1.2k citations · 1.3k across the 31 of their papers we have counts for
32 papers
PersonaFactor: Preference Factors with Dynamic Selection for LLM Personalization
Hang Su, Zequn Liu, Jianghan Chao +3
Although users may express different personalization needs across tasks, these task-specific behaviors can reflect shared underlying preference mechanisms. Existing LLM personaliza…
VeriGeo: Controllable Geometry Question Generation with Numerical and Analytical Verification
Xiaoxian Duan, Zequn Liu, Yingce Xia
Geometry problem generation is useful for AI-assisted education and multimodal mathematical reasoning, but reliable synthesis remains difficult because the problem statement, diagr…
ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment
Qiuyu Tian, Haojie Yin, Yingce Xia +5
AI research often requires decisions before future evidence exists: which bottleneck to attack, which direction to pursue, or where a project should be positioned. We introduce For…
SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models
Yiyang Gu, Junwei Yang, Junyu Luo +15
Large language models (LLMs) are increasingly applied to scientific research, yet existing evaluations often fail to reflect the fine-grained capabilities required in practice. Mos…
CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors
Hang Su, Zequn Liu, Chen Hu +3
While LLMs have demonstrated remarkable potential in Question Answering (QA), evaluating personalization remains a critical bottleneck. Existing paradigms predominantly rely on lex…
Deciphering Scientific Reasoning Steps from Outcome Data for Molecule Optimization
Zequn Liu, Kehan Wu, Shufang Xie +5
Emerging reasoning models hold promise for automating scientific discovery. However, their training is hindered by a critical supervision gap: experimental outcomes are abundant, w…
MolChord: Structure-Sequence Alignment for Protein-Guided Drug Design
Wei Zhang, Zekun Guo, Yingce Xia +4
Structure-based drug design (SBDD), which maps target proteins to candidate molecular ligands, is a fundamental task in drug discovery. Effectively aligning protein structural repr…
Trust Region Preference Approximation: A simple and stable reinforcement learning algorithm for LLM reasoning
Xuerui Su, Shufang Xie, Guoqing Liu +7
Recently, Large Language Models (LLMs) have rapidly evolved, approaching Artificial General Intelligence (AGI) while benefiting from large-scale reinforcement learning to enhance H…
Nature Language Model: Deciphering the Language of Nature for Scientific Discovery
Yingce Xia, Peiran Jin, Shufang Xie +43
Foundation models have revolutionized natural language processing and artificial intelligence, significantly enhancing how machines comprehend and generate human languages. Inspire…
Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest Neighbors
Qizhi Pei, Lijun Wu, Zhenyu He +4
Drug-Target binding Affinity (DTA) prediction is essential for drug discovery. Despite the application of deep learning methods to DTA prediction, the achieved accuracy remain subo…
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…
BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
Qizhi Pei, Wei Zhang, Jinhua Zhu +5
Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery. However, current models exhibit several…
FABind: Fast and Accurate Protein-Ligand Binding
Qizhi Pei, Kaiyuan Gao, Lijun Wu +7
Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements i…
Retrosynthesis Prediction with Local Template Retrieval
Shufang Xie, Rui Yan, Junliang Guo +3
Retrosynthesis, which predicts the reactants of a given target molecule, is an essential task for drug discovery. In recent years, the machine learing based retrosynthesis methods…
MolXPT: Wrapping Molecules with Text for Generative Pre-training
Zequn Liu, Wei Zhang, Yingce Xia +5
Generative pre-trained Transformer (GPT) has demonstrates its great success in natural language processing and related techniques have been adapted into molecular modeling. Conside…
What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization
Griffin Adams, Bichlien H Nguyen, Jake Smith +7
Summarization models often generate text that is poorly calibrated to quality metrics because they are trained to maximize the likelihood of a single reference (MLE). To address th…
De Novo Molecular Generation via Connection-aware Motif Mining
Zijie Geng, Shufang Xie, Yingce Xia +6
De novo molecular generation is an essential task for science discovery. Recently, fragment-based deep generative models have attracted much research attention due to their flexibi…
Retrosynthetic Planning with Dual Value Networks
Guoqing Liu, Di Xue, Shufang Xie +7
Retrosynthesis, which aims to find a route to synthesize a target molecule from commercially available starting materials, is a critical task in drug discovery and materials design…
Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design
Kaiyuan Gao, Lijun Wu, Jinhua Zhu +8
Antibodies are versatile proteins that can bind to pathogens and provide effective protection for human body. Recently, deep learning-based computational antibody design has attrac…
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
Renqian Luo, Liai Sun, Yingce Xia +4
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main…
Tailoring Molecules for Protein Pockets: a Transformer-based Generative Solution for Structured-based Drug Design
Kehan Wu, Yingce Xia, Yang Fan +7
Structure-based drug design is drawing growing attentions in computer-aided drug discovery. Compared with the virtual screening approach where a pre-defined library of compounds ar…
Unified 2D and 3D Pre-Training of Molecular Representations
Jinhua Zhu, Yingce Xia, Lijun Wu +5
Molecular representation learning has attracted much attention recently. A molecule can be viewed as a 2D graph with nodes/atoms connected by edges/bonds, and can also be represent…
SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction
Qizhi Pei, Lijun Wu, Jinhua Zhu +6
Accurate prediction of Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with…
Direct Molecular Conformation Generation
Jinhua Zhu, Yingce Xia, Chang Liu +9
Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology. Previou…