AIMC Topic: Chemical Phenomena
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Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery.
Scientific reports
May 25, 2017
Quantitative structure-activity relationship modeling using machine learning techniques constitutes a complex computational problem, where the identification of the most informative molecular descriptors for predicting a specific target property play...
Thin polymeric films for building biohybrid microrobots.
Bioinspiration & biomimetics
Mar 6, 2017
This paper aims to describe the disruptive potential that polymeric thin films have in the field of biohybrid devices and to review the recent efforts in this area. Thin (thickness < 1 mm) and ultra-thin (thickness < 1 µm) matrices possess a seri...
Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.
Scientific reports
Feb 13, 2017
Antimicrobial peptides (AMPs) are important components of the innate immune system that have been found to be effective against disease causing pathogens. Identification of AMPs through wet-lab experiment is expensive. Therefore, development of effic...
In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning.
Journal of chemical information and modeling
Jan 9, 2017
There are little available toxicity data on the vast majority of chemicals in commerce. High-throughput screening (HTS) studies, such as those being carried out by the U.S. Environmental Protection Agency (EPA) ToxCast program in partnership with the...
Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy.
BMC systems biology
Dec 23, 2016
BACKGROUND: It is necessary and essential to discovery protein function from the novel primary sequences. Wet lab experimental procedures are not only time-consuming, but also costly, so predicting protein structure and function reliably based only o...
Predicting the Presence of Uncommon Elements in Unknown Biomolecules from Isotope Patterns.
Analytical chemistry
Jul 22, 2016
The determination of the molecular formula is one of the earliest and most important steps when investigating the chemical nature of an unknown compound. Common approaches use the isotopic pattern of a compound measured using mass spectrometry. Compu...
ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition.
BioMed research international
Jan 28, 2016
Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This ...
Benefit of Retraining pKa Models Studied Using Internally Measured Data.
Journal of chemical information and modeling
Jun 29, 2015
The ionization state of drugs influences many pharmaceutical properties such as their solubility, permeability, and biological activity. It is therefore important to understand the structure property relationship for the acid-base dissociation consta...
An in silico expert system for the identification of eye irritants.
SAR and QSAR in environmental research
May 13, 2015
This report describes development of an in silico, expert rule-based method for the classification of chemicals into irritants or non-irritants to eye, as defined by the Draize test. This method was developed to screen data-poor cosmetic ingredient c...
Comprehensive studies of hydrogeochemical processes and quality status of groundwater with tools of cluster, grouping analysis, and fuzzy set method using GIS platform: a case study of Dalcheon in Ulsan City, Korea.
Environmental science and pollution research international
Mar 18, 2015
This research aimed at developing comprehensive assessments of physicochemical quality of groundwater for drinking and irrigation purposes at Dalcheon in Ulsan City, Korea. The mean concentration of major ions represented as follows: Ca (94.3 mg/L) >...
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