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Found 20 papers, 10 papers with code
An Energy-Adaptive Elastic Equivariant Transformer Framework for Protein Structure Representation
Structure-informed protein representation learning is essential for effective protein function annotation and \textit{de novo} design.
RiboFlow: Conditional De Novo RNA Sequence-Structure Co-Design via Synergistic Flow Matching
Ribonucleic acid (RNA) binds to molecules to achieve specific biological functions.
Multi-modal Contrastive Learning with Negative Sampling Calibration for Phenotypic Drug Discovery
Phenotypic drug discovery presents a promising strategy for identifying first-in-class drugs by bypassing the need for specific drug targets.
Reaction-conditioned De Novo Enzyme Design with GENzyme
Here, we introduce \textsc{GENzyme}, a \textit{de novo} enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex.
Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimization
Our method leverages a set of structural homologous motifs that align with query structural constraints to guide the generative model in inversely optimizing antibodies according to desired design criteria.
EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics
Enzyme design is a critical area in biotechnology, with applications ranging from drug development to synthetic biology.
ReactZyme: A Benchmark for Enzyme-Reaction Prediction
Enzymes, with their specific catalyzed reactions, are necessary for all aspects of life, enabling diverse biological processes and adaptations.
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks
While such methods could improve GNN predictions, they usually don't perform well on explanations.
Effective Protein-Protein Interaction Exploration with PPIretrieval
Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, and immune defense.
Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators
Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules.
Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction
To address these problems, we propose a novel Communicative Subgraph representation learning for Multi-relational Inductive drug-Gene interactions prediction (CoSMIG), where the predictions of drug-gene relations are made through subgraph patterns, and thus are naturally inductive for unseen drugs/genes without retraining or utilizing external domain features.
Molecular Attributes Transfer from Non-Parallel Data
Optimizing chemical molecules for desired properties lies at the core of drug development.
Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis
Specifically, we simultaneously perform intra-/inter-modal contrastive learning and semi-contrastive learning (that is why we call it hybrid contrastive learning), with which the model can fully explore cross-modal interactions, preserve inter-class relationships and reduce the modality gap.
Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning
In this way, we find the model can quickly adapt to few-shot relationships using only a handful of known facts with inductive settings.
Learning Attributed Graph Representations with Communicative Message Passing Transformer
For this sake, we propose a Communicative Message Passing Transformer (CoMPT) neural network to improve the molecular graph representation by reinforcing message interactions between nodes and edges based on the Transformer architecture.
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction
Advances in machine learning have led to graph neural network-based methods for drug discovery, yielding promising results in molecular design, chemical synthesis planning, and molecular property prediction.
BioNavi-NP: Biosynthesis Navigator for Natural Products
Nature, a synthetic master, creates more than 300, 000 natural products (NPs) which are the major constituents of FDA-proved drugs owing to the vast chemical space of NPs.
Communicative Message Passing for Inductive Relation Reasoning
Relation prediction for knowledge graphs aims at predicting missing relationships between entities.
RetroXpert: Decompose Retrosynthesis Prediction like a Chemist
Retrosynthesis is the process of recursively decomposing target molecules into available building blocks.
Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks
Synthesis planning is the process of recursively decomposing target molecules into available precursors.
