology, text, and spatial information. １０．A continuously updated repository of LLM-centric molecular research is provided at github, making this survey a central resource for the field. 💻Code: github.com/REAL-Lab-NU/Aweso… 📜Paper: arxiv.org/abs/2505.16094 #LLM #MoleculeGeneration #MolecularOptimization #DrugDiscovery #ChemLLM #AI4Science #InContextLearning #SMILES #MolecularDesign #LargeLanguageModels

📢 New paper: AskGNN - Making LLMs graph-aware through in-context learning! Our GNN-powered retriever learning-to-retrieve approach enables LLMs to process graph data effectively. No fine-tuning needed. 7 LLMs tested, 3 tasks, strong results. 🔗 arxiv.org/abs/2410.07074

🚨 Call for Workshop Papers at #KDD2024 🚨 Submit your paper to the KDD’24 Workshop on Resource-Efficient Learning for Knowledge Discovery 📆 June 30 chuxuzhang.github.io/RelKD/

***Internship Opportunity*** We're hiring interns. Come, join us in building an ML platform reinvented for real-time. You'll gain hands-on experience in building ML systems from the ground up. 💸 Stipend: ₹1L/month 🗓️ Start Date: May/June 2024 📍 Location: Virtual 🚀 Career Path: We'll roll out PPOs to top performers Open Roles: 1. ML 2. Infra 3. DevOps 4. Streaming Systems 5. UI/UX If you're interested, please fill out this form: forms.gle/SQxiXALXzztQyWbQA #internship #hiring

We heard that you missed the benefits of the standard registration. Don't worry, the deadline has been extended to February 18th. Register today: wsdm-conference.org/2024/reg… #WSDM2024 #WSDMCUP2024 #sigkdd #sigmod #sigir #sigweb #acm

📢 Interested in statistical machine learning and data science? Don't forget to submit your application to our Ph.D. program before Jan 5th! Details can be found at: statistics.northwestern.edu/… If you are also attending NeurIPS, feel free to talk to me!

#WSDM2024 is open for registration. Make sure to grab your spot soon. Early registration ends December 17th! Come learn from industry and academia experts such as @Google's VP Elizabeth Hamon Reid from and Nicolas Cristin (@nc2y). Register now at wsdm-conference.org/2024/reg…

🎉 Exciting News! 📢 Our paper on "GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs" has been accepted at #EMNLP findings! 📚🔍 Thanks for the co-authors @kaize0409, Kyumin Lee. State tuned for the preprint and code.

Working with Prof. Liu and Prof. Wang (my former phd and visiting phd students) to organize a special issue on Data Centric AI. Mathematics is a very decent journal. We are looking forward to your discussions on such interesting topics.

We are pleased to announce the call for Workshop Proposals for the #WSDM2024, which will take place for the first time in LATAM at Mérida, México wsdm-conference.org/2024/cal… Proposals Due: October 5, 2023 Acceptance Notifications: November 2, 2023

Oral: 4:00 pm – 4:20 pm, Tuesday, August 8, Room 201A Poster: 4:00 pm – 4:20 pm, Monday, August 7, Hall A, #386

🙋 Welcome to ReIKD @kdd_news !! ⏰Aug 7, 1-5 pm (PDT), 202B. Conversations on generative AI agents🤖 are also super welcome! #KDD2023

Announcing the call for proposals #WSDM2024 Cup. #WSDM2024 will take place in Mérida, México wsdm-conference.org/2024/cal… Submission: 8/15/2023 Notification: 8/31/2023 Previous prev partners #Google, #Amazon, #iQiYi, #Microsoft, #Wikimedia, #Adobe, #ByteDance, #Spotify, #Baidu.

Proud to be one of the DMMLers and to work with Dr. Liu, well deserved!@liuhuan Regents Professor is an AI explorer of 4 decades news.asu.edu/20230207-univer…

Few-shot learning on graphs via pretrained contrastive learning, ft. Zhen Tan, Song Wang, @kaize0409, @LiJundong, Huan Liu