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Found 5 papers, 2 papers with code
MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space
Generative models for structure-based drug design (SBDD) have shown promising results in recent years.
Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks
Advanced generative model (e. g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry.
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.
Knowledge Guided Geometric Editing for Unsupervised Drug Design
Deep learning models have been widely used in automatic drug design.
TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
Motivation Identifying compound–protein interaction (CPI) is a crucial task in drug discovery and chemogenomics studies, and proteins without three-dimensional structure account for a large part of potential biological targets, which requires developing methods using only protein sequence information to predict CPI.
Ranked #2 on
Drug Discovery
on LIT-PCBA(ESR1_ant)
