AIMC Topic: Molecular Docking Simulation

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Perceiver CPI: a nested cross-attention network for compound-protein interaction prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Compound-protein interaction (CPI) plays an essential role in drug discovery and is performed via expensive molecular docking simulations. Many artificial intelligence-based approaches have been proposed in this regard. Recently, two type...

Exploring Anti-osteoporosis Medicinal Herbs using Cheminformatics and Deep Learning Approaches.

Combinatorial chemistry & high throughput screening
BACKGROUND: Osteoporosis is a prevalent disease for the aged population. Chinese herbderived natural compounds have anti-osteoporosis effects. Due to the complexity of chemical ingredients and natural products, it is necessary to develop a high-throu...

A geometric deep learning framework for drug repositioning over heterogeneous information networks.

Briefings in bioinformatics
Drug repositioning (DR) is a promising strategy to discover new indicators of approved drugs with artificial intelligence techniques, thus improving traditional drug discovery and development. However, most of DR computational methods fall short of t...

Combined docking and machine learning identify key molecular determinants of ligand pharmacological activity on β2 adrenoceptor.

Pharmacology research & perspectives
G protein-coupled receptors (GPCRs) are valuable therapeutic targets for many diseases. A central question of GPCR drug discovery is to understand what determines the agonism or antagonism of ligands that bind them. Ligands exert their action via the...

A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia.

Briefings in bioinformatics
Nowadays, the complexity of disease mechanisms and the inadequacy of single-target therapies in restoring the biological system have inevitably instigated the strategy of multi-target therapeutics with the analysis of each target individually. Howeve...

D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19.

Briefings in bioinformatics
Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening ca...

Potential SARS-CoV-2 nonstructural proteins inhibitors: drugs repurposing with drug-target networks and deep learning.

Frontiers in bioscience (Landmark edition)
BACKGROUND: In the current COVID-19 pandemic, with an absence of approved drugs and widely accessible vaccines, repurposing existing drugs is vital to quickly developing a treatment for the disease.

A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2.

Briefings in bioinformatics
The outbreak of COVID-19 caused by SARS-coronavirus (CoV)-2 has made millions of deaths since 2019. Although a variety of computational methods have been proposed to repurpose drugs for treating SARS-CoV-2 infections, it is still a challenging task f...

sAMP-PFPDeep: Improving accuracy of short antimicrobial peptides prediction using three different sequence encodings and deep neural networks.

Briefings in bioinformatics
Short antimicrobial peptides (sAMPs) belong to a significant repertoire of antimicrobial agents and are known to possess enhanced antimicrobial activity, higher stability and less toxicity to human cells, as well as less complex than other large biol...

Ultrahigh Throughput Protein-Ligand Docking with Deep Learning.

Methods in molecular biology (Clifton, N.J.)
Ultrahigh-throughput virtual screening (uHTVS) is an emerging field linking together classical docking techniques with high-throughput AI methods. We outline mechanistic docking models' goals and successes. We present different AI accelerated workflo...