Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Pedro J Ballester

Showing results (41-50 of 60) with videos related to

Pageof 6
Sort By:
Journal of Cheminformatics|April 6, 2024
Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitorsKlaudia Caba, Viet-Khoa Tran-Nguyen, Taufiq Rahman, et al.
Nucleic Acids Research|April 24, 2016
USR-VS: a web server for large-scale prospective virtual screening using ultrafast shape recognition techniquesHongjian Li, Kwong-S Leung, Man-H Wong, et al.
BMC Bioinformatics|February 11, 2017
Correcting the impact of docking pose generation error on binding affinity predictionHongjian Li, Kwong-Sak Leung, Man-Hon Wong, et al.
Molecules (Basel, Switzerland)|June 16, 2015
Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random ForestHongjian Li, Kwong-Sak Leung, Man-Hon Wong, et al.
Biomaterials Science|July 4, 2023
A machine learning approach to predict cellular uptake of pBAE polyplexesAparna Loecher, Michael Bruyns-Haylett, Pedro J Ballester, et al.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)|July 5, 2022
Interpretable Machine Learning Models to Predict the Resistance of Breast Cancer Patients to Doxorubicin from Their microRNA ProfilesAdeolu Z Ogunleye, Chayanit Piyawajanusorn, Anthony Gonçalves, et al.
Journal of the Royal Society, Interface|July 10, 2009
Prospective virtual screening with Ultrafast Shape Recognition: the identification of novel inhibitors of arylamine N-acetyltransferasesPedro J Ballester, Isaac Westwood, Nicola Laurieri, et al.
NAR Genomics and Bioinformatics|September 8, 2025
A pan-cancer, pan-treatment model for predicting drug responses from patient-derived xenograftsShruti Gupta, Vikash K Mohani, Ghita Ghislat, et al.
Frontiers in Chemistry|August 6, 2019
Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC DataPavel Sidorov, Stefan Naulaerts, Jérémy Ariey-Bonnet, et al.
Biomedicines|October 23, 2021
Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular ProfilesLinh C Nguyen, Stefan Naulaerts, Alejandra Bruna, et al.
Pageof 6

Showing results (41-50 of 60) with videos related to

Sort By:
Pageof 6
Journal of Cheminformatics|April 6, 2024
Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitorsKlaudia Caba, Viet-Khoa Tran-Nguyen, Taufiq Rahman, et al.
Nucleic Acids Research|April 24, 2016
USR-VS: a web server for large-scale prospective virtual screening using ultrafast shape recognition techniquesHongjian Li, Kwong-S Leung, Man-H Wong, et al.
BMC Bioinformatics|February 11, 2017
Correcting the impact of docking pose generation error on binding affinity predictionHongjian Li, Kwong-Sak Leung, Man-Hon Wong, et al.
Molecules (Basel, Switzerland)|June 16, 2015
Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random ForestHongjian Li, Kwong-Sak Leung, Man-Hon Wong, et al.
Biomaterials Science|July 4, 2023
A machine learning approach to predict cellular uptake of pBAE polyplexesAparna Loecher, Michael Bruyns-Haylett, Pedro J Ballester, et al.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)|July 5, 2022
Interpretable Machine Learning Models to Predict the Resistance of Breast Cancer Patients to Doxorubicin from Their microRNA ProfilesAdeolu Z Ogunleye, Chayanit Piyawajanusorn, Anthony Gonçalves, et al.
Journal of the Royal Society, Interface|July 10, 2009
Prospective virtual screening with Ultrafast Shape Recognition: the identification of novel inhibitors of arylamine N-acetyltransferasesPedro J Ballester, Isaac Westwood, Nicola Laurieri, et al.
NAR Genomics and Bioinformatics|September 8, 2025
A pan-cancer, pan-treatment model for predicting drug responses from patient-derived xenograftsShruti Gupta, Vikash K Mohani, Ghita Ghislat, et al.
Frontiers in Chemistry|August 6, 2019
Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC DataPavel Sidorov, Stefan Naulaerts, Jérémy Ariey-Bonnet, et al.
Biomedicines|October 23, 2021
Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular ProfilesLinh C Nguyen, Stefan Naulaerts, Alejandra Bruna, et al.
Pageof 6