RT Journal Article
SR Electronic
T1 KinasepKipred: A Predictive Model for Estimating Ligand-Kinase Inhibitor Constant (<em>pK</em><sub><em>i</em></sub>)
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 798561
DO 10.1101/798561
A1 Govinda, KC
A1 Hassan, Md Mahmudulla
A1 Sirimulla, Suman
YR 2019
UL http://biorxiv.org/content/early/2019/10/11/798561.abstract
AB Summary Kinases are one of the most important classes of drug targets for therapeutic use. Algorithms that can accurately predict the drug-kinase inhibitor constant (pKi) of kinases can considerably accelerate the drug discovery process. In this study, we have developed computational models, leveraging machine learning techniques, to predict ligand-kinase (pKi) values. Kinase-ligand inhibitor constant (Ki) data was retrieved from Drug Target Commons (DTC) and Metz databases. Machine learning models were developed based on structural and physicochemical features of the protein and, topological pharmacophore atomic triplets fingerprints of the ligands. Three machine learning models [random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN)] were tested for model development. RF model was finally selected based on the evaluation metrics on test datasets and used for web implementation.Availability GitHub: https://github.com/sirimullalab/KinasepKipred, Docker: sirimullalab/kinasepkipredImplementation https://drugdiscovery.utep.edu/pkiContact ssirimulla{at}utep.eduSupplementary information Supplementary data are available Bioinformatics online.