100 citations · 176 across the 15 of their papers we have counts for
15 papers
Generating Developable 3D Molecules via Pocket-Conditioned Diffusion and Property-Aware Optimization
Ruoxi Gao, Jiangweizhi Peng, Ziqi Chen +10
Drug discovery and development is time-consuming and resource-intensive, motivating computational approaches such as diffusion models for de novo drug design. Many such models foll…
PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents
Minghao Yan, Bo Peng, Benjamin Coleman +11
Large language models have become drivers of evolutionary search, but most systems rely on a fixed, prompt-elicited policy to sample next candidates. This limits adaptation in prac…
DiffER: Categorical Diffusion for Chemical Retrosynthesis
Sean Current, Ziqi Chen, Daniel Adu-Ampratwum +2
Methods for automatic chemical retrosynthesis have found recent success through the application of models traditionally built for natural language processing, primarily through tra…
Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models with Guidance
Ziqi Chen, Bo Peng, Tianhua Zhai +2
Drug development is a critical but notoriously resource- and time-consuming process. In this manuscript, we develop a novel generative artificial intelligence (genAI) method DiffSM…
log-RRIM: Yield Prediction via Local-to-global Reaction Representation Learning and Interaction Modeling
Xiao Hu, Ziqi Chen, Bo Peng +2
Accurate prediction of chemical reaction yields is crucial for optimizing organic synthesis, potentially reducing time and resources spent on experimentation. With the rise of arti…
LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset
Botao Yu, Frazier N. Baker, Ziqi Chen +2
Chemistry plays a crucial role in many domains, such as drug discovery and material science. While large language models (LLMs) such as GPT-4 exhibit remarkable capabilities on nat…
Modeling Sequences as Star Graphs to Address Over-smoothing in Self-attentive Sequential Recommendation
Bo Peng, Ziqi Chen, Srinivasan Parthasarathy +1
Self-attention (SA) mechanisms have been widely used in developing sequential recommendation (SR) methods, and demonstrated state-of-the-art performance. However, in this paper, we…
Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation
Bo Peng, Ben Burns, Ziqi Chen +2
In recent years, with large language models (LLMs) achieving state-of-the-art performance in context understanding, increasing efforts have been dedicated to developing LLM-enhance…
RLSynC: Offline-Online Reinforcement Learning for Synthon Completion
Frazier N. Baker, Ziqi Chen, Daniel Adu-Ampratwum +1
Retrosynthesis is the process of determining the set of reactant molecules that can react to form a desired product. Semi-template-based retrosynthesis methods, which imitate the r…
Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models
Ziqi Chen, Bo Peng, Srinivasan Parthasarathy +1
Ligand-based drug design aims to identify novel drug candidates of similar shapes with known active molecules. In this paper, we formulated an in silico shape-conditioned molecule…
T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy
Ziqi Chen, Martin Renqiang Min, Hongyu Guo +3
T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of…
as a Two-Step Graph Generative Models for Retrosynthesis Prediction
Ziqi Chen, Oluwatosin R. Ayinde, James R. Fuchs +2
Retrosynthesis is a procedure where a target molecule is transformed into potential reactants and thus the synthesis routes can be identified. Recently, computational approaches ha…
Ranking-based Convolutional Neural Network Models for Peptide-MHC Binding Prediction
Ziqi Chen, Martin Renqiang Min, Xia Ning
T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identify…
A Deep Generative Model for Molecule Optimization via One Fragment Modification
Ziqi Chen, Martin Renqiang Min, Srinivasan Parthasarathy +1
Molecule optimization is a critical step in drug development to improve desired properties of drug candidates through chemical modification. We developed a novel deep generative mo…
Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations
Yonghyun Nam, Jae-Seung Yun, Seung Mi Lee +7
Currently, the number of patients with COVID-19 has significantly increased. Thus, there is an urgent need for developing treatments for COVID-19. Drug repurposing, which is the pr…