AIMC Topic: Databases, Chemical

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A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network.

Journal of computational biology : a journal of computational molecular cell biology
Identifying the interaction between drugs and target proteins is an important area of drug research, which provides a broad prospect for low-risk and faster drug development. However, due to the limitations of traditional experiments when revealing d...

Novel Method Proposing Chemical Structures with Desirable Profile of Activities Based on Chemical and Protein Spaces.

Molecular informatics
Active molecules among numerous chemical structures in a chemical database can be searched easily by statistical prediction of compound-protein interactions. However, constructing a simple prediction model against one protein does not aid drug design...

Bias-Free Chemically Diverse Test Sets from Machine Learning.

ACS combinatorial science
Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal an...

Identification of small molecules using accurate mass MS/MS search.

Mass spectrometry reviews
Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-base...

The influence of the negative-positive ratio and screening database size on the performance of machine learning-based virtual screening.

PloS one
The machine learning-based virtual screening of molecular databases is a commonly used approach to identify hits. However, many aspects associated with training predictive models can influence the final performance and, consequently, the number of hi...

MOST: most-similar ligand based approach to target prediction.

BMC bioinformatics
BACKGROUND: Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searc...

Active learning for computational chemogenomics.

Future medicinal chemistry
AIM: Computational chemogenomics models the compound-protein interaction space, typically for drug discovery, where existing methods predominantly either incorporate increasing numbers of bioactivity samples or focus on specific subfamilies of protei...

ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature.

Journal of chemical information and modeling
The emergence of "big data" initiatives has led to the need for tools that can automatically extract valuable chemical information from large volumes of unstructured data, such as the scientific literature. Since chemical information can be present i...

Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification-Salvinorin A as a case study.

Journal of molecular graphics & modelling
Drug abuse is a serious problem worldwide. Recently, hallucinogens have been reported as a potential preventative and auxiliary therapy for substance abuse. However, the use of hallucinogens as a drug abuse treatment has potential risks, as the funda...

Chemical entity recognition in patents by combining dictionary-based and statistical approaches.

Database : the journal of biological databases and curation
We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Pa...