AIMC Topic: Quantitative Structure-Activity Relationship

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First report on Quantitative Structure-Toxicity Relationship modeling approaches for the prediction of acute toxicity of various organic chemicals against rotifer species.

The Science of the total environment
Nowadays, organic chemicals are crucial components in virtually every aspect of daily life, serving as indispensable elements for modern society. The ongoing synthesis of chemicals and the various potential harmful effects on living organisms are pro...

PoseidonQ: A Free Machine Learning Platform for the Development, Analysis, and Validation of Efficient and Portable QSAR Models for Drug Discovery.

Journal of chemical information and modeling
The advent of powerful machine learning algorithms as well as the availability of high volume of pharmacological data has given new fuel to QSAR, opening new unprecedented options for deriving highly predictive models for assisting the rationale desi...

Modelling of intrinsic membrane permeability of drug molecules by explainable ML-based q-RASPR approach towards better pharmacokinetics and toxicokinetics properties.

SAR and QSAR in environmental research
Drug discovery's success lies in potent inhibition against a target and optimum pharmacokinetic and toxicokinetic properties of drug molecules. Membrane permeability is a crucial factor in determining the absorption, distribution, metabolism, and exc...

Clinical microbiology and artificial intelligence: Different applications, challenges, and future prospects.

Journal of microbiological methods
Conventional clinical microbiological techniques are enhanced by the introduction of artificial intelligence (AI). Comprehensive data processing and analysis enabled the development of curated datasets that has been effectively used in training diffe...

Molecular docking-QSAR-Kronecker-regularized least squares-based multiple machine learning for assessment and prediction of PFAS-protein binding interactions.

Journal of hazardous materials
Ubiquitous per- and poly-fluoroalkyl substances (PFAS) threaten human's health and attract worldwide attention. PFAS-mediated toxicity involves adverse effects of PFAS on proteins, and assessment of PFAS-protein binding interactions helps to explain ...

DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods.

Chemical research in toxicology
Drug-induced cardiotoxicity (DICT) is a significant challenge in drug development and public health. DICT can arise from various mechanisms; New Approach Methods (NAMs), including quantitative structure-activity relationships (QSARs), have been exten...

Binding mechanism of inhibitors to DFG-in and DFG-out P38α deciphered using multiple independent Gaussian accelerated molecular dynamics simulations and deep learning.

SAR and QSAR in environmental research
P38α has been identified as a key target for drug design to treat a wide range of diseases. In this study, multiple independent Gaussian accelerated molecular dynamics (GaMD) simulations, deep learning (DL), and the molecular mechanics generalized Bo...

Multitarget Natural Compounds for Ischemic Stroke Treatment: Integration of Deep Learning Prediction and Experimental Validation.

Journal of chemical information and modeling
Ischemic stroke's complex pathophysiology demands therapeutic approaches targeting multiple pathways simultaneously, yet current treatments remain limited. We developed an innovative drug discovery pipeline combining a deep learning approach with exp...