AIMC Topic: Molecular Structure

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Machine Learning-Assisted Molecular Structure Embedding for Accurate Prediction of Emerging Contaminant Removal by Ozonation Oxidation.

Environmental science & technology
Ozone has demonstrated high efficacy in depredating emerging contaminants (ECs) during drinking water treatment. However, traditional quantitative structure-activation relationship (QSAR) models often fall short in effectively normalizing and charact...

Evaluating Molecular Similarity Measures: Do Similarity Measures Reflect Electronic Structure Properties?

Journal of chemical information and modeling
The rapid adoption of big data, machine learning (ML), and generative artificial intelligence (AI) in chemical discovery has heightened the importance of quantifying molecular similarity. Molecular similarity, commonly assessed as the distance betwee...

Benzoyl Chloride Derivatization Coupled With Liquid Chromatography-Mass Spectrometry for the Simultaneous Quantification of Molnupiravir and Its Metabolite β-d-N-hydroxycytidine in Human Plasma.

Journal of separation science
A sensitive and efficient method for simultaneous quantifying molnupiravir and its active metabolite β-d-N-hydroxycytidine in human plasma was developed by combining chemical derivatization with liquid chromatography-tandem mass spectrometry. Through...

COX-2 Inhibitor Prediction With KNIME: A Codeless Automated Machine Learning-Based Virtual Screening Workflow.

Journal of computational chemistry
Cyclooxygenase-2 (COX-2) is an enzyme that plays a crucial role in inflammation by converting arachidonic acid into prostaglandins. The overexpression of enzyme is associated with conditions such as cancer, arthritis, and Alzheimer's disease (AD), wh...

CGPDTA: An Explainable Transfer Learning-Based Predictor With Molecule Substructure Graph for Drug-Target Binding Affinity.

Journal of computational chemistry
Identifying interactions between drugs and targets is crucial for drug discovery and development. Nevertheless, the determination of drug-target binding affinities (DTAs) through traditional experimental methods is a time-consuming process. Conventio...

MvMRL: a multi-view molecular representation learning method for molecular property prediction.

Briefings in bioinformatics
Effective molecular representation learning is very important for Artificial Intelligence-driven Drug Design because it affects the accuracy and efficiency of molecular property prediction and other molecular modeling relevant tasks. However, previou...

Machine learning framework to predict pharmacokinetic profile of small molecule drugs based on chemical structure.

Clinical and translational science
Accurate prediction of a new compound's pharmacokinetic (PK) profile is pivotal for the success of drug discovery programs. An initial assessment of PK in preclinical species and humans is typically performed through allometric scaling and mathematic...

DMFDDI: deep multimodal fusion for drug-drug interaction prediction.

Briefings in bioinformatics
Drug combination therapy has gradually become a promising treatment strategy for complex or co-existing diseases. As drug-drug interactions (DDIs) may cause unexpected adverse drug reactions, DDI prediction is an important task in pharmacology and cl...

ComABAN: refining molecular representation with the graph attention mechanism to accelerate drug discovery.

Briefings in bioinformatics
An unsolved challenge in developing molecular representation is determining an optimal method to characterize the molecular structure. Comprehension of intramolecular interactions is paramount toward achieving this goal. In this study, ComABAN, a new...

ABC-Net: a divide-and-conquer based deep learning architecture for SMILES recognition from molecular images.

Briefings in bioinformatics
Structural information for chemical compounds is often described by pictorial images in most scientific documents, which cannot be easily understood and manipulated by computers. This dilemma makes optical chemical structure recognition (OCSR) an ess...