Kaize Ding

Assistant Professor @Northwestern; CS Ph.D. @ASU DMML; Reliable and Efficient AI; Formerly @GoogleDeepMind @MSFTResearch, @Amazon Alexa AI;

[Kaize Ding @kaize0409]

@weijie444 Congratulations!

[Kaize Ding @kaize0409]

@realhuawei @SCAI_ASU Congratulations！

[Biology+AI Daily @BiologyAIDaily]

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: https://t.co/UxjZHjKwSK
📜Paper: https://t.co/DxzJMudNWX
#LLM #MoleculeGeneration #MolecularOptimization #DrugDiscovery #ChemLLM #AI4Science #InContextLearning #SMILES #MolecularDesign #LargeLanguageModels

[Kaize Ding @kaize0409]

The research fellow will be working on a collaborative project and jointly advised by me, Prof. Noelle Samia, and Prof. Bonnie Martin-Harris.

[Kaize Ding @kaize0409]

📣 We are seeking exceptional postdoctoral candidates on AI4Health at Northwestern University! Please share with anyone who might be interested in this exciting opportunity! #Postdoc #AI4Health #ML #AI #LLM #MedicalAI #Northwestern

[Yichuan Li @hai72724774]

📢 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.

🔗 https://t.co/TtVDLC6mE5

[Kaize Ding @kaize0409]

@liu_xiaorui Congrats!

[SIGKDD 2026 @kdd_news]

🚨 Call for Workshop Papers at #KDD2024 🚨

Submit your paper to the KDD’24 Workshop on Resource-Efficient Learning for Knowledge Discovery
📆 June 30

https://t.co/bhRQN0vwmx

[Siddharth Bhatia @siddharthb_]

***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:
https://t.co/mz64pL6xW9

#internship #hiring

[Kaize Ding @kaize0409]

@yuz9yuz @TAMU Big congrats!

[WSDM Conference @WSDMSocial]

We heard that you missed the benefits of the standard registration.
Don't worry, the deadline has been extended to February 18th.

Register today:
https://t.co/PX75CIVcvW

#WSDM2024 #WSDMCUP2024 #sigkdd #sigmod #sigir #sigweb #acm

[Kaize Ding @kaize0409]

📢 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: https://t.co/m0gbMQBCG4

If you are also attending NeurIPS, feel free to talk to me!

[WSDM Conference @WSDMSocial]

#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 https://t.co/PX75CIVcvW

[Kaize Ding @kaize0409]

@SharonYixuanLi Big Congratulations!

[Yichuan Li @hai72724774]

🎉 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.

[data insight miner @yanjiefoo]

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.

[WSDM Conference @WSDMSocial]

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

https://t.co/m4o3NhPfh2

Proposals Due: October 5, 2023
Acceptance Notifications: November 2, 2023

[Kaize Ding @kaize0409]

Happy to chat about research if you are around! Also, I'm recruiting students to join my group at Northwestern University. Let me know if you are interested!

[Kaize Ding @kaize0409]

Heading to Long Beach for KDD'23! This time I will present our recent work "Learning Strong Graph Neural Networks with Weak Information". If you are interested in data-efficient graph learning, you are welcome to join the oral and poster sessions!

[Kaize Ding @kaize0409]

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