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Ligands

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Multi-source transfer learning with Graph Neural Network for excellent modelling the bioactivities of ligands targeting orphan G protein-coupled receptors.

Mathematical biosciences and engineering : MBE
G protein-coupled receptors (GPCRs) have been the targets for more than 40% of the currently approved drugs. Although neural networks can effectively improve the accuracy of prediction with the biological activity, the result is undesirable in the li...

AlphaFill: enriching AlphaFold models with ligands and cofactors.

Nature methods
Artificial intelligence-based protein structure prediction approaches have had a transformative effect on biomolecular sciences. The predicted protein models in the AlphaFold protein structure database, however, all lack coordinates for small molecul...

Few-Shot Learning for Low-Data Drug Discovery.

Journal of chemical information and modeling
The discovery of new hits through ligand-based virtual screening in drug discovery is essentially a low-data problem, as data acquisition is both difficult and expensive. The requirement for large amounts of training data hinders the application of c...

Hybrid-Enhanced Siamese Similarity Models in Ligand-Based Virtual Screen.

Biomolecules
Information technology has become an integral aspect of the drug development process. The virtual screening process (VS) is a computational technique for screening chemical compounds in a reasonable amount of time and cost. The similarity search is o...

Deffini: A family-specific deep neural network model for structure-based virtual screening.

Computers in biology and medicine
Deep learning-based virtual screening methods have been shown to significantly improve the accuracy of traditional docking-based virtual screening methods. In this paper, we developed Deffini, a structure-based virtual screening neural network model....

A New Hybrid Neural Network Deep Learning Method for Protein-Ligand Binding Affinity Prediction and De Novo Drug Design.

International journal of molecular sciences
Accurately predicting ligand binding affinity in a virtual screening campaign is still challenging. Here, we developed hybrid neural network (HNN) machine deep learning methods, HNN-denovo and HNN-affinity, by combining the 3D-CNN (convolutional neur...

MILCDock: Machine Learning Enhanced Consensus Docking for Virtual Screening in Drug Discovery.

Journal of chemical information and modeling
Molecular docking tools are regularly used to computationally identify new molecules in virtual screening for drug discovery. However, docking tools suffer from inaccurate scoring functions with widely varying performance on different proteins. To en...

Prediction of drug-target interactions through multi-task learning.

Scientific reports
Identifying the binding between the target proteins and molecules is essential in drug discovery. The multi-task learning method has been introduced to facilitate knowledge sharing among tasks when the amount of information for each task is small. Ho...

DeepLPI: a novel deep learning-based model for protein-ligand interaction prediction for drug repurposing.

Scientific reports
The substantial cost of new drug research and development has consistently posed a huge burden for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs by ...

Computational model of the full-length TSH receptor.

eLife
(GPCR)The receptor for TSH receptor (TSHR), a G protein coupled receptor (GPCR), is of particular interest as the primary antigen in autoimmune hyperthyroidism (Graves' disease) caused by stimulating TSHR antibodies. To date, only one domain of the e...