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 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

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 Distributed Representation of Chemical Fragments

2017 ◽  
Author(s):  
Suman Chakravarti
Keyword(s):  
Hydrophobic Interactions ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
In Silico Prediction ◽  
Data Set ◽  
Computer Aided Drug Design ◽  
Benchmark Data ◽  
Structure Activity ◽  
Benchmark Datasets ◽  
Quantitative Structure Activity Relationships

<p>1. Approximately 3.7 million (3,700,103) PubChem compounds were used in training the fragment vectors. Five million compounds (from CID 1 to 5,000,000) were downloaded in SMILES format from PubChem Download Service<sup>1</sup> for this purpose. Inorganic part of salts, charges on certain atoms were neutralized, components of mixtures were split and only one chemical from sets of duplicates were kept. Approximately 100,000 chemicals were held out for future needs.</p> <p>2. Hansen<sup>2</sup> and Bursi<sup>3</sup> Ames mutagenicity benchmark datasets were used. The data was preprocessed in the same way as the PubChem chemicals, except in the case of duplicates, the compound with the highest mutagenicity value was retained, leaving 6771 compounds (3639 mutagenic and 3132 non-mutagenic).</p> <p>3. A dataset of 575 compounds was taken from the publication of Ghose <i>et al</i><sup>4</sup> to compute molar refractivity of the fragments. </p> An in-house dataset<sup>5</sup> of 7000 chemicals (Zhu Hao et al) with their experimentally observed LogP was also used to help in computing LogP contribution of the fragments.<div><br></div><div> <p>1. PubChem Download Service, URL: https://pubchem.ncbi.nlm.nih.gov/pc_fetch/pc_fetch.cgi.</p> <p>2. Hansen, K., Mika, S., Schroeter, T., Sutter, A., Laak, A. T., Steger-Hartmann, T., Heinrich, N. and Müller, K. R. (2009) Benchmark Data Set for in Silico Prediction of Ames Mutagenicity. J. Chem. Inf. Model. 49, 2077-2081.</p> <p>3. Kazius, J., McGuire, R., Bursi, R. (2005) Derivation and validation of toxicophores for mutagenicity prediction. J. Med. Chem. 48, 312-320.</p> <p>4. Ghose, A. K., Crippen, G. M. (1987) Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions. J. Chem. Inf. Comput. Sci. 27(1), 21-35.</p> Zhu, H., Sedykh, A., Chakravarti, S. K., Klopman, G. (2005) A new group contribution approach to the calculation of LogP. Current Computer-Aided Drug Design 1, 3-9. <br></div>

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

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
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