8 citations · 37 across the 14 of their papers we have counts for
15 papers
Revisiting Sampling Strategies for Molecular Generation
Yuyan Ni, Shikun Feng, Wei-Ying Ma +2
Sampling strategies in diffusion models are critical to molecular generation yet remain relatively underexplored. In this work, we investigate a broad spectrum of sampling methods…
Straight-Line Diffusion Model for Efficient 3D Molecular Generation
Yuyan Ni, Shikun Feng, Haohan Chi +5
Diffusion-based models have shown great promise in molecular generation but often require a large number of sampling steps to generate valid samples. In this paper, we introduce a…
UniGEM: A Unified Approach to Generation and Property Prediction for Molecules
Shikun Feng, Yuyan Ni, Yan Lu +3
Molecular generation and molecular property prediction are both crucial for drug discovery, but they are often developed independently. Inspired by recent studies, which demonstrat…
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction
Yuyan Ni, Shikun Feng, Xin Hong +5
Deep learning methods have been considered promising for accelerating molecular screening in drug discovery and material design. Due to the limited availability of labelled data, v…
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning
Shikun Feng, Yuyan Ni, Minghao Li +4
Recently, a noticeable trend has emerged in developing pre-trained foundation models in the domains of CV and NLP. However, for molecular pre-training, there lacks a universal mode…
Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion
Bowen Gao, Minsi Ren, Yuyan Ni +5
In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score. However, further study sh…
Elastic Information Bottleneck
Yuyan Ni, Yanyan Lan, Ao Liu +1
Information bottleneck is an information-theoretic principle of representation learning that aims to learn a maximally compressed representation that preserves as much information…
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method
Yuyan Ni, Shikun Feng, Wei-Ying Ma +2
While molecular pre-training has shown great potential in enhancing drug discovery, the lack of a solid physical interpretation in current methods raises concerns about whether the…
ProFSA: Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment
Bowen Gao, Yinjun Jia, Yuanle Mo +4
Pocket representations play a vital role in various biomedical applications, such as druggability estimation, ligand affinity prediction, and de novo drug design. While existing ge…
Fractional Denoising for 3D Molecular Pre-training
Shikun Feng, Yuyan Ni, Yanyan Lan +2
Coordinate denoising is a promising 3D molecular pre-training method, which has achieved remarkable performance in various downstream drug discovery tasks. Theoretically, the objec…
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Shengchao Liu, Weitao Du, Yanjing Li +10
Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific communities, particularly in the domains of…
Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization
Yimeng Chen, Tianyang Hu, Fengwei Zhou +2
The proliferation of pretrained models, as a result of advancements in pretraining techniques, has led to the emergence of a vast zoo of publicly available models. Effectively util…
A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
Shengchao Liu, Weitao Du, Zhiming Ma +2
Molecule pretraining has quickly become the go-to schema to boost the performance of AI-based drug discovery. Naturally, molecules can be represented as 2D topological graphs or 3D…
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction
Yuancheng Sun, Yimeng Chen, Weizhi Ma +5
Molecular property prediction is essential for drug discovery. In recent years, deep learning methods have been introduced to this area and achieved state-of-the-art performances.…
When Does Group Invariant Learning Survive Spurious Correlations?
Yimeng Chen, Ruibin Xiong, Zhiming Ma +1
By inferring latent groups in the training data, recent works introduce invariant learning to the case where environment annotations are unavailable. Typically, learning group inva…