"Streamlining Large Chemical Library Docking with Artificial Intelligence: ..."

Michele Roggia et al. (2024)

> Home

Details and statistics

DOI: 10.1021/ACS.JCIM.3C00647

access: closed

type: Journal Article

metadata version: 2024-05-17

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - journals/jcisd/RoggiaNAMC24
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/journals/jcisd/RoggiaNAMC24
Michele Roggia, Benito Natale, Giorgio Amendola, Salvatore Di Maro, Sandro Cosconati:
Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach. J. Chem. Inf. Model. 64(7): 2143-2149 (2024)

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.