Hemophilia

How can artificial intelligence help rehabilitation programs for people with hemophilia in the coming decades?

Probing the molecular mechanisms of α-synuclein inhibitors unveils promising natural candidates through machine-learning QSAR, pharmacophore modeling, and molecular dynamics simulations.

Parkinson's disease is characterized by a multifactorial nature that is linked to different pathways. Among them, the abnormal deposition and accumulation of α-synuclein fibrils is considered a neuropathological hallmark of Parkinson's disease. Several synthetic and natural compounds have been tested for their potency to inhibit the aggregation of α-synuclein. However, the molecular mechanisms res...

High accuracy epidermal growth factor receptor mutation prediction via histopathological deep learning.

BACKGROUND: The detection of epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer is critical for tyrosine ki...

Outcome-Supervised Deep Learning on Pathologic Whole Slide Images for Survival Prediction of Immunotherapy in Patients with Non-Small Cell Lung Cancer.

Although programmed death-(ligand) 1 (PD-(L)1) inhibitors are marked by durable efficacy in patients with non-small cell lung cancer (NSCLC), approxim...

A deep learning and docking simulation-based virtual screening strategy enables the rapid identification of HIF-1α pathway activators from a marine natural product database.

Artificial Intelligence is hailed as a cutting-edge technology for accelerating drug discovery efforts, and our goal was to validate its potential in ...

Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (M) using molecular docking and deep learning methods.

Highly transmissive and rapidly evolving Coronavirus disease-2019 (COVID-19), a viral disease caused by severe acute respiratory syndrome coronavirus ...

MEMMAL: A tool for expanding large-scale mechanistic models with machine learned associations and big datasets.

Computational models that can explain and predict complex sub-cellular, cellular, and tissue-level drug response mechanisms could speed drug discovery...

Establishment of extensive artificial intelligence models for kinase inhibitor prediction: Identification of novel PDGFRB inhibitors.

Identifying hit compounds is an important step in drug development. Unfortunately, this process continues to be a challenging task. Several machine le...

Strategy toward Kinase-Selective Drug Discovery.

Kinase drug selectivity is the ground challenge in cancer research. Due to the structurally similar kinase drug pockets, off-target inhibitor toxicity...

Development, validation, and evaluation of a deep learning model to screen cyclin-dependent kinase 12 inhibitors in cancers.

Deep learning-based in silico alternatives have been demonstrated to be of significant importance in the acceleration of the drug discovery process an...

A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer.

BACKGROUND: The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a first-line therapy for non-small cell lung cancer (NSC...

Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer.

AIM: To develop and validate a nomogram model that combines computed tomography (CT)-based radiological factors extracted from deep-learning and clini...

Multiple machine learning methods aided virtual screening of Na 1.5 inhibitors.

Na 1.5 sodium channels contribute to the generation of the rapid upstroke of the myocardial action potential and thereby play a central role in the ex...

Water irradiation devoid pulses enhance the sensitivity of H,H nuclear Overhauser effects.

The nuclear Overhauser effect (NOE) is one of NMR spectroscopy's most important and versatile parameters. NOE is routinely utilized to determine the s...

Molecular modeling of C1-inhibitor as SARS-CoV-2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study.

The expeditious transmission of the severe acute respiratory coronavirus 2 (SARS-CoV-2), a strain of COVID-19, crumbled the global economic strength a...

Deep learning identifies morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide images.

Homologous recombination DNA-repair deficiency (HRD) is becoming a well-recognized marker of platinum salt and polyADP-ribose polymerase inhibitor che...

Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor.

The retrieval of hit/lead compounds with novel scaffolds during early drug development is an important but challenging task. Various generative models...

Microfluidics guided by deep learning for cancer immunotherapy screening.

Immunocyte infiltration and cytotoxicity play critical roles in both inflammation and immunotherapy. However, current cancer immunotherapy screening m...

Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search.

Designing highly selective molecules for a drug target protein is a challenging task in drug discovery. This task can be regarded as a multiobjective ...

Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen.

SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalent...