Abstract
BackgroundIdentifying and assessing ligand-target binding is a core component in early drug discovery as one or more unwanted interactions may be associated with safety issues.ContributionsWe present an open-source, extendable web service for predicting target profiles with confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking scores from a large virtual library. The method uses conformal prediction to produce valid measures of prediction efficiency for a particular confidence level. The service also offers the possibility to dock chemical structures to the panel of targets with QuickVina on individual compound basis.ResultsThe docking procedure and resulting models were validated by docking well-known inhibitors for each of the 7 targets using QuickVina. The model predictions showed comparable performance to molecular docking scores against an external validation set. The implementation as publicly available microservices on Kubernetes ensures resilience, scalability, and extensibility.
Highlights
Identifying and assessing ligand-target binding is a core component in early drug discovery as one or more unwanted interactions may be associated with safety issues.Contributions: We present an open-source, extendable web service for predicting target profiles with confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking scores from a large virtual library
In this paper we introduce an extensible methodology for predicting target profiles with confidence, where models are trained on docking scores
We show in this manuscript that target profiles built using docking scores has predictive properties, and that conformal prediction enables quantifying the confidence for each target in a panel
Summary
Identifying and assessing ligand-target binding is a core component in early drug discovery as one or more unwanted interactions may be associated with safety issues.Contributions: We present an open-source, extendable web service for predicting target profiles with confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking scores from a large virtual library. To determine drug-target interactions, pharmaceutical companies and academic institutions involved in drug discovery apply different techniques to detect drug-target interactions, including in-vitro pharmacological profiling [5]. Another interesting method is to build in-silico target profiles for ligands [6][7], which helps in. A common method to construct target profiles is to predict them using QSAR models based on interaction values available for known active ligands in large interaction databases like ChEMBL [8] and ExCAPE-DB [9]. Bender et al [12] employs Bayesian based technique to prepare seventy QSAR models that were used to create target profiles to predict adverse off-target effects of drugs.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
