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Molecular Structure

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Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties.

Molecular pharmaceutics
Machine learning (ML) has become an indispensable tool to predict absorption, distribution, metabolism, and excretion (ADME) properties in pharmaceutical research. ML algorithms are trained on molecular structures and corresponding ADME assay data to...

Message Passing Neural Networks Improve Prediction of Metabolite Authenticity.

Journal of chemical information and modeling
Cytochrome P450 enzymes aid in the elimination of a preponderance of small molecule drugs, but can generate reactive metabolites that may adversely react with protein and DNA and prompt drug candidate attrition or market withdrawal. Previously develo...

One-pot multicomponent synthesis of novel pyridine derivatives for antidiabetic and antiproliferative activities.

Future medicinal chemistry
Due to the close relationship of diabetes with hypertension reported in various research, a set of pyridine derivatives with US FDA-approved drug cores were designed and integrated by artificial intelligence. Novel pyridines were designed and synth...

Artificial Intelligence in Decrypting Cytoprotective Activity under Oxidative Stress from Molecular Structure.

International journal of molecular sciences
Artificial intelligence (AI) is widely explored nowadays, and it gives opportunities to enhance classical approaches in QSAR studies. The aim of this study was to investigate the cytoprotective activity parameter under oxidative stress conditions for...

DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening.

Journal of chemical information and modeling
Recently emerging generative AI models enable us to produce a vast number of compounds for potential applications. While they can provide novel molecular structures, the synthetic feasibility of the generated molecules is often questioned. To address...

Effect of Flattened Structures of Molecules and Materials on Machine Learning Model Training.

Journal of chemical information and modeling
A key aspect of producing accurate and reliable machine learning models for the prediction of properties of quantum chemistry (QC) data is identifying possible data characteristics that may negatively influence model training. In previous work, we id...

Developing machine learning approaches to identify candidate persistent, mobile and toxic (PMT) and very persistent and very mobile (vPvM) substances based on molecular structure.

Water research
Determining which substances on the global market could be classified as persistent, mobile and toxic (PMT) substances or very persistent, very mobile (vPvM) substances is essential to prevent or reduce drinking water contamination from them. This st...

De novo drug design based on patient gene expression profiles via deep learning.

Molecular informatics
Computational de novo drug design is a challenging issue in medicine, and it is desirable to consider all of the relevant information of the biological systems in a disease state. Here, we propose a novel computational method to generate drug candida...

XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm.

Journal of biomolecular structure & dynamics
Determining the structure-odor relationship has always been a very challenging task. The main challenge in investigating the correlation between the molecular structure and its associated odor is the ambiguous and obscure nature of verbally defined o...

Graph neural networks for the identification of novel inhibitors of a small RNA.

SLAS discovery : advancing life sciences R & D
MicroRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation and have been implicated in various diseases, including cancers and lung disease. In recent years, Graph Neural Networks (GNNs) have emerged as powerful tools for analyzin...