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Found 23 papers, 12 papers with code
Leveraging Induced Transferable Binding Principles for Associative Prediction of Novel Drug-Target Interactions
Significant differences in protein structures hinder the generalization of existing drug-target interaction (DTI) models, which often rely heavily on pre-learned binding principles or detailed annotations.
Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language Models
In the first step, we use textual descriptions, SMILES, and biochemical properties as multimodal inputs to pre-train a model called PEIT-GEN, by aligning multi-modal representations to synthesize instruction data.
Ranked #13 on
Molecule Captioning
on ChEBI-20
S$^2$DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completion
These results demonstrate the effectiveness of S$^2$DN in preserving semantic consistency and enhancing the robustness of filtering out unreliable interactions in contaminated KGs.
Balancing property optimization and constraint satisfaction for constrained multi-property molecular optimization
Molecular optimization, which aims to discover improved molecules from a vast chemical search space, is a critical step in chemical development.
Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development
Besides, Y-Mol offers a set of LLM paradigms that can autonomously execute the downstream tasks across the entire process of drug development, including virtual screening, drug design, pharmacological properties prediction, and drug-related interaction prediction.
MaskMol: Knowledge-guided Molecular Image Pre-Training Framework for Activity Cliffs
Activity cliffs, which refer to pairs of molecules that are structurally similar but show significant differences in their potency, can lead to model representation collapse and make the model challenging to distinguish them.
MoFormer: Multi-objective Antimicrobial Peptide Generation Based on Conditional Transformer Joint Multi-modal Fusion Descriptor
Deep learning holds a big promise for optimizing existing peptides with more desirable properties, a critical step towards accelerating new drug discovery.
HMAMP: Hypervolume-Driven Multi-Objective Antimicrobial Peptides Design
This paper introduces a paradigm shift by considering multiple attributes in AMP design.
KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion
Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web.
Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model
While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge.
DrugAssist: A Large Language Model for Molecule Optimization
Recently, the impressive performance of large language models (LLMs) on a wide range of tasks has attracted an increasing number of attempts to apply LLMs in drug discovery.
Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction
To address this limitation, we propose BioKDN (Biomedical Knowledge Graph Denoising Network) for robust molecular interaction prediction.
DiffColor: Toward High Fidelity Text-Guided Image Colorization with Diffusion Models
To address these issues, we propose a new method called DiffColor that leverages the power of pre-trained diffusion models to recover vivid colors conditioned on a prompt text, without any additional inputs.
Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs).
LRBmat: A Novel Gut Microbial Interaction and Individual Heterogeneity Inference Method for Colorectal Cancer
Many methods adopt gut microbiota to solve it, but few of them simultaneously take into account the complex interactions and individual heterogeneity of gut microbiota, which are two common and important issues in genetics and intestinal microbiology, especially in high-dimensional cases.
Molecule optimization via multi-objective evolutionary in implicit chemical space
However, efficient search for optimized molecules satisfying several properties with scarce labeled data remains a challenge for machine learning molecule optimization.
Multi-View Substructure Learning for Drug-Drug Interaction Prediction
Drug-drug interaction (DDI) prediction provides a drug combination strategy for systemically effective treatment.
Deep learning for drug repurposing: methods, databases, and applications
Drug development is time-consuming and expensive.
Fast k-NN Graph Construction by GPU based NN-Descent
In this paper, NN-Descent has been redesigned to adapt to the GPU architecture.
Heterogeneous network-based drug repurposing for COVID-19
The Corona Virus Disease 2019 (COVID-19) belongs to human coronaviruses (HCoVs), which spreads rapidly around the world.
Large-Scale Approximate k-NN Graph Construction on GPU
NN-Descent was proposed as an effective solution for the graph construction problem.
Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning
While this study, by no means recommends specific drugs, it demonstrates a powerful deep learning methodology to prioritize existing drugs for further investigation, which holds the potential of accelerating therapeutic development for COVID-19.
Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy
TATA-binding protein (TBP) is a kind of DNA binding protein, which plays a key role in the transcription regulation.
