AI Medical Compendium Journal:
ACS combinatorial science

Showing 1 to 10 of 17 articles

Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters.

ACS combinatorial science
Nanoclusters add an additional dimension in which to look for promising catalyst candidates, since catalytic activity of materials often changes at the nanoscale. However, the large search space of relevant atomic sites exacerbates the challenge for ...

Activity Prediction of Small Molecule Inhibitors for Antirheumatoid Arthritis Targets Based on Artificial Intelligence.

ACS combinatorial science
Rheumatoid arthritis (RA) is a chronic autoimmune disease, which is compared to "immortal cancer" in industry. Currently, SYK, BTK, and JAK are the three major targets of protein tyrosine kinase for this disease. According to existing research, marke...

Identifying Optimal Strain in Bismuth Telluride Thermoelectric Film by Combinatorial Gradient Thermal Annealing and Machine Learning.

ACS combinatorial science
The thermoelectric properties of bismuth telluride thin film (BTTF) was tuned by inducing internal strain through a combination of combinatorial gradient thermal annealing (COGTAN) and machine learning. BTTFs were synthesized via magnetron sputter co...

Profiling SARS-CoV-2 Main Protease (M) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields.

ACS combinatorial science
The current COVID-19 pandemic caused by a novel coronavirus SARS-CoV-2 urgently calls for a working therapeutic. Here, we report a computation-based workflow for efficiently selecting a subset of FDA-approved drugs that can potentially bind to the SA...

Cautionary Guidelines for Machine Learning Studies with Combinatorial Datasets.

ACS combinatorial science
Regression modeling is becoming increasingly prevalent in organic chemistry as a tool for reaction outcome prediction and mechanistic interrogation. Frequently, to acquire the requisite amount of data for such studies, researchers employ combinatoria...

Comprehensive Prediction of Molecular Recognition in a Combinatorial Chemical Space Using Machine Learning.

ACS combinatorial science
In combinatorial chemical approaches, optimizing the composition and arrangement of building blocks toward a particular function has been done using a number of methods, including high throughput molecular screening, molecular evolution, and computat...

Denoising DNA Encoded Library Screens with Sparse Learning.

ACS combinatorial science
DNA-encoded libraries (DELs) are large, pooled collections of compounds in which every library member is attached to a stretch of DNA encoding its complete synthetic history. DEL-based hit discovery involves affinity selection of the library against ...

High-Throughput and Autonomous Grazing Incidence X-ray Diffraction Mapping of Organic Combinatorial Thin-Film Library Driven by Machine Learning.

ACS combinatorial science
High-throughput X-ray diffraction (XRD) is one of the most indispensable techniques to accelerate materials research. However, the conventional XRD analysis with a large beam spot size may not best appropriate in a case for characterizing organic mat...

Optimized Multimetal Sensitized Phosphor for Enhanced Red Up-Conversion Luminescence by Machine Learning.

ACS combinatorial science
In this research, machine learning including the genetic algorithm (GA) and support vector machine (SVM) algorithm is used to solve the "low up-conversion luminescence (UCL) intensity" problem in order to find the optimal phosphor with enhanced red U...

PTML Model of ChEMBL Compounds Assays for Vitamin Derivatives.

ACS combinatorial science
Determining the biological activity of vitamin derivatives is needed given that organic synthesis of analogs of vitamins is an active field of interest for medicinal chemistry, pharmaceuticals, and food additives. Accordingly, scientists from differe...