Hemophilia

Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure-activity relationship-based artificial intelligence and molecular docking of hit compounds.

Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus; however, some classes of these drugs exert side effects, including joint pain and pancreatitis. Studies suggest that these side effects might be related to secondary inhibition of DPP-8 and DPP-9. In this study, we identified DPP-4-inhibitor hit compounds selective against DPP-8 a...

Predicting response to immunotherapy plus chemotherapy in patients with esophageal squamous cell carcinoma using non-invasive Radiomic biomarkers.

OBJECTIVES: To develop and validate a radiomics model for evaluating treatment response to immune-checkpoint inhibitor plus chemotherapy (ICI + CT) in patients with advanced esophageal squamous cell carcinoma (ESCC).

De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations.

The main protease of SARS-CoV-2 is a critical target for the design and development of antiviral drugs. 2.5 M compounds were used in this study to tra...

Deep learning-driven scaffold hopping in the discovery of Akt kinase inhibitors.

Scaffold hopping has been widely used in drug discovery and is a topic of high interest. Here a deep conditional transformer neural network, SyntaLink...

Stem-cell based, machine learning approach for optimizing natural killer cell-based personalized immunotherapy for high-grade ovarian cancer.

Advanced high-grade serous ovarian cancer continues to be a therapeutic challenge for those affected using the current therapeutic interventions. Ther...

Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches.

The protein kinase family contains many promising drug targets. Many kinase inhibitors target the ATP-binding pocket, leading to approved drugs in pas...

An pipeline for the discovery of multitarget ligands: A case study for epi-polypharmacology based on DNMT1/HDAC2 inhibition.

The search for novel therapeutic compounds remains an overwhelming task owing to the time-consuming and expensive nature of the drug development proce...

Ensemble based machine learning approach for prediction of glioma and multi-grade classification.

Glioma is the most pernicious cancer of the nervous system, with histological grade influencing the survival of patients. Despite many studies on the ...

Domain Adaptation-Based Deep Learning for Automated Tumor Cell (TC) Scoring and Survival Analysis on PD-L1 Stained Tissue Images.

We report the ability of two deep learning-based decision systems to stratify non-small cell lung cancer (NSCLC) patients treated with checkpoint inhi...

CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes.

The vast majority of approved drugs are metabolized by the five major cytochrome P450 (CYP) isozymes, 1A2, 2C9, 2C19, 2D6 and 3A4. Inhibition of CYP i...

Benzo[]thiazole-2-thiol bearing 2-oxo-2-substituted-phenylethan-1-yl as potent selective quorum sensing inhibitors of Gram-negative bacteria.

Quorum sensing is a well-known term for describing bacterial cell-cell communication. Bacteria use quorum sensing pathways to respond to external fact...

Using machine learning and big data to explore the drug resistance landscape in HIV.

Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to rev...

Artificial Inteligence-Based Decision for the Prediction of Cardioembolism in Patients with Chagas Disease and Ischemic Stroke.

BACKGROUND: Chagas disease (CD) and ischemic stroke (IS) have a close, but poorly understood, association. There is paucity of evidence on the ideal s...

Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses.

Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the ...

Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines.

Computational models for drug sensitivity prediction have the potential to significantly improve personalized cancer medicine. Drug sensitivity assays...

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease pro...

Detection of subclinical rheumatic heart disease in children using a deep learning algorithm on digital stethoscope: a study protocol.

INTRODUCTION: Rheumatic heart diseases (RHDs) contribute significant morbidity and mortality globally. To reduce the burden of RHD, timely initiation ...

Predicting mutant outcome by combining deep mutational scanning and machine learning.

Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experi...

A model of modified -iodobenzylguanidine conjugated gold nanoparticles for neuroblastoma treatment.

Iodine-131 -iodobenzylguanidine (I-IBG) has been utilized as a standard treatment to minimize adverse side effects by targeting therapies to bind to t...

Identification of kinase inhibitors that rule out the CYP27B1-mediated activation of vitamin D: an integrated machine learning and structure-based drug designing approach.

CYP27B1, a cytochrome P450-containing hydroxylase enzyme, converts vitamin D precursor calcidiol (25-hydroxycholecalciferol) to its active form calcit...