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Found 16 papers, 8 papers with code
Sculpting Molecules in 3D: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization
This work not only holds general significance for the advancement of deep learning methodologies but also paves the way for a transformative shift in molecular design strategies.
A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics
We show the efficiency and effectiveness of NeuralMD, with a 2000$\times$ speedup over standard numerical MD simulation and outperforming all other ML approaches by up to 80% under the stability metric.
A quatum inspired neural network for geometric modeling
By conceiving physical systems as 3D many-body point clouds, geometric graph neural networks (GNNs), such as SE(3)/E(3) equivalent GNNs, have showcased promising performance.
Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion
The fundamental building block for drug discovery is molecule geometry and thus, the molecule's geometrical representation is the main bottleneck to better utilize machine learning techniques for drug discovery.
Power-law Dynamic arising from machine learning
We study a kind of new SDE that was arisen from the research on optimization in machine learning, we call it power-law dynamic because its stationary distribution cannot have sub-Gaussian tail and obeys power-law.
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific communities, particularly in the domains of chemistry, biology, and material discovery.
A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
Meanwhile, existing molecule multi-modal pretraining approaches approximate MI based on the representation space encoded from the topology and geometry, thus resulting in the loss of critical structural information of molecules.
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.
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.
A Flexible Diffusion Model
Despite the empirical success of the hand-crafted fixed forward SDEs, a great quantity of proper forward SDEs remain unexplored.
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations
In this paper, we show the potential hazards of inappropriate augmentations and then propose a novel Collaborative Graph Contrastive Learning framework (CGCL).
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.
Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective
Inspired by our theoretical insights on trainability, we propose Critical DropEdge, a connectivity-aware and graph-adaptive sampling method, to alleviate the exponential decay problem more fundamentally.
Implicit bias of deep linear networks in the large learning rate phase
We claim that depending on the separation conditions of data, the gradient descent iterates will converge to a flatter minimum in the catapult phase.
On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization
The prevailing thinking is that orthogonal weights are crucial to enforcing dynamical isometry and speeding up training.
Mean field theory for deep dropout networks: digging up gradient backpropagation deeply
In recent years, the mean field theory has been applied to the study of neural networks and has achieved a great deal of success.
