184 citations · 261 across the 6 of their papers we have counts for
6 papers
DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction Prediction
Haoyu Lin, Shiwei Wang, Jintao Zhu +3
Protein (receptor)--ligand interaction prediction is a critical component in computer-aided drug design, significantly influencing molecular docking and virtual screening processes…
Synthesis-driven design of 3D molecules for structure-based drug discovery using geometric transformers
Yibo Li, Jianfeng Pei, Luhua Lai
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. With their out…
Learning to design drug-like molecules in three-dimensional space using deep generative models
Yibo Li, Jianfeng Pei, Luhua Lai
Recently, deep generative models for molecular graphs are gaining more and more attention in the field of de novo drug design. A variety of models have been developed to generate t…
TF3P: Three-dimensional Force Fields Fingerprint Learned by Deep Capsular Network
Yanxing Wang, Jianxing Hu, Junyong Lai +5
Molecular fingerprints are the workhorse in ligand-based drug discovery. In recent years, an increasing number of research papers reported fascinating results on using deep neural…
DeepScaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning
Yibo Li, Jianxing Hu, Yanxing Wang +3
The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as the core structures of…
Multi-Objective De Novo Drug Design with Conditional Graph Generative Model
Yibo Li, Liangren Zhang, Zhenming Liu
Recently, deep generative models have revealed itself as a promising way of performing de novo molecule design. However, previous research has focused mainly on generating SMILES s…