Parkinson's severity diagnosis explainable model based on 3D multi-head attention residual network.

The severity evaluation of Parkinson's disease (PD) is of great significance for the treatment of PD. However, existing methods either have limitations based on prior knowledge or are invasive methods. To propose a more generalized severity evaluation model, this paper proposes an explainable 3D multi-head attention residual convolution network.

Early Diagnosis of Mild Cognitive Impairment due to Alzheimer's Disease Using a Composite of MemTrax and Blood Biomarkers.

Background: Accessible measurements for the early detection of mild cognitive impairment (MCI) due to Alzheimer's disease (AD) are urgently needed to address the increasing prevalence of AD.

Objective: To determine the benefits of a composite MemTrax Memory Test and AD-related blood biomarker assessment for the early detection of MCI-AD in non-specialty clinics.

Methods: The MemTrax Memory Test and Montreal Cognitive Assessment were administered to 99 healthy seniors with normal cognitive function and 101 patients with MCI-AD; clinical manifestation and peripheral blood samples were collected.

Geometric Interaction Graph Neural Network for Predicting Protein-Ligand Binding Affinities from 3D Structures (GIGN).

Predicting protein-ligand binding affinities (PLAs) is a core problem in drug discovery. Recent advances have shown great potential in applying machine learning (ML) for PLA prediction. However, most of them omit the 3D structures of complexes and physical interactions between proteins and ligands, which are considered essential to understanding the binding mechanism.

Learning size-adaptive molecular substructures for explainable drug-drug interaction prediction by substructure-aware graph neural network.

Drug-drug interactions (DDIs) can trigger unexpected pharmacological effects on the body, and the causal mechanisms are often unknown. Graph neural networks (GNNs) have been developed to better understand DDIs. However, identifying key substructures that contribute most to the DDI prediction is a challenge for GNNs.

3DGT-DDI: 3D graph and text based neural network for drug-drug interaction prediction.

Motivation: Drug-drug interactions (DDIs) occur during the combination of drugs. Identifying potential DDI helps us to study the mechanism behind the combination medication or adverse reactions so as to avoid the side effects. Although many artificial intelligence methods predict and mine potential DDI, they ignore the 3D structure information of drug molecules and do not fully consider the contribution of molecular substructure in DDI.

MGraphDTA: deep multiscale graph neural network for explainable drug-target binding affinity prediction.

Predicting drug-target affinity (DTA) is beneficial for accelerating drug discovery. Graph neural networks (GNNs) have been widely used in DTA prediction. However, existing shallow GNNs are insufficient to capture the global structure of compounds.

Mol2Context-vec: learning molecular representation from context awareness for drug discovery.

With the rapid development of proteomics and the rapid increase of target molecules for drug action, computer-aided drug design (CADD) has become a basic task in drug discovery. One of the key challenges in CADD is molecular representation. High-quality molecular expression with chemical intuition helps to promote many boundary problems of drug discovery.

Graph convolutional network approach to investigate potential selective Limk1 inhibitors.

Since the Limk1 is a promising drug target and few inhibitors with good Limk1/ROCK2 selectivity have been reported, discovering potential and selective Limk1 inhibitors with novel scaffolds is becoming an urgent need to develop new treatments for the related diseases. Here, we utilized molecular docking to screen potential compounds of Limk1 from Traditional Chinese Medicine (TCM) database. Meanwhile, we performed a three-dimensional graph convolutional network (3DGCN), based on 3D molecular graph, to predict the inhibitory activity of Limk1 and ROCK2.

In Silico Investigation of Traditional Chinese Medicine for Potential Lead Compounds as SPG7 Inhibitors against Coronary Artery Disease.

Coronary artery disease (CAD) is the most common cause of heart attack and the leading cause of mortality in the world. It is associated with mitochondrial dysfunction and increased level of reactive oxygen species production. According to the Ottawa Heart Genomics Study genome-wide association study, a recent research identified that Q688 spastic paraplegia 7 (SPG7) variant is associated with CAD as it bypasses the regulation of tyrosine phosphorylation of AFG3L2 and enhances the processing and maturation of SPG7 protein.

