23 citations · 30 across the 6 of their papers we have counts for
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q-bio.QM2024★ 2 cited
Accelerating Discovery of Novel and Bioactive Ligands With Pharmacophore-Informed Generative Models
Weixin Xie, Jianhang Zhang, Qin Xie +4
Deep generative models have gained significant advancements to accelerate drug discovery by generating bioactive chemicals against desired targets. Nevertheless, most generated com…
q-bio.QM2021★ 1 cited
Deep Learning Model of Dock by Dock Process Significantly Accelerate the Process of Docking-based Virtual Screening
Wei Ma, Qin Xie, Jianhang Zhang +4
Docking-based virtual screening (VS process) selects ligands with potential pharmacological activities from millions of molecules using computational docking methods, which greatly…
q-bio.QM2019★ 1 cited
Automatic Retrosynthetic Pathway Planning Using Template-free Models
Kangjie Lin, Youjun Xu, Jianfeng Pei +1
We present an attention-based Transformer model for automatic retrosynthesis route planning. Our approach starts from reactants prediction of single-step organic reactions for give…