In Silico Prediction of Volume of Distribution in Human Using Linear and Nonlinear Models on a 669 Compound Data Set

2009 ◽  
Vol 52 (14) ◽  
pp. 4488-4495 ◽  
Author(s):  
Giuliano Berellini ◽  
Clayton Springer ◽  
Nigel J. Waters ◽  
Franco Lombardo
Keyword(s):  
In Silico ◽  
Nonlinear Models ◽  
Volume Of Distribution ◽  
In Silico Prediction ◽  
Data Set ◽  
Linear And Nonlinear

Benchmark Data Set for in Silico Prediction of Ames Mutagenicity

2009 ◽  
Vol 49 (9) ◽  
pp. 2077-2081 ◽  
Author(s):  
Katja Hansen ◽  
Sebastian Mika ◽  
Timon Schroeter ◽  
Andreas Sutter ◽  
Antonius ter Laak ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Prediction ◽  
Data Set ◽  
Benchmark Data

scholarly journals In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity

2022 ◽  
Vol 12 ◽  
Author(s):  
Yinping Shi ◽  
Yuqing Hua ◽  
Baobao Wang ◽  
Ruiqiu Zhang ◽  
Xiao Li
Keyword(s):  
Drug Development ◽  
In Silico ◽  
Molecular Properties ◽  
Structural Basis ◽  
Consensus Model ◽  
In Silico Prediction ◽  
Drug Induced ◽  
Data Set ◽  
Chemical Structures ◽  
Structural Alerts

Drug induced nephrotoxicity is a major clinical challenge, and it is always associated with higher costs for the pharmaceutical industry and due to detection during the late stages of drug development. It is desirable for improving the health outcomes for patients to distinguish nephrotoxic structures at an early stage of drug development. In this study, we focused on in silico prediction and insights into the structural basis of drug induced nephrotoxicity, based on reliable data on human nephrotoxicity. We collected 565 diverse chemical structures, including 287 nephrotoxic drugs on humans in the real world, and 278 non-nephrotoxic approved drugs. Several different machine learning and deep learning algorithms were employed for in silico model building. Then, a consensus model was developed based on three best individual models (RFR_QNPR, XGBOOST_QNPR, and CNF). The consensus model performed much better than individual models on internal validation and it achieved prediction accuracy of 86.24% external validation. The results of analysis of molecular properties differences between nephrotoxic and non-nephrotoxic structures indicated that several key molecular properties differ significantly, including molecular weight (MW), molecular polar surface area (MPSA), AlogP, number of hydrogen bond acceptors (nHBA), molecular solubility (LogS), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). These molecular properties may be able to play an important part in the identification of nephrotoxic chemicals. Finally, 87 structural alerts for chemical nephrotoxicity were mined with f-score and positive rate analysis of substructures from Klekota-Roth fingerprint (KRFP). These structural alerts can well identify nephrotoxic drug structures in the data set. The in silico models and the structural alerts could be freely accessed via https://ochem.eu/article/140251 and http://www.sapredictor.cn, respectively. We hope the results should provide useful tools for early nephrotoxicity estimation in drug development.

scholarly journals A benchmark data set for in silico prediction of ames mutagenicity

2009 ◽  
Vol 3 (S1) ◽  
Author(s):  
K Hansen ◽  
S Mika ◽  
T Schroeter ◽  
A Sutter ◽  
A Ter Laak ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Prediction ◽  
Data Set ◽  
Benchmark Data

Speciation of cultivated temperate and tropical Pleurotus species –an in silico prediction using conserved sequences

2019 ◽  
Vol 28 (1) ◽  
Author(s):  
Anupam Barh ◽  
V P Sharma ◽  
Shwet Kamal ◽  
Mahantesh Shirur ◽  
Sudheer Kumar Annepu ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Prediction ◽  
Conserved Sequences ◽  
Pleurotus Species
Sign in / Sign up

Export Citation Format

Share Document