AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Thrombin

Showing 1 to 9 of 9 articles

Clear Filters

GTM-Based QSAR Models and Their Applicability Domains.

Molecular informatics
In this paper we demonstrate that Generative Topographic Mapping (GTM), a machine learning method traditionally used for data visualisation, can be efficiently applied to QSAR modelling using probability distribution functions (PDF) computed in the l...

Multiscale simulation of thrombus growth and vessel occlusion triggered by collagen/tissue factor using a data-driven model of combinatorial platelet signalling.

Mathematical medicine and biology : a journal of the IMA
During clotting under flow, platelets bind and activate on collagen and release autocrinic factors such as ADP and thromboxane, while tissue factor (TF) on the damaged wall leads to localized thrombin generation. Towards patient-specific simulation o...

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning.

Combinatorial chemistry & high throughput screening
BACKGROUND: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors.

Probing light chain mutation effects on thrombin via molecular dynamics simulations and machine learning.

Journal of biomolecular structure & dynamics
Thrombin is a key component for chemotherapeutic and antithrombotic therapy development. As the physiologic and pathologic roles of the light chain still remain vague, here, we continue previous efforts to understand the impacts of the disease-associ...

Ensembling machine learning models to boost molecular affinity prediction.

Computational biology and chemistry
This study unites six popular machine learning approaches to enhance the prediction of a molecular binding affinity between receptors (large protein molecules) and ligands (small organic molecules). Here we examine a scheme where affinity of ligands ...

The current role of artificial intelligence in hemophilia.

Expert review of hematology
INTRODUCTION: The utilization of artificial intelligence (AI) in hemophilia is still in its early phases.

Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection.

PLoS computational biology
Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. We show that Restricted Boltzma...

Machine learning-assisted search for novel coagulants: When machine learning can be efficient even if data availability is low.

Journal of computational chemistry
Design of new drugs is a challenging process: a candidate molecule should satisfy multiple conditions to act properly and make the least side-effect-perfect candidates selectively attach to and influence only targets, leaving off-targets intact. The ...