31 citations · 96 across the 6 of their papers we have counts for
6 papers
Pretraining a Foundation Model for Small-Molecule Natural Products
Yuheng Ding, Bo Qiang, Shaoning Li +8
Natural products, as metabolites from microorganisms, animals, or plants, exhibit diverse biological activities, making them crucial for drug discovery. Nowadays, existing deep lea…
Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation
Ningfeng Liu, Jie Yu, Siyu Xiu +7
Molecular generation, an essential method for identifying new drug structures, has been supported by advancements in machine learning and computational technology. However, challen…
Bridging the Gap between Chemical Reaction Pretraining and Conditional Molecule Generation with a Unified Model
Bo Qiang, Yiran Zhou, Yuheng Ding +5
Chemical reactions are the fundamental building blocks of drug design and organic chemistry research. In recent years, there has been a growing need for a large-scale deep-learning…
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…