AI Medical Compendium Topic:
Models, Molecular

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EMQIT: a machine learning approach for energy based PWM matrix quality improvement.

Biology direct
BACKGROUND: Transcription factor binding affinities to DNA play a key role for the gene regulation. Learning the specificity of the mechanisms of binding TFs to DNA is important both to experimentalists and theoreticians. With the development of high...

Machine learning-enabled discovery and design of membrane-active peptides.

Bioorganic & medicinal chemistry
Antimicrobial peptides are a class of membrane-active peptides that form a critical component of innate host immunity and possess a diversity of sequence and structure. Machine learning approaches have been profitably employed to efficiently screen s...

QSAR Study of Artemisinin Analogues as Antimalarial Drugs by Neural Network and Replacement Method.

Drug research
Quantitative structure-activity relationship (QSAR) models were derived for 179 analogues of artemisinin, a potent antimalarial agent. Molecular descriptors derived solely from molecular structure were used to represent molecular structure. Utilizing...

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

Computational biology and chemistry
The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular...

Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery.

Scientific reports
Quantitative structure-activity relationship modeling using machine learning techniques constitutes a complex computational problem, where the identification of the most informative molecular descriptors for predicting a specific target property play...

Membrane protein contact and structure prediction using co-evolution in conjunction with machine learning.

PloS one
De novo membrane protein structure prediction is limited to small proteins due to the conformational search space quickly expanding with length. Long-range contacts (24+ amino acid separation)-residue positions distant in sequence, but in close proxi...

Protein-Ligand Scoring with Convolutional Neural Networks.

Journal of chemical information and modeling
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affini...

Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model.

BioMed research international
The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redunda...

Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data.

BMC bioinformatics
BACKGROUND: One goal of structural biology is to understand how a protein's 3-dimensional conformation determines its capacity to interact with potential ligands. In the case of small chemical ligands, deconstructing a static protein-ligand complex i...