Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations

By Payel Das, Tom Sercu, Kahini Wadhawan, Inkit Padhi, Sebastian Gehrmann, F. Cipcigan, Vijil Chenthamarakshan, Hendrik Strobelt, Cicero Dos Santos, Pin-Yu Chen and 5 more. Nature Biomedical Engineering, 2021

398 citations recorded by the source corpus.

Subfields: Microbiology, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/65401a01f9e60ed8afbb5e3d6b62457a35caf358

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1. Min Wang
2. Yue Ma
3. Zhengyan Guo
4. Na Tang
5. Xiaolin Liu
6. Yihua Chen
7. Yuwei Zhang
8. Binbin Xia

Citations in the corpus

• MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design (2022, neutral): “…GEGL [ [151] 2D-Graph 2D-Graph VAE Hier-VAE [152] 2D-Graph 2D-Graph VAE NeVAE [153] 2D-Graph+3D-Geometry 2D-Graph+3D-Geometry VAE Cogmol [154], [155] Data-agnostic Data-agnostic VAE MSO [115] SMILES SMILES VAE+BO Constrained-BO-VAE [118] SMILES SMILES VAE+BO TI-MI/IS-MI [117] Data-agnostic…”
• Identification of antimicrobial peptides from the human gut microbiome using deep learning (2022, background): “5), agreeing with previous findings of alpha-helix, pore-forming peptides among the most potent AMPs 21,54,55 .”