TY  - JOUR
TI  - Development of Predictive in Silico Cytotoxic Activity Model 
to Predict the Cytotoxicity of a Diverse Set of Colchicine 
Binding Site Inhibitors
AB  - Microtubule-targeting agents often have limitations to the development of resistance. Colchicine binding site (CBS) 
agents have several advantages compared with other tubulin inhibitors. Numerous medications in this class are less 
susceptible to multidrug resistance that restricts the viability of different inhibitors. In the present study, molecules that 
bind to the CBS of tubulin are collected from PubMed literature against the A549 cancer cell line. Regression models 
were established between the descriptor and IC50 value of all the compounds present in the training set based on 
significant molecular fingerprints using multiple linear regression (MLR). Fifteen most significant descriptors selected 
include Burden modified eigenvalue descriptors, PaDEL-weighted path descriptor, autocorrelation descriptor, topological distance matrix descriptor, MLFER descriptor, Barysz matrix descriptor, chi path cluster descriptor, and validated 
using internal and external validation parameters. The selected MLR-GA model has R2adjusted = 0.7895, Q2
CV = 0.76577, 
R2
pred = 0.7419, and R2
tes = 0.77373. An applicability domain is also defined so that it defines the chemical space that the 
model can predict. The above details suggest a good predictive model for CBS inhibitors that can predict the IC50 value 
of the unknown chemical compound.
AU  - SINGH, Vijay Kumar
AU  - SAHU, Sumanta Kumar
AU  - OJHA, Krishna Kumar
DO  - 10.14744/ejmo.2022.44123
PY  - 2022
JO  - Eurasian Journal of Medicine and Oncology
VL  - 6
IS  - 2
SN  - 2587-2400
SP  - 172
EP  - 181
DB  - TRDizin
UR  - http://search/yayin/detay/531311
ER  -