Iron depletion strategy for targeted cancer therapy: utilizing the dual roles of neutrophil gelatinase-associated lipocalin protein.

Decreasing iron uptake and increasing iron efflux may result in cell death by oxidative inactivation of vital enzymes. Applying the dual function of neutrophil gelatinase-associated lipocalin (NGAL) could achieve the goal of iron depletion in the cancer cells. Tyr106, Lys125 or Lys134 was the key binding site for NGAL protein to sequester iron-chelating siderophores.

Susceptible gene of stasis-stagnation constitution from genome-wide association study related to cardiovascular disturbance and possible regulated traditional Chinese medicine.

Background: This study identified susceptible loci related to the Yu-Zhi (YZ) constitution, which indicates stasis-stagnation, found in a genome-wide association study (GWAS) in patients with type 2 diabetes and possible regulated traditional Chinese medicine (TCM) using docking and molecular dynamics (MD) simulation.

Methods: Non-aboriginal Taiwanese with type 2 diabetes were recruited. Components of the YZ constitution were assessed by a self-reported questionnaire.

Insight into molecular dynamics simulation of BRAF(V600E) and potent novel inhibitors for malignant melanoma.

BRAF inhibitors have changed the standard therapeutic protocol for advanced or metastatic melanoma which harbored notorious BRAF(V600E) single mutation. However, drug resistance to BRAF inhibitors happens just like other cancer treatment. In this study, we constructed the ideal BRAF(V600E)-modeled structure through homology modeling and introduced the method of structure-based docking or virtual screening from the large compound database.

Identification of tyrosinase inhibitors from traditional Chinese medicines for the management of hyperpigmentation.

The inhibition of tyrosinase is the most effective method to decrease melanin synthesis during the process of pigmentation. We aimed to find compounds from traditional Chinese medicines (TCM) that are more effective than the most commonly used tyrosinase inhibitor, arbutin. First, we employed homology modeling to construct a tyrosinase-modeled structure, and structure-based virtual screening to screen from 61,000 TCM compounds.

Beware of docking!

Docking is now routine in virtual screening or lead optimization for drug screening and design. The number of papers related to docking has dramatically increased over the past decade. However, there are many issues to consider when undertaking a docking study.

Chalcone derivatives inhibit human platelet aggregation and inhibit growth in human bladder cancer cells.

In an effort to develop potent cyclooxygenase-1 (COX-1) inhibitors used as anticancer agent, a series of 2',5'-dimethoxychalcones was screened to evaluate their antiplatelet effect on human washed platelets suspension. Compound 2 exhibited potent inhibition of human washed platelet aggregation induced by collagen, significantly inhibited collagen- and arachidonic acid-induced thromboxane B2 release, and revealed inhibitory effect on COX-1 activity. Molecular docking studies showed that 1, 2, and 4 were bound in the active site of COX-1.

Antiplatelet effect and selective binding to cyclooxygenase by molecular docking analysis of 3-alkylaminopropoxy-9,10-anthraquinone derivatives.

In an effort to develop potent cytotoxic inhibitors of cyclooxygenase (COX), a series of cytotoxic 3-alkylaminopropoxy-9,10-anthraquinone derivatives was screened to evaluate their antiplatelet effect on washed rabbit platelets and human platelet-rich plasma (PRP). Thrombin, arachidonic acid (AA), collagen, and platelet-activating factor (PAF) induced platelet aggregations were potently inhibited by compounds 1, 2, and 3 (each at 300 microM). Of the compounds tested in human PRP, compounds 1, 8, and 10 showed significant inhibition of primary and secondary aggregation induced by epinephrine and had a weak inhibitory effect on cyclooxygenase-1 (COX-1).

Novel selective inhibitors of hydroxyxanthone derivatives for human cyclooxygenase-2.

Aim: To screen the selective inhibitors for human cyclooxygenase-2 ((h)COX-2) utilizing molecular simulation.

Methods: Eight xanthone derivatives, compounds A-H, were employed by the structure-based research methodology. Resveratrol and NS-398 were selected as the control compounds for COX-1 and COX-2, respectively.