Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set.

Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein - ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research.

Computer-aided diagnosis of keratoconus through VAE-augmented images using deep learning.

Detecting clinical keratoconus (KCN) poses a challenging and time-consuming task. During the diagnostic process, ophthalmologists are required to review demographic and clinical ophthalmic examinations in order to make an accurate diagnosis. This study aims to develop and evaluate the accuracy of deep convolutional neural network (CNN) models for the detection of keratoconus (KCN) using corneal topographic maps.

Identification of Catechins' Binding Sites in Monomeric A through Ensemble Docking and MD Simulations.

The assembly of the amyloid- peptide (A) into toxic oligomers and fibrils is associated with Alzheimer's disease and dementia. Therefore, disrupting amyloid assembly by direct targeting of the A monomeric form with small molecules or antibodies is a promising therapeutic strategy. However, given the dynamic nature of A, standard computational tools cannot be easily applied for high-throughput structure-based virtual screening in drug discovery projects.

GB-score: Minimally designed machine learning scoring function based on distance-weighted interatomic contact features.

In recent years, thanks to advances in computer hardware and dataset availability, data-driven approaches (like machine learning) have become one of the essential parts of the drug design framework to accelerate drug discovery procedures. Constructing a new scoring function, a function that can predict the binding score for a generated protein-ligand pose during docking procedure or a crystal complex, based on machine and deep learning has become an active research area in computer-aided drug design. GB-Score is a state-of-the-art machine learning-based scoring function that utilizes distance-weighted interatomic contact features, PDBbind-v2019 general set, and Gradient Boosting Trees algorithm to the binding affinity prediction.