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Found 11 papers, 6 papers with code
DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization
DecompOpt presents a new generation paradigm which combines optimization with conditional diffusion models to achieve desired properties while adhering to the molecular grammar.
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
Designing 3D ligands within a target binding site is a fundamental task in drug discovery.
Structure-Based Drug Design via 3D Molecular Generative Pre-training and Sampling
We develop a novel 3D graph editing model to generate molecules using fragments, and pre-train this model on abundant 3D ligands for learning target-independent properties.
Binding-Adaptive Diffusion Models for Structure-Based Drug Design
Then the selected protein-ligand subcomplex is processed with SE(3)-equivariant neural networks, and transmitted back to each atom of the complex for augmenting the target-aware 3D molecule diffusion generation with binding interaction information.
Context-Aware Alignment and Mutual Masking for 3D-Language Pre-Training
The current approaches for 3D visual reasoning are task-specific, and lack pre-training methods to learn generic representations that can transfer across various tasks.
Geometry and Uncertainty-Aware 3D Point Cloud Class-Incremental Semantic Segmentation
Despite the significant recent progress made on 3D point cloud semantic segmentation, the current methods require training data for all classes at once, and are not suitable for real-life scenarios where new categories are being continuously discovered.
Zero-Shot Point Cloud Segmentation by Semantic-Visual Aware Synthesis
Given only the class-level semantic information for unseen objects, we strive to enhance the correspondence, alignment and consistency between the visual and semantic spaces, to synthesise diverse, generic and transferable visual features.
Knowledge Guided Geometric Editing for Unsupervised Drug Design
Deep learning models have been widely used in automatic drug design.
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
Searching for novel molecules with desired chemical properties is crucial in drug discovery.
A New Local Transformation Module for Few-shot Segmentation
The result by the matrix transformation can be regarded as an attention map with high-level semantic cues, based on which a transformation module can be built simply. The proposed transformation module is a general module that can be used to replace the transformation module in the existing few-shot segmentation frameworks.
Ranked #79 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
A New Few-shot Segmentation Network Based on Class Representation
This paper studies few-shot segmentation, which is a task of predicting foreground mask of unseen classes by a few of annotations only, aided by a set of rich annotations already existed.
