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Found 37 papers, 21 papers with code
AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation
Virtual screening (VS) is a critical component of modern drug discovery, yet most existing methods--whether physics-based or deep learning-based--are developed around holo protein structures with known ligand-bound pockets.
Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule
Structure-Based Drug Design (SBDD) is crucial for identifying bioactive molecules.
PharmAgents: Building a Virtual Pharma with Large Language Model Agents
The discovery of novel small molecule drugs remains a critical scientific challenge with far-reaching implications for treating diseases and advancing human health.
DAPO: An Open-Source LLM Reinforcement Learning System at Scale
Inference scaling empowers LLMs with unprecedented reasoning ability, with reinforcement learning as the core technique to elicit complex reasoning.
Straight-Line Diffusion Model for Efficient 3D Molecular Generation
Diffusion-based models have shown great promise in molecular generation but often require a large number of sampling steps to generate valid samples.
Pushing the boundaries of Structure-Based Drug Design through Collaboration with Large Language Models
Structure-Based Drug Design (SBDD) has revolutionized drug discovery by enabling the rational design of molecules for specific protein targets.
MoMa: A Modular Deep Learning Framework for Material Property Prediction
Deep learning methods for material property prediction have been widely explored to advance materials discovery.
Steering Protein Family Design through Profile Bayesian Flow
Protein family design emerges as a promising alternative by combining the advantages of de novo protein design and mutation-based directed evolution. In this paper, we propose ProfileBFN, the Profile Bayesian Flow Networks, for specifically generative modeling of protein families.
A Periodic Bayesian Flow for Material Generation
To successfully realize the concept of periodic Bayesian flow, CrysBFN integrates a new entropy conditioning mechanism and empirically demonstrates its significance compared to time-conditioning.
ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer
The analysis of the trade-off between autoregressive modeling and diffusion demonstrates the potential of ACDiT to be used in long-horizon visual generation tasks.
Ranked #9 on
Video Generation
on UCF-101
Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks
Structure-Based molecule optimization (SBMO) aims to optimize molecules with both continuous coordinates and discrete types against protein targets.
UniGEM: A Unified Approach to Generation and Property Prediction for Molecules
Inspired by recent studies, which demonstrate that diffusion model, a prominent generative approach, can learn meaningful data representations that enhance predictive tasks, we explore the potential for developing a unified generative model in the molecular domain that effectively addresses both molecular generation and property prediction tasks.
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction
Deep learning methods have been considered promising for accelerating molecular screening in drug discovery and material design.
From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics
Recent advancements in structure-based drug design (SBDD) have significantly enhanced the efficiency and precision of drug discovery by generating molecules tailored to bind specific protein pockets.
MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis
We believe this dataset will serve as a more accurate and reliable benchmark for molecular representation learning, thereby expediting progress in the field of artificial intelligence-driven drug discovery.
SIU: A Million-Scale Structural Small Molecule-Protein Interaction Dataset for Unbiased Bioactivity Prediction
Small molecules play a pivotal role in modern medicine, and scrutinizing their interactions with protein targets is essential for the discovery and development of novel, life-saving therapeutics.
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning
Recently, a noticeable trend has emerged in developing pre-trained foundation models in the domains of CV and NLP.
MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space
Generative models for structure-based drug design (SBDD) have shown promising results in recent years.
Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks
Advanced generative model (e. g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry.
ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling
In this paper, we propose ESM-AA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular modeling.
Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion
In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score.
Contextual Molecule Representation Learning from Chemical Reaction Knowledge
REMO offers a novel solution to MRL by exploiting the underlying shared patterns in chemical reactions as \textit{context} for pre-training, which effectively infers meaningful representations of common chemistry knowledge.
Equivariant Flow Matching with Hybrid Probability Transport
The generation of 3D molecules requires simultaneously deciding the categorical features~(atom types) and continuous features~(atom coordinates).
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method
By aligning with physical principles, SliDe shows a 42\% improvement in the accuracy of estimated force fields compared to current state-of-the-art denoising methods, and thus outperforms traditional baselines on various molecular property prediction tasks.
Fractional Denoising for 3D Molecular Pre-training
Theoretically, the objective is equivalent to learning the force field, which is revealed helpful for downstream tasks.
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction
In our implementation, we adopt both the state-of-the-art molecule embedding models under the supervised learning paradigm and the pretraining paradigm as the molecule representation module of PEMP, respectively.
Neural Energy Minimization for Molecular Conformation Optimization
Assuming different forms of the underlying potential energy function, we can not only reinterpret and unify many of the existing models but also derive new variants of SE(3)-equivariant neural networks in a principled manner.
Controllable Person Image Synthesis with Attribute-Decomposed GAN
This paper introduces the Attribute-Decomposed GAN, a novel generative model for controllable person image synthesis, which can produce realistic person images with desired human attributes (e. g., pose, head, upper clothes and pants) provided in various source inputs.
Ranked #6 on
Pose Transfer
on Deep-Fashion
Effective Domain Knowledge Transfer with Soft Fine-tuning
However, the datasets from source domain are simply discarded in the fine-tuning process.
UniVSE: Robust Visual Semantic Embeddings via Structured Semantic Representations
We propose Unified Visual-Semantic Embeddings (UniVSE) for learning a joint space of visual and textual concepts.
Automatic Dataset Augmentation
Large scale image dataset and deep convolutional neural network (DCNN) are two primary driving forces for the rapid progress made in generic object recognition tasks in recent years.
Hierarchical Recurrent Attention Network for Response Generation
With the word level attention, hidden vectors of a word level encoder are synthesized as utterance vectors and fed to an utterance level encoder to construct hidden representations of the context.
Dual Learning for Machine Translation
Based on the feedback signals generated during this process (e. g., the language-model likelihood of the output of a model, and the reconstruction error of the original sentence after the primal and dual translations), we can iteratively update the two models until convergence (e. g., using the policy gradient methods).
Topic Aware Neural Response Generation
We consider incorporating topic information into the sequence-to-sequence framework to generate informative and interesting responses for chatbots.
LightLDA: Big Topic Models on Modest Compute Clusters
When building large-scale machine learning (ML) programs, such as big topic models or deep neural nets, one usually assumes such tasks can only be attempted with industrial-sized clusters with thousands of nodes, which are out of reach for most practitioners or academic researchers.
Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data
Image retrieval refers to finding relevant images from an image database for a query, which is considered difficult for the gap between low-level representation of images and high-level representation of queries.
