AIMC Topic: Pharmaceutical Preparations

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DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation.

Nature communications
Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug disco...

Computational hybrid analysis of drug diffusion in three-dimensional domain with the aid of mass transfer and machine learning techniques.

Scientific reports
Molecular diffusion of drugs is of major importance for development and understanding drug delivery systems. Indeed, the main phenomenon which is controlling the rate of release is molecular diffusion which can be controlled via different phenomena s...

Predicting the Brain-To-Plasma Unbound Partition Coefficient of Compounds via Formula-Guided Network.

Journal of chemical information and modeling
Blood-brain barrier (BBB) permeability plays a crucial role in determining drug efficacy in the brain, with the brain-to-plasma unbound partition coefficient () recognized as a key parameter of BBB permeability in drug development. However, data are...

Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.

Chemical research in toxicology
Machine learning (ML) is increasingly valuable for predicting molecular properties and toxicity in drug discovery. However, toxicity-related end points have always been challenging to evaluate experimentally with respect to translation due to the re...

Data Exploration for Target Predictions Using Proprietary and Publicly Available Data Sets.

Chemical research in toxicology
When applying machine learning (ML) approaches for the prediction of bioactivity, it is common to collect data from different assays or sources and combine them into single data sets. However, depending on the data domains and sources from which thes...

BoostDILI: Extreme Gradient Boost-Powered Drug-Induced Liver Injury Prediction and Structural Alerts Generation.

Chemical research in toxicology
Over the past 60 years, drug-induced liver injury (DILI) has played a key role in the withdrawal of marketed drugs due to safety concerns. Early prediction of DILI is crucial for developing safer pharmaceuticals, yet current and testing methods are...

Predicting drug-gene relations via analogy tasks with word embeddings.

Scientific reports
Natural language processing is utilized in a wide range of fields, where words in text are typically transformed into feature vectors called embeddings. BioConceptVec is a specific example of embeddings tailored for biology, trained on approximately ...

Prediction of plasma concentration-time profiles in mice using deep neural network integrated with pharmacokinetic models.

International journal of pharmaceutics
Quantitative structure-activity relationship (QSAR) methods have emerged as powerful tools to streamline non-clinical pharmacokinetic (PK) studies, with extensive evidence demonstrating their potential to predict key in vivo PK parameters such as cle...

Rapid and sensitive detection of pharmaceutical pollutants in aquaculture by aluminum foil substrate based SERS method combined with deep learning algorithm.

Analytica chimica acta
BACKGROUND: Pharmaceutical residual such as antibiotics and disinfectants in aquaculture wastewater have significant potential risks for environment and human health. Surface enhanced Raman spectroscopy (SERS) has been widely used for the detection o...

Improved QSAR methods for predicting drug properties utilizing topological indices and machine learning models.

The European physical journal. E, Soft matter
This research investigates the anticipated physicochemical and topological properties of compounds such as drug complexity (C), molecular weight (MW), and topological polar surface area (TPSA) using quantitative structure-activity relationship (QSAR)...