AI Medical Compendium Topic

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Drug Evaluation, Preclinical

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The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review.

Biosensors
Organs-on-chips (OoCs) are miniature microfluidic systems that have arguably become a class of advanced in vitro models. Deep learning, as an emerging topic in machine learning, has the ability to extract a hidden statistical relationship from the in...

Artificial intelligence assisted identification of potential tau aggregation inhibitors: ligand- and structure-based virtual screening, in silico ADME, and molecular dynamics study.

Molecular diversity
Alzheimer's disease (AD) is a severe, growing, multifactorial disorder affecting millions of people worldwide characterized by cognitive decline and neurodegeneration. The accumulation of tau protein into paired helical filaments is one of the major ...

DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein-Ligand Interaction Prediction.

Molecules (Basel, Switzerland)
The core of large-scale drug virtual screening is to select the binders accurately and efficiently with high affinity from large libraries of small molecules in which non-binders are usually dominant. The binding affinity is significantly influenced ...

Deep learning-based classification model for GPR151 activator activity prediction.

BMC bioinformatics
BACKGROUND: GPR151 is a kind of protein belonging to G protein-coupled receptor family that is closely associated with a variety of physiological and pathological processes.The potential use of GPR151 as a therapeutic target for the management of met...

Deep-Learning Terahertz Single-Cell Metabolic Viability Study.

ACS nano
Cell viability assessment is critical, yet existing assessments are not accurate enough. We report a cell viability evaluation method based on the metabolic ability of a single cell. Without culture medium, we measured the absorption of cells to tera...

Adapting physiologically-based pharmacokinetic models for machine learning applications.

Scientific reports
Both machine learning and physiologically-based pharmacokinetic models are becoming essential components of the drug development process. Integrating the predictive capabilities of physiologically-based pharmacokinetic (PBPK) models within machine le...

FIAMol-AB: A feature fusion and attention-based deep learning method for enhanced antibiotic discovery.

Computers in biology and medicine
Antibiotic resistance continues to be a growing concern for global health, accentuating the need for novel antibiotic discoveries. Traditional methodologies in this field have relied heavily on extensive experimental screening, which is often time-co...

Bioactivity predictions and virtual screening using machine learning predictive model.

Journal of biomolecular structure & dynamics
Recently, there has been significant attention on machine learning algorithms for predictive modeling. Prediction models for enzyme inhibitors are limited, and it is essential to account for chemical biases while developing them. The lack of repeatab...

AI-Driven Enhancements in Drug Screening and Optimization.

Methods in molecular biology (Clifton, N.J.)
The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, qua...

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications.

Lab on a chip
High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high...