Search Results for author:
Found 37 papers, 18 papers with code
React-OT: Optimal Transport for Generating Transition State in Chemical Reactions
The RMSD and barrier height error is further improved by roughly 25% through pretraining React-OT on a large reaction dataset obtained with a lower level of theory, GFN2-xTB.
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
To constrain the optimization process to the data manifold, we reformulate the original optimization problem as a sampling problem from the product of the Boltzmann distribution defined by the objective function and the data distribution learned by the diffusion model.
Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks
Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design.
On Separate Normalization in Self-supervised Transformers
Self-supervised training methods for transformers have demonstrated remarkable performance across various domains.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
MUBen: Benchmarking the Uncertainty of Molecular Representation Models
While some studies have included UQ to improve molecular pre-trained models, the process of selecting suitable backbone and UQ methods for reliable molecular uncertainty estimation remains underexplored.
Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model
Provided reactant and product, this model generates a TS structure in seconds instead of hours required when performing quantum chemistry-based optimizations.
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects.
Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction
Modern machine learning techniques have been extensively applied to materials science, especially for property prediction tasks.
GAUCHE: A Library for Gaussian Processes in Chemistry
By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry.
Audio-Driven Co-Speech Gesture Video Generation
Our key insight is that the co-speech gestures can be decomposed into common motion patterns and subtle rhythmic dynamics.
A Systematic Survey of Chemical Pre-trained Models
Deep learning has achieved remarkable success in learning representations for molecules, which is crucial for various biochemical applications, ranging from property prediction to drug design.
Structure-based Drug Design with Equivariant Diffusion Models
Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets.
Multi-objective Deep Data Generation with Correlated Property Control
Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design.
Improving Molecular Pretraining with Complementary Featurizations
Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry and drug discovery.
Controllable Data Generation by Deep Learning: A Review
This article is a systematic review that explains this promising research area, commonly known as controllable deep data generation.
Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths
We consider the problem of sampling transition paths between two given metastable states of a molecular system, e. g. a folded and unfolded protein or products and reactants of a chemical reaction.
A Flexible Diffusion Model
Despite the empirical success of the hand-crafted fixed forward SDEs, a great quantity of proper forward SDEs remain unexplored.
Pik-Fix: Restoring and Colorizing Old Photos
Our proposed framework consists of three modules: a restoration sub-network that conducts restoration from degradations, a similarity network that performs color histogram matching and color transfer, and a colorization subnet that learns to predict the chroma elements of images conditioned on chromatic reference signals.
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design
Recently, with the rapid development of machine learning methods, especially generative methods, molecule design has achieved great progress by leveraging machine learning models to generate candidate molecules.
A Survey on Deep Graph Generation: Methods and Applications
In this paper, we conduct a comprehensive review on the existing literature of deep graph generation from a variety of emerging methods to its wide application areas.
Recovering medical images from CT film photos
While medical images such as computed tomography (CT) are stored in DICOM format in hospital PACS, it is still quite routine in many countries to print a film as a transferable medium for the purposes of self-storage and secondary consultation.
Disentangled Spatiotemporal Graph Generative Models
Spatiotemporal graph represents a crucial data structure where the nodes and edges are embedded in a geometric space and can evolve dynamically over time.
Interpretable Molecular Graph Generation via Monotonic Constraints
Recent advances in deep graph generative models treat molecule design as graph generation problems which provide new opportunities toward the breakthrough of this long-lasting problem.
A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications
Pretrained Language Models (PLMs) such as BERT have revolutionized the landscape of Natural Language Processing (NLP).
ROMNet: Renovate the Old Memories
Renovating the memories in old photos is an intriguing research topic in computer vision fields.
Graph-based Ensemble Machine Learning for Student Performance Prediction
Student performance prediction is a critical research problem to understand the students' needs, present proper learning opportunities/resources, and develop the teaching quality.
SE(3) Equivariant Graph Neural Networks with Complete Local Frames
In this paper, we propose a framework to construct SE(3) equivariant graph neural networks that can approximate the geometric quantities efficiently.
Interpreting Molecule Generative Models for Interactive Molecule Discovery
In this work, we develop a simple yet effective method to interpret the latent space of the learned generative models with various molecular properties for more interactive molecule generation and discovery.
Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning
Integrating physical inductive biases into machine learning can improve model generalizability.
GraphGT: Machine Learning Datasets for Graph Generation and Transformation
Graph generation, which learns from known graphs and discovers novel graphs, has great potential in numerous research topics like drug design and mobility synthesis and is one of the fastest-growing domains recently due to its promise for discovering new knowledge.
Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image
Pseudo-normality synthesis, which computationally generates a pseudo-normal image from an abnormal one (e. g., with lesions), is critical in many perspectives, from lesion detection, data augmentation to clinical surgery suggestion.
A Survey on Graph Structure Learning: Progress and Opportunities
Specifically, we first formulate a general pipeline of GSL and review state-of-the-art methods classified by the way of modeling graph structures, followed by applications of GSL across domains.
Property Controllable Variational Autoencoder via Invertible Mutual Dependence
Deep generative models have made important progress towards modeling complex, high dimensional data via learning latent representations.
CT Film Recovery via Disentangling Geometric Deformation and Illumination Variation: Simulated Datasets and Deep Models
We also record all accompanying information related to the geometric deformation (such as 3D coordinate, depth, normal, and UV maps) and illumination variation (such as albedo map).
Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models
Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored.
Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder
Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function.
