ESSESA: an expert system for elucidation of structures from spectra. 1. Knowledge base of infrared spectra and analysis and interpretation programs

Thakkar

Chen

et al. 2017

Sci Rep

Self Cite

Drug-induced liver injury (DILI) presents a significant challenge to drug development and regulatory science. The FDA’s Liver Toxicity Knowledge Base (LTKB) evaluated >1000 drugs for their likelihood of causing DILI in humans, of which >700 drugs were classified into three categories (most-DILI, less-DILI, and no-DILI). Based on this dataset, we developed and compared 2-class and 3-class DILI prediction models using the machine learning algorithm of Decision Forest (DF) with Mold2 structural descriptors. The models were evaluated through 1000 iterations of 5-fold cross-validations, 1000 bootstrapping validations and 1000 permutation tests (that assessed the chance correlation). Furthermore, prediction confidence analysis was conducted, which provides an additional parameter for proper interpretation of prediction results. We revealed that the 3-class model not only had a higher resolution to estimate DILI risk but also showed an improved capability to differentiate most-DILI drugs from no-DILI drugs in comparison with the 2-class DILI model. We demonstrated the utility of the models for drug ingredients with warnings very recently issued by the FDA. Moreover, we identified informative molecular features important for assessing DILI risk. Our results suggested that the 3-class model presents a better option than the binary model (which most publications are focused on) for drug safety evaluation.

Section: Methodsmentioning

confidence: 99%

Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs

Thakkar

Chen

et al. 2017

Sci Rep

Self Cite

“…Mold2 is a freely available software package that calculates molecular descriptors from two-dimensional chemical structures and has been demonstrated to be a reliable tool for QSAR models development [ 52 , 53 , 54 , 55 ]. One of the attractive features of this software package is its high computational speed due to adoption of an extremely fast algorithm for ring structure recognition [ 56 ] and the utilization of an efficient representation system for chemical structures [ 57 , 58 ] that have been developed in the structure elucidation system based on nuclear magnetic resonance (NMR) [ 59 , 60 , 61 ] and infrared (FT-IR) spectra [ 62 ]. For each structure, 777 molecular descriptors were calculated from Mold2 and then preprocessed by removing duplicate values for all the candidates in TS-1 or TS-2.…”

Section: Methodsmentioning

confidence: 99%

Experimental Data Extraction and in Silico Prediction of the Estrogenic Activity of Renewable Replacements for Bisphenol A

Harvey

Palmese

et al. 2016

IJERPH

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Bisphenol A (BPA) is a ubiquitous compound used in polymer manufacturing for a wide array of applications; however, increasing evidence has shown that BPA causes significant endocrine disruption and this has raised public concerns over safety and exposure limits. The use of renewable materials as polymer feedstocks provides an opportunity to develop replacement compounds for BPA that are sustainable and exhibit unique properties due to their diverse structures. As new bio-based materials are developed and tested, it is important to consider the impacts of both monomers and polymers on human health. Molecular docking simulations using the Estrogenic Activity Database in conjunction with the decision forest were performed as part of a two-tier in silico model to predict the activity of 29 bio-based platform chemicals in the estrogen receptor-α (ERα). Fifteen of the candidates were predicted as ER binders and fifteen as non-binders. Gaining insight into the estrogenic activity of the bio-based BPA replacements aids in the sustainable development of new polymeric materials.

“…Mold 2 is a free software which calculates molecular descriptors from 2D chemical structures. This software is very fast because it adopts the extremely rapid algorithm for cyclic structure recognition [ 56 ] and uses the efficient chemical structure representation system [ 57 , 58 ] that has shown high efficiency in the system for chemicals structure elucidation based on infrared [ 59 ] and nuclear magnetic resonance (NMR) spectra [ 60 , 61 , 62 ]. Mold 2 has been demonstrated to be reliable for developing QSAR models [ 63 , 64 ].…”

Section: Methodsmentioning

confidence: 99%

A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals

Shen

et al. 2016

IJERPH

Self Cite

Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold2 software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.