Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Rishal Aggarwal"'
Autor:
Andrew T. McNutt, Paul Francoeur, Rishal Aggarwal, Tomohide Masuda, Rocco Meli, Matthew Ragoza, Jocelyn Sunseri, David Ryan Koes
Publikováno v:
Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-20 (2021)
Abstract Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describ
Externí odkaz:
https://doaj.org/article/0acbd65d6e0e448cb2dda39d3167906b
Publikováno v:
ACS Omega. 8:2389-2397
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fb9e48b08506915a8b09c270ce2e8d5f
https://doi.org/10.1021/acsomega.2c06781
https://doi.org/10.1021/acsomega.2c06781
Publikováno v:
Journal of Chemical Information and Modeling. 62:2064-2076
Application of deep learning techniques for de novo generation of molecules, termed as inverse molecular design, has been gaining enormous traction in drug design. The representation of molecules in SMILES notation as a string of characters enables t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6be90333e6b1ee53f7a03d4565b671c
https://doi.org/10.1021/acs.jcim.1c00600
https://doi.org/10.1021/acs.jcim.1c00600
Autor:
Michael Brocidiacono, Paul Francoeur, Rishal Aggarwal, Konstantin Popov, David Koes, Alexander Tropsha
Recent attempts at utilizing deep learning for structure-based virtual screening have focused on training models to predict binding affinity from protein-ligand complexes with known crystal structures. The PDBbind dataset is the current standard for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b45355308f5271c679287bfca6a7d56
https://doi.org/10.26434/chemrxiv-2022-3qc9t
https://doi.org/10.26434/chemrxiv-2022-3qc9t
Publikováno v:
WIREs Computational Molecular Science. 13
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1900635986007eeb87fc14582855acf9
https://doi.org/10.1002/wcms.1637
https://doi.org/10.1002/wcms.1637
A structure-based drug design pipeline involves the development of potential drug molecules or ligands that form stable complexes with a given receptor at its binding site. A prerequisite to this is finding druggable and functionally relevant binding
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae706ed1ff1d5b684a5eb063cf936dda
https://doi.org/10.26434/chemrxiv.14611146
https://doi.org/10.26434/chemrxiv.14611146
Application of deep learning techniques for the de novo generation of molecules, termed as inverse molecular design, has been gaining enormous traction in drug design. The representation of molecules in SMILES notation as a string of characters enabl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::684da1967e88bcc29006b7c9e9245da1
https://doi.org/10.26434/chemrxiv.14561901
https://doi.org/10.26434/chemrxiv.14561901
Autor:
Paul G. Francoeur, Matthew Ragoza, Tomohide Masuda, Jocelyn Sunseri, Rocco Meli, Rishal Aggarwal, David Ryan Koes, Andrew T. McNutt
Publikováno v:
Journal of Cheminformatics
Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-20 (2021)
Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-20 (2021)
Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and eva
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::80508e2e3e124bd3d0bf02c4af1b5366
https://doi.org/10.26434/chemrxiv.13578140.v1
https://doi.org/10.26434/chemrxiv.13578140.v1
Autor:
Rishal Aggarwal, David Ryan Koes
Docking algorithms are an essential part of the Structure Based Drug Design (SBDD) process as they aim to effectively identify the binding poses of chemical structures at the target site. These algorithms are reliant on scoring functions that evaluat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f64d50ada7b35a75b848776ab6027915
https://doi.org/10.26434/chemrxiv.11910870
https://doi.org/10.26434/chemrxiv.11910870