AIMC Topic: Software

Clear Filters Showing 1601 to 1610 of 3675 articles

Deep learning based detection of intracranial aneurysms on digital subtraction angiography: A feasibility study.

The neuroradiology journal
BACKGROUND: Digital subtraction angiography is the gold standard for detecting and characterising aneurysms. Here, we assess the feasibility of commercial-grade deep learning software for the detection of intracranial aneurysms on whole-brain anterop...

Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition.

Methods (San Diego, Calif.)
In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be...

Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning.

Journal of chemical information and modeling
This work presents efforts to augment the performance of data-driven machine learning algorithms for reaction template recommendation used in computer-aided synthesis planning software. Often, machine learning models designed to perform the task of p...

SUSSOL-Using Artificial Intelligence for Greener Solvent Selection and Substitution.

Molecules (Basel, Switzerland)
Solvents come in many shapes and types. Looking for solvents for a specific application can be hard, and looking for green alternatives for currently used nonbenign solvents can be even harder. We describe a new methodology for solvent selection and ...

HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks.

Scientific reports
The growing prevalence of resistance to antibiotics motivates the search for new antibacterial agents. Antimicrobial peptides are a diverse class of well-studied membrane-active peptides which function as part of the innate host defence system, and f...

Benchmarking machine learning models on multi-centre eICU critical care dataset.

PloS one
Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and publi...

Predicting Binding from Screening Assays with Transformer Network Embeddings.

Journal of chemical information and modeling
Cheminformatics aims to assist in chemistry applications that depend on molecular interactions, structural characteristics, and functional properties. The arrival of deep learning and the abundance of easily accessible chemical data from repositories...

Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer.

JAMA network open
IMPORTANCE: Accurate identification of lymph node metastasis preoperatively and noninvasively in patients with cervical cancer can avoid unnecessary surgical intervention and benefit treatment planning.

Synthesis Success Calculator: Predicting the Rapid Synthesis of DNA Fragments with Machine Learning.

ACS synthetic biology
The synthesis and assembly of long DNA fragments has greatly accelerated synthetic biology and biotechnology research. However, long turnaround times or synthesis failures create unpredictable bottlenecks in the design-build-test-learn cycle. We deve...

Machine Estimation of Drug Melting Properties and Influence on Solubility Prediction.

Molecular pharmaceutics
There has been much recent interest in machine learning (ML) and molecular quantitative structure property relationships (QSPR). The present research evaluated modern ML-based methods implemented in commercial software (COSMOquick and Molecular Model...