Odin Zhang[张昊天]

Structure-based Molecular Generation: A New Paradigm Enables Efficient Design of Potent CLIP1-LTK Inhibitors

Peichen Pan*, ShiCheng Chen*, Odin Zhang*, ..., Tingjun Hou

Cell, Submitted

We extend the Delete model to Casual-Delete model, scussfully design a potent inhibitors targeting to LTK

Delete: Deep Lead Optimization Enveloped in Protein Pocket through unified Deleting Strategies and a Structure-aware Network

Odin Zhang*, Huifeng Zhao*, Xujun Zhang*, ..., Chang-yu Hsieh, Tingjun Hou.

Nat. Commun. Second-round Peer Review

When you face some problems in drug discovery, just delete!

SurfGen: Learning on Topological Surface and Geometric Structure for 3D Molecular Generation

Odin Zhang*, Tianyue Wang*, Gaoqi Weng, ..., Chang-yu Hsieh, Tingjun Hou.

Nat. Compt. Sci, 3, 849–859 (2023)

Inspired from key-and-lock model, we build a 3D molecular design model directly on topological protein surface through Geodesic-GNN and Geoatton-GNN, which satisfy the physical equivariance.

ResGen: Learning the Interaction Patterns through Multiscale Modeling and SE(3)-Framework for 3D-Pocket Molecular Generation

Odin Zhang, Jintu Zhang, Xujun Zhang, ..., Chang-yu Hsieh, Tingjun Hou.

Nat. Mach. Intell., 5, 1020–1030 (2023)

We construct a novel 3D Pocket-aware molecular generation model via multi-scale modeling, achieving SOTA performance on CrossDock dataset and our curated real-world dataset.

Multi-Objective Structure-Based Molecule Generation with Pareto MCTS

Yaodong Yang*, Guangyong Chen, Jinpeng Li, Odin Zhang*, ..., Pheng-Ann Heng

AAAI

A MCTS-based method for molecular generation

Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration

Haitao Lin, Yufei Huang, Odin Zhang, ..., Stan Z. Li.

NIPS2023

We use diffusion model to grow fragments and link fragments for lead optimization

DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding

Haitao Lin*, Yufei Huang*, Liu Meng, Odin Zhang, Xuanjing Li, Shuiwang Ji, Tingjun Hou, Stan Z. Li

We propose a one-shot approach to capture global interatomic forces for growing molecules directly within protein pockets

A Flexible Data-Free Framework for Structure-Based De Novo Drug Design with Reinforcement Learning

Hongyan Du*, Dejun Jiang*, Odin Zhang, ..., TingjunHou.

Chemical Science

A MCTS-based approach enables de novo design according to the chemicts' requirement

FFLOM: a flow-based autoregressive model for fragment-to-lead optimization. Journal of Medicinal Chemistry

Jieyu Jin, Dong Wang, Odin Zhang, ..., Yu Kang, Tingjun Hou

Journal of Medicinal Chemistry 14(8), 2054-2069

We develop a flow-based model for fragment elaboration and linker design, achieving the SOTA performance regarding 2D metrics

KarmaDock: a deep learning paradigm for ultra-large library docking with fast speed and high accuracy

Xujun Zhang*, Odin Zhang*, ..., Chang-yu Hsieh, Tingjun Hou.

Nat. Compt. Sci.,3, 739–740 (2023).

A Deep Learning-based docking method, from binding pose to virtual screening

Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoop

Tianyue Wang*, Xujun Zhang*, Odin Zhang*, ..., Chang-yu Hsieh, Tingjun Hou.

Nat. Mach. Intell., Major Revision

A SOTA method for the geometry prediction of the loop domain in proteins

SDEGen: Learning to Evolve Molecular Conformations from Thermodynamic Noise for Conformation Generation

Odin Zhang*, ShengMing Li*, Jintu Zhang, .., Yu Kang, Tingjun Hou.

Chem. Sci., 14, 1557-1568

We construct a novel conformation generation model via the stochastic differential equation-based generative model, which is a kind of diffusion process.

Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction

Yufei Huang*, ..., Odin Zhang, ..., Stan. Z. Li.

AAAI 2024

In this paper, we first investigate the rea- son behind the decrease in prediction accuracy when utilizing predicted structures, attributing it to the structural distribution bias from the perspective of structure representation learning

Infinite Physical Monkey: Do Deep Learning Methods Really Perform Better in Conformation Generation?

Odin Zhang, Jintu Zhang, Huifeng Zhao, Dejun Jiang, Yafeng Deng.

Bio Arxiv

We present our opinions on conformation generation, discussing another two papers on arxiv platform

MetalProGNet: A Structure-based Deep Graph Model for Metalloprotein–ligand Interaction Predictions

Dejun Jiang, Zhaofeng Ye, Chang-yu Hsieh, Odin Zhang, ..., Jian Wu, Tingjun Hou

Chem. Sci. 14(8), 2054-2069

We develop a novel and accurate scoring function for metalloproteins, the main methodology is bi-partite graph and GNN

Explainable Fragment-Based Molecular Property Attribution

Linxiang Jia, Zunlei Feng, Odin Zhang, Jie Song, Zipeng Zhong, Shaolun Yao, Mingli Song.

Adv. Intell. Syst. 4(10), 2200104

I led the experimental validation of the model throught collection of pharmacological data, this is my early-stage participation in undergraduate period

Quasiclassical Trajectory Simulation as a Protocol to Build Locally Accurate Machine Learning Potentials

Jintu Zhang*, Odin Zhang*, Zhixin Qin, Yu Kang, Xin Hong, Tingjun Hou.

J. Chem. Inf. Model. 2023, 63, 4, 1133–1142

We show that quasiclassical trajectory (QCT) calculations can be invoked to efficiently obtain locally accurate ML-PESs, thus accelearing molecular dynamics

An Engineered Design of Self-Assembly Nanomedicine Guided by Molecular Dynamic Simulation for Photodynamic and Hypoxia-Directed Therapy

Yu Wang, Odin Zhang, Jianwei Wang, Guping Tang, Hongzhen Bai.

Mol. Pharmaceutics, 2023, 20, 4, 2128–2137

I conduct the MD and DPD to guide the design of the nanomedicine ISDNN, which achieves uniform size distribution and high drug loading up to 90%

Sigmoid Accelerated Molecular Dynamics: An Efficient Enhanced Sampling Method for Biosystems

Yihao Zhao, Jintu Zhang, Odin Zhang, Shukai Gu, Yafeng Deng, Yaoquan Tu, Tingjun Hou, Yu Kang.

J. Phys. Chem. Lett. 2023, 14, 4, 1103–1112

We propose sigmoid accelerated molecular dynamics (SaMD), which can produce the free energy landscape with better accuracy and efficiency compared with GaMD.

Amphiphilic porphyrin-based supramolecular self-assembly for photochemotherapy: From molecular design to application

Shuping Wang, Xuechun Huang, Yunhong He, Odin Zhang*, Jun Zhou, Guping Tang, Shijun Li, Hongzhen Bai.

Nano Today, 48, 101732

I conduct the Disspative Particle Dynamics (DPD) to study the stability of CPT@P complex, this is my early-stage work in undergraduate period

How Good Are Current Docking Programs at Nucleic acids-ligand Docking? a Comprehensive Evaluation

Dejun Jiang, Huifeng Zhao, ...,Odin Zhang, Tingjun Hou

Journal of Chemical Theory and Computation 14(8), 2054-2069

We conducted a comprehensive evaluation of molecular docking programs for NA–ligand systems, contributing to the future research about virtual screening on such a system