AI Medical Compendium Journal:
Molecular diversity

Showing 31 to 40 of 70 articles

Multimodal multi-task deep neural network framework for kinase-target prediction.

Molecular diversity
Kinase plays a significant role in various disease signaling pathways. Due to the highly conserved sequence of kinase family members, understanding the selectivity profile of kinase inhibitors remains a priority for drug discovery. Previous methods f...

Machine learning prediction of empirical polarity using SMILES encoding of organic solvents.

Molecular diversity
Machine learning based statistical models have played a significant role in increasing the speed and accuracy with which the chemical and physical properties of chemical compounds can be predicted as compared to the experimental, and traditional ab i...

Matrix factorization with denoising autoencoders for prediction of drug-target interactions.

Molecular diversity
Drug-target interaction is crucial in the discovery of new drugs. Computational methods can be used to identify new drug-target interactions at low costs and with reasonable accuracy. Recent studies pay more attention to machine-learning methods, ran...

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

Molecular diversity
CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique c...

Multi-channel GCN ensembled machine learning model for molecular aqueous solubility prediction on a clean dataset.

Molecular diversity
This study constructed a new aqueous solubility dataset and a solubility regression model which was ensembled by GCN and machine learning models. Aqueous solubility is a key physiochemical property of small molecules in drug discovery. In the past fe...

Classification models and SAR analysis on HDAC1 inhibitors using machine learning methods.

Molecular diversity
Histone deacetylase (HDAC) 1, a member of the histone deacetylases family, plays a pivotal role in various tumors. In this study, we collected 7313 human HDAC1 inhibitors with bioactivities to form a dataset. Then, the dataset was divided into a trai...

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...

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.

Molecular diversity
The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted...

Computational investigation of drug bank compounds against 3C-like protease (3CL) of SARS-CoV-2 using deep learning and molecular dynamics simulation.

Molecular diversity
Blocking the main replicating enzyme, 3 Chymotrypsin-like protease (3CL) is the most promising drug development strategy against the SARS-CoV-2 virus, responsible for the current COVID-19 pandemic. In the present work, 9101 drugs obtained from the dr...

Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors.

Molecular diversity
Microtubules are receiving enormous interest in drug discovery due to the important roles they play in cellular functions. Targeting tubulin polymerization presents an excellent opportunity for the development of anti-tubulin drugs. Drug resistance a...