AIMC Topic: Databases, Chemical

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Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics.

ACS chemical neuroscience
Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein...

Predictive modeling for odor character of a chemical using machine learning combined with natural language processing.

PloS one
Recent studies on machine learning technology have reported successful performances in some visual and auditory recognition tasks, while little has been reported in the field of olfaction. In this paper we report computational methods to predict the ...

Development of Ligand-based Big Data Deep Neural Network Models for Virtual Screening of Large Compound Libraries.

Molecular informatics
High-performance ligand-based virtual screening (VS) models have been developed using various computational methods, including the deep neural network (DNN) method. There are high expectations for exploration of the advanced capabilities of DNN to im...

Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Promising and Economical Small Molecule Inhibitors of Complement Factor C1s.

Biomolecules
When excessively activated, C1 is insufficiently regulated, which results in tissue damage. Such tissue damage causes the complement system to become further activated to remove the resulting tissue damage, and a vicious cycle of activation/tissue da...

Knowledge-Based Conformer Generation Using the Cambridge Structural Database.

Journal of chemical information and modeling
Fast generation of plausible molecular conformations is central to molecular modeling. This paper presents an approach to conformer generation that makes extensive use of the information available in the Cambridge Structural Database. By using geomet...

Inner and Outer Recursive Neural Networks for Chemoinformatics Applications.

Journal of chemical information and modeling
Deep learning methods applied to problems in chemoinformatics often require the use of recursive neural networks to handle data with graphical structure and variable size. We present a useful classification of recursive neural network approaches into...

SCScore: Synthetic Complexity Learned from a Reaction Corpus.

Journal of chemical information and modeling
Several definitions of molecular complexity exist to facilitate prioritization of lead compounds, to identify diversity-inducing and complexifying reactions, and to guide retrosynthetic searches. In this work, we focus on synthetic complexity and ref...

Potent pairing: ensemble of long short-term memory networks and support vector machine for chemical-protein relation extraction.

Database : the journal of biological databases and curation
Biomedical researchers regularly discover new interactions between chemical compounds/drugs and genes/proteins, and report them in research literature. Having knowledge about these interactions is crucially important in many research areas such as pr...

Extracting chemical-protein relations using attention-based neural networks.

Database : the journal of biological databases and curation
Relation extraction is an important task in the field of natural language processing. In this paper, we describe our approach for the BioCreative VI Task 5: text mining chemical-protein interactions. We investigate multiple deep neural network (DNN) ...