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Quantitative Structure-Activity Relationship

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Technique of Augmenting Molecular Graph Data by Perturbating Hidden Features.

Molecular informatics
Quantitative structure-property relationship models are useful in efficiently searching for molecules with desired properties in drug discovery and materials development. In recent years, many such models based on graph neural networks, showing good ...

Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures.

Molecular diversity
In designing drug dosing for hemodialysis patients, the removal rate (RR) of the drug by hemodialysis is important. However, acquiring the RR is difficult, and there is a need for an estimation method that can be used in clinical settings. In this st...

Machine learning models on chemical inhibitors of mitochondrial electron transport chain.

Journal of hazardous materials
Chemicals can induce adverse effects in humans by inhibiting mitochondrial electron transport chain (ETC) such as disrupting mitochondrial membrane potential, enhancing oxidative stress and causing some diseases. Thus, identifying ETC inhibitors (ETC...

QSAR and deep learning model for virtual screening of potential inhibitors against Inosine 5' Monophosphate dehydrogenase (IMPDH) of Cryptosporidium parvum.

Journal of molecular graphics & modelling
Cryptosporidium parvum (Cp) causes a gastro-intestinal disease called Cryptosporidiosis. C. parvum Inosine 5' monophosphate dehydrogenase (CpIMPDH) is responsible for the production of guanine nucleotides. In the present study, 37 known urea-based co...

Block-wise Exploration of Molecular Descriptors with Multi-block Orthogonal Component Analysis (MOCA).

Molecular informatics
Data tables for machine learning and structure-activity relationship modelling (QSAR) are often naturally organized in blocks of data, where multiple molecular representations or sets of descriptors form the blocks. Multi-block Orthogonal Component A...

RespiraTox - Development of a QSAR model to predict human respiratory irritants.

Regulatory toxicology and pharmacology : RTP
Respiratory irritation is an important human health endpoint in chemical risk assessment. There are two established modes of action of respiratory irritation, 1) sensory irritation mediated by the interaction with sensory neurons, potentially stimula...

CRNNTL: Convolutional Recurrent Neural Network and Transfer Learning for QSAR Modeling in Organic Drug and Material Discovery.

Molecules (Basel, Switzerland)
Molecular latent representations, derived from autoencoders (AEs), have been widely used for drug or material discovery over the past couple of years. In particular, a variety of machine learning methods based on latent representations have shown exc...

Unsupervised Representation Learning for Proteochemometric Modeling.

International journal of molecular sciences
In silico protein-ligand binding prediction is an ongoing area of research in computational chemistry and machine learning based drug discovery, as an accurate predictive model could greatly reduce the time and resources necessary for the detection a...

First molecular modelling report on tri-substituted pyrazolines as phosphodiesterase 5 (PDE5) inhibitors through classical and machine learning based multi-QSAR analysis.

SAR and QSAR in environmental research
Phosphodiesterase 5 (PDE5) falls under a broad category of metallohydrolase enzymes responsible for the catalysis of the phosphodiesterase bond, and thus it can terminate the action of cyclic guanosine monophosphate (cGMP). Overexpression of this enz...

Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure-activity relationship-based artificial intelligence and molecular docking of hit compounds.

Computational biology and chemistry
Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus; however, some classes of these drugs exert side effects, including joint pain and pancreatitis. Studies suggest that these side eff...