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Found 14 papers, 5 papers with code
FineMolTex: Towards Fine-grained Molecular Graph-Text Pre-training
Specifically, FineMolTex consists of two pre-training tasks: a contrastive alignment task for coarse-grained matching and a masked multi-modal modeling task for fine-grained matching.
Less is More: on the Over-Globalizing Problem in Graph Transformers
Then we propose a novel Bi-Level Global Graph Transformer with Collaborative Training (CoBFormer), including the inter-cluster and intra-cluster Transformers, to prevent the over-globalizing problem while keeping the ability to extract valuable information from distant nodes.
Graph Fairness Learning under Distribution Shifts
Recently, there has been an increasing interest in ensuring fairness on GNNs, but all of them are under the assumption that the training and testing data are under the same distribution, i. e., training data and testing data are from the same graph.
DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction Prediction
In order to equip the model to generalize to conformations beyond the confines of crystal structures and to adapt to molecular docking and virtual screening tasks, we propose a multi-objective strategy, that is, the model outputs three scores for scoring and ranking, docking, and screening, and the training process optimizes these three objectives simultaneously.
A Generalized Neural Diffusion Framework on Graphs
In this paper, we propose a general diffusion equation framework with the fidelity term, which formally establishes the relationship between the diffusion process with more GNNs.
Towards Graph Foundation Models: A Survey and Beyond
Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and several other domains.
Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling
The ablation study also validates that the proposed coordinate sequence decoder and the visual-alignment loss are the keys to the success of our method.
Synthesis-driven design of 3D molecules for structure-based drug discovery using geometric transformers
Finding drug-like compounds with high bioactivity is essential for drug discovery, but the task is complicated by the high cost of chemical synthesis and validation.
Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network
Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various tasks, but we cannot accurately know the importance of different design dimensions of HGNNs due to diverse architectures and applied scenarios.
Automatic Description Construction for Math Expression via Topic Relation Graph
In the Selector, a Topic Relation Graph (TRG) is proposed to obtain the relevant documents which contain the comprehensive information of math expressions.
Learning to design drug-like molecules in three-dimensional space using deep generative models
The architecture of L-Net is specifically optimized for drug-like molecules, and a set of metrics is assembled to comprehensively evaluate its performance.
TF3P: Three-dimensional Force Fields Fingerprint Learned by Deep Capsular Network
Molecular fingerprints are the workhorse in ligand-based drug discovery.
DeepScaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning
Furthermore, the generated compounds were evaluated by molecular docking in DRD2 targets and the results demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold and de novo drug design of potential drug candidates with specific docking scores.
Multi-Objective De Novo Drug Design with Conditional Graph Generative Model
Recently, deep generative models have revealed itself as a promising way of performing de novo molecule design.
Ranked #4 on
Molecular Graph Generation
on InterBioScreen
