Search Results for author: Weitao Du

Found 16 papers, 8 papers with code

Sculpting Molecules in 3D: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization

no code implementations6 Mar 2024 Kaiwei Zhang, Yange Lin, Guangcheng Wu, Yuxiang Ren, Xuecang Zhang, Bo wang, XiaoYu Zhang, Weitao Du

This work not only holds general significance for the advancement of deep learning methodologies but also paves the way for a transformative shift in molecular design strategies.

A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics

no code implementations26 Jan 2024 Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Vignesh Bhethanabotla, Nakul Rampal, Omar Yaghi, Christian Borgs, Anima Anandkumar, Hongyu Guo, Jennifer Chayes

We show the efficiency and effectiveness of NeuralMD, with a 2000$\times$ speedup over standard numerical MD simulation and outperforming all other ML approaches by up to 80% under the stability metric.

Drug Discovery

A quatum inspired neural network for geometric modeling

no code implementations3 Jan 2024 Weitao Du, Shengchao Liu, Xuecang Zhang

By conceiving physical systems as 3D many-body point clouds, geometric graph neural networks (GNNs), such as SE(3)/E(3) equivalent GNNs, have showcased promising performance.

Tensor Networks

Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion

no code implementations NeurIPS 2023 Weitao Du, Jiujiu Chen, Xuecang Zhang, ZhiMing Ma, Shengchao Liu

The fundamental building block for drug discovery is molecule geometry and thus, the molecule's geometrical representation is the main bottleneck to better utilize machine learning techniques for drug discovery.

Drug Discovery

Power-law Dynamic arising from machine learning

no code implementations16 Jun 2023 Wei Chen, Weitao Du, Zhi-Ming Ma, Qi Meng

We study a kind of new SDE that was arisen from the research on optimization in machine learning, we call it power-law dynamic because its stationary distribution cannot have sub-Gaussian tail and obeys power-law.

Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials

1 code implementation NeurIPS 2023 Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Zhiling Zheng, Chenru Duan, ZhiMing Ma, Omar Yaghi, Anima Anandkumar, Christian Borgs, Jennifer Chayes, Hongyu Guo, Jian Tang

Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific communities, particularly in the domains of chemistry, biology, and material discovery.


A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining

1 code implementation28 May 2023 Shengchao Liu, Weitao Du, ZhiMing Ma, Hongyu Guo, Jian Tang

Meanwhile, existing molecule multi-modal pretraining approaches approximate MI based on the representation space encoded from the topology and geometry, thus resulting in the loss of critical structural information of molecules.

Drug Discovery

Structure-based Drug Design with Equivariant Diffusion Models

2 code implementations24 Oct 2022 Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael Bronstein, Bruno Correia

Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets.


A Flexible Diffusion Model

no code implementations17 Jun 2022 Weitao Du, Tao Yang, He Zhang, Yuanqi Du

Despite the empirical success of the hand-crafted fixed forward SDEs, a great quantity of proper forward SDEs remain unexplored.

CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations

1 code implementation5 Nov 2021 Tianyu Zhang, Yuxiang Ren, Wenzheng Feng, Weitao Du, Xuecang Zhang

In this paper, we show the potential hazards of inappropriate augmentations and then propose a novel Collaborative Graph Contrastive Learning framework (CGCL).

Contrastive Learning Data Augmentation +2

SE(3) Equivariant Graph Neural Networks with Complete Local Frames

1 code implementation26 Oct 2021 Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Bin Shao, Tie-Yan Liu

In this paper, we propose a framework to construct SE(3) equivariant graph neural networks that can approximate the geometric quantities efficiently.

Computational Efficiency

Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective

no code implementations ICLR 2022 Wei Huang, Yayong Li, Weitao Du, Jie Yin, Richard Yi Da Xu, Ling Chen, Miao Zhang

Inspired by our theoretical insights on trainability, we propose Critical DropEdge, a connectivity-aware and graph-adaptive sampling method, to alleviate the exponential decay problem more fundamentally.

Implicit bias of deep linear networks in the large learning rate phase

no code implementations25 Nov 2020 Wei Huang, Weitao Du, Richard Yi Da Xu, Chunrui Liu

We claim that depending on the separation conditions of data, the gradient descent iterates will converge to a flatter minimum in the catapult phase.

Binary Classification

On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization

2 code implementations13 Apr 2020 Wei Huang, Weitao Du, Richard Yi Da Xu

The prevailing thinking is that orthogonal weights are crucial to enforcing dynamical isometry and speeding up training.

Mean field theory for deep dropout networks: digging up gradient backpropagation deeply

1 code implementation19 Dec 2019 Wei Huang, Richard Yi Da Xu, Weitao Du, Yutian Zeng, Yunce Zhao

In recent years, the mean field theory has been applied to the study of neural networks and has achieved a great deal of success.

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