Hemophilia

Identification of therapeutics against PfPK6 protein of Plasmodium falciparum: Structure and Deep Learning approach.

The Plasmodium falciparum Protein Kinase 6 (PfPK6) is a serine/threonine protein kinase categorized under the CMGC group, displaying both cyclin-dependent kinases (CDKs) and mitogen-activated protein kinases (MAPKs) activity. Previous research has indicated that PfPK6 is expressed during the trophozoite and schizont stages of the Plasmodium falciparum asexual blood stage. Unlike typical cyclin-dep...

Development and validation of a CT-based radiomics machine learning model for differentiating immune-related interstitial pneumonia.

INTRODUCTION: Immune checkpoint inhibitor-related interstitial pneumonia (CIP) poses a diagnostic challenge due to its radiographic similarity to other pneumonias. We developed a non-invasive model using CT imaging to differentiate CIP from other pneumonias (OTP).

AI-Driven Design and Development of Nontoxic DYRK1A Inhibitors.

Dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) is implicated in several human diseases, including DYRK1A syndrome, cancer, and...

Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on the Chemical Structure.

Antibody-drug conjugates (ADCs) are promising cancer therapeutics, but optimizing their cytotoxic payloads remains challenging. We present DumplingGNN...

Deep transfer learning approach for automated cell death classification reveals novel ferroptosis-inducing agents in subsets of B-ALL.

Ferroptosis is a recently described type of regulated necrotic cell death whose induction has anti-cancer therapeutic potential, especially in hematol...

Protein Profiles Predict Treatment Responses to the PI3K Inhibitor Umbralisib in Patients with Chronic Lymphocytic Leukemia.

PURPOSE: The management of chronic lymphocytic leukemia (CLL) has significantly improved with targeted therapies. However, many patients experience a ...

Transforming Healthcare: The Role of Artificial Intelligence.

The integration of artificial intelligence (AI) into healthcare is revolutionising the industry by enhancing diagnostic accuracy, personalising treatm...

Single-cell mitochondrial morphomics reveals cellular heterogeneity and predicts complex I, III, and ATP synthase Inhibition responses.

Mitochondrial heterogeneity drives diverse cellular responses in neurodegenerative diseases, complicating the evaluation of mitochondrial dysfunction....

Computational Analysis using Multi-ligand Simultaneous Docking of Withaferin A and Garcinol Reveals Enhanced BCL-2 and AKT-1 Inhibition

Developing an effective medicine to combat cancer and elusive stem cells is crucial in the current scenario. Withaferin A and Garcinol, important ph...

Active Learning FEP: Impact on Performance of AL Protocol and Chemical Diversity.

Active learning using models built on binding potency predictions from free energy perturbation (AL-FEP) has been proposed as a method for generating ...

Enhancing PI3Kγ inhibitor discovery: a machine learning-based virtual screening approach integrating pharmacophores, docking, and molecular descriptors.

PI3Kγ is a lipid kinase that is expressed primarily in leukocytes and plays a significant role in tumors, inflammation, and autoimmune diseases. Conse...

In Silico Prediction and Validation of LmGt Inhibitors Using QSAR and Molecular Docking Approaches

Leishmaniasis caused by Leishmania mexicana relies on Leishmania mexicana gluscose transporter (LmGT) receptors, which play an important role in glu...

A deep learning and molecular modeling approach to repurposing Cangrelor as a potential inhibitor of Nipah virus.

Deforestation, urbanization, and climate change have significantly increased the risk of zoonotic diseases. Nipah virus (NiV) of Paramyxoviridae famil...

Identification of predictive subphenotypes for clinical outcomes using real world data and machine learning.

Predicting treatment response is an important problem in real-world applications, where the heterogeneity of the treatment response remains a signific...

Discovery of novel GluN1/GluN3A NMDA receptor inhibitors using a deep learning-based method.

Ligand-based drug discovery methods typically utilize pharmacophore similarities among molecules to screen for potential active compounds. Among these...

Blood-brain barrier crossing biopolymer targeting c-Myc and anti-PD-1 activate primary brain lymphoma immunity: Artificial intelligence analysis.

Primary Central Nervous System Lymphoma is an aggressive central nervous system neoplasm with poor response to pharmacological treatment, partially du...

Clinical characteristics, outcomes, and predictive modeling of patients diagnosed with immune checkpoint inhibitor therapy-related pneumonitis.

PURPOSE: The aim of this study is to better characterize the clinical characteristics and outcomes of patients diagnosed with Immune checkpoint Inhibi...

Discovery, Biological Evaluation and Binding Mode Investigation of Novel Butyrylcholinesterase Inhibitors Through Hybrid Virtual Screening.

Butyrylcholinesterase (BChE), plays a critical role in alleviating the symptoms of Alzheimer's disease (AD) by regulating acetylcholine levels, emergi...

Advancing promiscuous aggregating inhibitor analysis with intelligent machine learning classification.

Small molecules have been playing a crucial role in drug discovery; however, some exhibit nonspecific inhibitory effects during hit screening due to t...

Discovery of hematopoietic progenitor kinase 1 inhibitors using machine learning-based screening and free energy perturbation.

Hematopoietic progenitor kinase 1 (HPK1) is a key negative regulator of T-cell receptor (TCR) signaling and a promising target for cancer immunotherap...