AIMC Topic: Databases, Chemical

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Accurate predictions of aqueous solubility of drug molecules via the multilevel graph convolutional network (MGCN) and SchNet architectures.

Physical chemistry chemical physics : PCCP
Deep learning based methods have been widely applied to predict various kinds of molecular properties in the pharmaceutical industry with increasingly more success. In this study, we propose two novel models for aqueous solubility predictions, based ...

Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning.

Biomolecules
Microbial natural products (NPs) are an important source of drugs, however, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint sui...

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...

Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning.

Molecules (Basel, Switzerland)
A significant percentage of Duchenne muscular dystrophy (DMD) cases are caused by premature termination codon (PTC) mutations in the dystrophin gene, leading to the production of a truncated, non-functional dystrophin polypeptide. PTC-suppressing com...

Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches.

Laboratory investigation; a journal of technical methods and pathology
As defined by the World Health Organization, an endocrine disruptor is an exogenous substance or mixture that alters function(s) of the endocrine system and consequently causes adverse health effects in an intact organism, its progeny, or (sub)popula...

BIOINTMED: integrated biomedical knowledge base with ontologies and clinical trials.

Medical & biological engineering & computing
Biomedical data are complex and heterogeneous. An ample reliable quantity of data is important for understanding and exploring the domain. The work aims to integrate biomedical data from various heterogeneous sources like dictionaries or corpus and a...

Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.

Pharmaceutical research
PURPOSE: To advance fundamental biological and translational research with the bacterium Neisseria gonorrhoeae through the prediction of novel small molecule growth inhibitors via naïve Bayesian modeling methodology.

"Ring Breaker": Neural Network Driven Synthesis Prediction of the Ring System Chemical Space.

Journal of medicinal chemistry
Ring systems in pharmaceuticals, agrochemicals, and dyes are ubiquitous chemical motifs. While the synthesis of common ring systems is well described and novel ring systems can be readily and computationally enumerated, the synthetic accessibility of...

An Artificial Intelligence Approach to Proactively Inspire Drug Discovery with Recommendations.

Journal of medicinal chemistry
Artificial intelligence (AI) is becoming established in drug discovery. For example, many in the industry are applying machine learning approaches to target discovery or to optimize compound synthesis. While our organization is certainly applying the...

Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity.

Molecular informatics
Over the last few years more and more organ and idiosyncratic toxicities were linked to mitochondrial toxicity. Despite well-established assays, such as the seahorse and Glucose/Galactose assay, an in silico approach to mitochondrial toxicity is stil...