AIMC Topic: Molecular Docking Simulation

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Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4.

Journal of computer-aided molecular design
Modern molecular docking comprises the prediction of pose and affinity. Prediction of docking poses is required for affinity prediction when three-dimensional coordinates of the ligand have not been provided. However, a large number of feature engine...

PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments.

Journal of computational chemistry
Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better te...

Design of SARS-CoV-2 Mpro, PLpro dual-target inhibitors based on deep reinforcement learning and virtual screening.

Future medicinal chemistry
Since December 2019, SARS-CoV-2 has continued to spread rapidly around the world. The effective drugs may provide a long-term strategy to combat this virus. The main protease (Mpro) and papain-like protease (PLpro) are two important targets for the ...

Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking.

Nature protocols
With the recent explosion of chemical libraries beyond a billion molecules, more efficient virtual screening approaches are needed. The Deep Docking (DD) platform enables up to 100-fold acceleration of structure-based virtual screening by docking onl...

Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Computational drug design relies on the calculation of binding strength between two biological counterparts especially a chemical compound, i.e., a ligand, and a protein. Predicting the affinity of protein-ligand binding with reasonable accuracy is c...

Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem.

Scientific reports
Despite considerable advances obtained by applying machine learning approaches in protein-ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a so...

Harnessing protein folding neural networks for peptide-protein docking.

Nature communications
Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been develop...

Artificial intelligence-based identification of octenidine as a Bcl-xL inhibitor.

Biochemical and biophysical research communications
Apoptosis plays an essential role in maintaining cellular homeostasis and preventing cancer progression. Bcl-xL, an anti-apoptotic protein, is an important modulator of the mitochondrial apoptosis pathway and is a promising target for anticancer ther...

Protein embeddings and deep learning predict binding residues for various ligand classes.

Scientific reports
One important aspect of protein function is the binding of proteins to ligands, including small molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of experimental progress many binding sites remain obscure. Here, we propose...

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...