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Cheminformatics

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Artificial Intelligence and Cheminformatics-Guided Modern Privileged Scaffold Research.

Current topics in medicinal chemistry
With the rapid development of computer science in scopes of theory, software, and hardware, artificial intelligence (mainly in form of machine learning and more complex deep learning) combined with advanced cheminformatics is playing an increasingly ...

Improved Deep Learning Based Method for Molecular Similarity Searching Using Stack of Deep Belief Networks.

Molecules (Basel, Switzerland)
Virtual screening (VS) is a computational practice applied in drug discovery research. VS is popularly applied in a computer-based search for new lead molecules based on molecular similarity searching. In chemical databases similarity searching is us...

Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently.

The Biochemical journal
The number of 'small' molecules that may be of interest to chemical biologists - chemical space - is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved 'forward' problems (have molecule...

Self-Optimizing Support Vector Elastic Net.

Analytical chemistry
Chemometrics is widely used to solve various quantitative and qualitative problems in analytical chemistry. A self-optimizing chemometrics method facilitates scientists to exploit the advantages of chemometrics. In this report, a parameter-free suppo...

A Recurrent Neural Network model to predict blood-brain barrier permeability.

Computational biology and chemistry
The rapid development of computational methods and the increasing volume of chemical and biological data have contributed to an immense growth in chemical research. This field of study is known as "chemoinformatics," which is a discipline that uses m...

Impact of Chemist-In-The-Loop Molecular Representations on Machine Learning Outcomes.

Journal of chemical information and modeling
The development of molecular descriptors is a central challenge in cheminformatics. Most approaches use algorithms that extract atomic environments or end-to-end machine learning. However, a looming question is that how do these approaches compare wi...

Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics.

Scientific reports
One of the major drawbacks of cheminformatics is a large amount of information present in the datasets. In the majority of cases, this information contains redundant instances that affect the analysis of similarity measurements with respect to drug d...

Learning Molecular Representations for Medicinal Chemistry.

Journal of medicinal chemistry
The accurate modeling and prediction of small molecule properties and bioactivities depend on the critical choice of molecular representation. Decades of informatics-driven research have relied on expert-designed molecular descriptors to establish qu...

Detection and identification of Cannabis sativa L. using near infrared hyperspectral imaging and machine learning methods. A feasibility study.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Remote identification of illegal plantations of Cannabis sativa Linnaeus is an important task for the Brazilian Federal Police. The current analytical methodology is expensive and strongly dependent on the expertise of the forensic investigator. A fa...