AIMC Topic: Models, Chemical

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Dual graph convolutional neural network for predicting chemical networks.

BMC bioinformatics
BACKGROUND: Predicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery. The recent rapid growth of the amount of ...

Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis.

Journal of medicinal chemistry
Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing synthetic planning into their overall appr...

How Sure Can We Be about ML Methods-Based Evaluation of Compound Activity: Incorporation of Information about Prediction Uncertainty Using Deep Learning Techniques.

Molecules (Basel, Switzerland)
A great variety of computational approaches support drug design processes, helping in selection of new potentially active compounds, and optimization of their physicochemical and ADMET properties. Machine learning is a group of methods that are able ...

A Deep Learning-Based Chemical System for QSAR Prediction.

IEEE journal of biomedical and health informatics
Research on quantitative structure-activity relationships (QSAR) provides an effective approach to determine new hits and promising lead compounds during drug discovery. In the past decades, various works have gained good performance for QSAR with th...

Modeling and optimization of imidacloprid degradation by catalytic percarbonate oxidation using artificial neural network and Box-Behnken experimental design.

Chemosphere
Due to its toxicity and persistence, pesticide pollution poses a serious threat to human health and the environment. Imidacloprid or IMD is an archetypal neonicotinoid insecticide commonly used to protect a variety of crops worldwide. The present stu...

Machine learning meets pK .

F1000Research
We present a small molecule pK prediction tool entirely written in Python. It predicts the macroscopic pK value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest perf...

A deep learning approach for the blind logP prediction in SAMPL6 challenge.

Journal of computer-aided molecular design
Water octanol partition coefficient serves as a measure for the lipophilicity of a molecule and is important in the field of drug discovery. A novel method for computational prediction of logarithm of partition coefficient (logP) has been developed u...

The METLIN small molecule dataset for machine learning-based retention time prediction.

Nature communications
Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retent...

Mesoporous cobalt monoxide-supported platinum nanoparticles: Superior catalysts for the oxidative removal of benzene.

Journal of environmental sciences (China)
Mesoporous CoO (meso-CoO)-supported Pt (0.53 wt.% Pt/meso-CoO) was synthesized via the KIT-6-templating and polyvinyl alcohol (PVA)-assisted reduction routes. Mesoporous CoO (meso-CoO) was fabricated through in situ reduction of meso-CoO with glycero...

Response surface methodology and artificial neural network approach for the optimization of ultrasound-assisted extraction of polyphenols from garlic.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
This paper aimed to establish the optimal conditions for ultrasound-assisted extraction of polyphenols from domestic garlic (Allium sativum L.) using response surface methodology (RSM) and artificial neural network (ANN) approach. A 4-factor-3-level ...