Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities
نویسندگان
چکیده
منابع مشابه
Prediction of drug binding affinities by comparative binding energy analysis.
A new computational method for deducing quantitative structure-activity relationships (QSARs) using structural data from ligand-macromolecule complexes is presented. First, the ligand-macromolecule interaction energy is computed for a set of ligands using molecular mechanics calculations. Then, by selecting and scaling components of the ligand-macromolecule interaction energy that show good pre...
متن کاملDeepDTA: Deep Drug-Target Binding Affinity Prediction
The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-ligand interactions assume a continuum of binding strength values, also called...
متن کاملComputational study of Anticancer Dasatinib for drug delivery systems
Dasatinib is a tyrosine kinase inhibitor (TKI) that is used to treat chronic myeloid leukemia and in the management of ulcerative colitis (UC) and to provide appropriate results in treatment. Dasatinib is significantly higher and faster than full cytogenetic and large molecular responses as compared to imatinib. In the recent study, using the NMR data, the frequency and thermochemical propertie...
متن کاملComputational Study of Anticancer Dasatinib for Drug Delivery Systems
Dasatinib is a tyrosine kinase inhibitor (TKI) that is used to treat chronic myeloid leukemiaand in the management of ulcerative colitis (UC) and to provide appropriate results in treatment. Dasatinib is significantly higher and faster than full cytogenetic and large molecular responses as compared to imatinib. In the recent study, using the NMR data, thermochemical properties of the dasa...
متن کاملSimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines
Computational prediction of the interaction between drugs and targets is a standing challenge in the field of drug discovery. A number of rather accurate predictions were reported for various binary drug-target benchmark datasets. However, a notable drawback of a binary representation of interaction data is that missing endpoints for non-interacting drug-target pairs are not differentiated from...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Chemistry
سال: 2019
ISSN: 2296-2646
DOI: 10.3389/fchem.2019.00782