160 citations · 659 across the 33 of their papers we have counts for
33 papers
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…
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
Shengchao Liu, Jiongxiao Wang, Yijin Yang +4
Recent advancements in conversational large language models (LLMs), such as ChatGPT, have demonstrated remarkable promise in various domains, including drug discovery. However, exi…
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…
GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow
Fang Sun, Zhihao Zhan, Hongyu Guo +2
Designing molecules that bind to specific target proteins is a fundamental task in drug discovery. Recent models leverage geometric constraints to generate ligand molecules that bi…
Wheat Head Counting by Estimating a Density Map with Convolutional Neural Networks
Hongyu Guo
Wheat is one of the most significant crop species with an annual worldwide grain production of 700 million tonnes. Assessing the production of wheat spikes can help us measure the…
Effectiveness of Data Augmentation for Parameter Efficient Tuning with Limited Data
Stephen Obadinma, Hongyu Guo, Xiaodan Zhu
Recent work has demonstrated that using parameter efficient tuning techniques such as prefix tuning (or P-tuning) on pretrained language models can yield performance that is compar…
A Text-guided Protein Design Framework
Shengchao Liu, Yanjing Li, Zhuoxinran Li +10
Current AI-assisted protein design mainly utilizes protein sequential and structural information. Meanwhile, there exists tremendous knowledge curated by humans in the text format…
Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering
Tianxiao Li, Hongyu Guo, Filippo Grazioli +2
In protein biophysics, the separation between the functionally important residues (forming the active site or binding surface) and those that create the overall structure (the fold…
Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching
Shengchao Liu, Hongyu Guo, Jian Tang
Molecular representation pretraining is critical in various applications for drug and material discovery due to the limited number of labeled molecules, and most existing work focu…
SoftEdge: Regularizing Graph Classification with Random Soft Edges
Hongyu Guo, Sun Sun
Augmented graphs play a vital role in regularizing Graph Neural Networks (GNNs), which leverage information exchange along edges in graphs, in the form of message passing, for lear…
ifMixup: Interpolating Graph Pair to Regularize Graph Classification
Hongyu Guo, Yongyi Mao
We present a simple and yet effective interpolation-based regularization technique, aiming to improve the generalization of Graph Neural Networks (GNNs) on supervised graph classif…
Pre-training Molecular Graph Representation with 3D Geometry
Shengchao Liu, Hanchen Wang, Weiyang Liu +3
Molecular graph representation learning is a fundamental problem in modern drug and material discovery. Molecular graphs are typically modeled by their 2D topological structures, b…
Midpoint Regularization: from High Uncertainty Training to Conservative Classification
Hongyu Guo
Label Smoothing (LS) improves model generalization through penalizing models from generating overconfident output distributions. For each training sample the LS strategy smooths th…
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction
Hangrui Bi, Hengyi Wang, Chence Shi +3
Reliably predicting the products of chemical reactions presents a fundamental challenge in synthetic chemistry. Existing machine learning approaches typically produce a reaction pr…
Self-supervised Graph-level Representation Learning with Local and Global Structure
Minghao Xu, Hang Wang, Bingbing Ni +2
This paper studies unsupervised/self-supervised whole-graph representation learning, which is critical in many tasks such as molecule properties prediction in drug and material dis…
Regularization via Adaptive Pairwise Label Smoothing
Hongyu Guo
Label Smoothing (LS) is an effective regularizer to improve the generalization of state-of-the-art deep models. For each training sample the LS strategy smooths the one-hot encoded…
On SkipGram Word Embedding Models with Negative Sampling: Unified Framework and Impact of Noise Distributions
Dezhi Liu, Richong Zhang, Ziqiao Wang
SkipGram word embedding models with negative sampling, or SGN in short, is an elegant family of word embedding models. In this paper, we formulate a framework for word embedding, r…
A Graph to Graphs Framework for Retrosynthesis Prediction
Chence Shi, Minkai Xu, Hongyu Guo +2
A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction. Existing state-of-the-art methods…
Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers
Masoumeh Soflaei, Hongyu Guo, Ali Al-Bashabsheh +2
We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem a representation lea…
Weighted graphlets and deep neural networks for protein structure classification
Hongyu Guo, Khalique Newaz, Scott Emrich +2
As proteins with similar structures often have similar functions, analysis of protein structures can help predict protein functions and is thus important. We consider the problem o…
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework
Junfan Chen, Richong Zhang, Yongyi Mao +2
Distant supervision for relation extraction enables one to effectively acquire structured relations out of very large text corpora with less human efforts. Nevertheless, most of th…
MixUp as Directional Adversarial Training
Guillaume P. Archambault, Yongyi Mao, Hongyu Guo +1
In this work, we explain the working mechanism of MixUp in terms of adversarial training. We introduce a new class of adversarial training schemes, which we refer to as directional…
Augmenting Data with Mixup for Sentence Classification: An Empirical Study
Hongyu Guo, Yongyi Mao, Richong Zhang
Mixup, a recent proposed data augmentation method through linearly interpolating inputs and modeling targets of random samples, has demonstrated its capability of significantly imp…
MixUp as Locally Linear Out-Of-Manifold Regularization
Hongyu Guo, Yongyi Mao, Richong Zhang
MixUp is a recently proposed data-augmentation scheme, which linearly interpolates a random pair of training examples and correspondingly the one-hot representations of their label…