AIMC Topic: Cheminformatics

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Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives.

International journal of molecular sciences Sep 15, 2023
Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding af...
Cholinesterase Inhibitors Daphnia Acetylcholinesterase Cheminformatics Fullerenes Cyprinidae Quantitative Structure-Activity Relationship Machine Learning Humans Water Pollutants, Chemical Animals
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Cheminformatics and machine learning approaches for repurposing anti-viral compounds against monkeypox virus thymidylate kinase.

Molecular diversity Aug 2, 2023
One of the emerging epidemic concerns is Monkeypox disease which is spreading globally. This disease is caused by the monkeypox virus (MPXV), with an increasing global incidence with an outbreak in 2022. One of the novel targets for monkeypox disease...
Antiviral Agents Drug Repositioning Molecular Docking Simulation Machine Learning Nucleoside-Phosphate Kinase Molecular Dynamics Simulation Cheminformatics
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Fragments quantum descriptors in classification of bio-accumulative compounds.

Journal of molecular graphics & modelling Aug 2, 2023
The aim of the following research is to assess the applicability of calculated quantum properties of molecular fragments as molecular descriptors in machine learning classification task. The research is based on bio-concentration and QM9-extended dat...
Cheminformatics Bioaccumulation Principal Component Analysis Databases, Factual Machine Learning
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PREFER: A New Predictive Modeling Framework for Molecular Discovery.

Journal of chemical information and modeling Jul 24, 2023
Machine-learning and deep-learning models have been extensively used in cheminformatics to predict molecular properties, to reduce the need for direct measurements, and to accelerate compound prioritization. However, different setups and frameworks a...
Cheminformatics Machine Learning
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Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science.

Journal of biomolecular structure & dynamics Jul 11, 2023
In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science a...
Cheminformatics Drug Discovery Drug Development Machine Learning Artificial Intelligence Humans
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Novel Molecular Representations Using Neumann-Cayley Orthogonal Gated Recurrent Unit.

Journal of chemical information and modeling Apr 19, 2023
Advances in deep neural networks (DNNs) have made a very powerful machine learning method available to researchers across many fields of study, including the biomedical and cheminformatics communities, where DNNs help to improve tasks such as protein...
Cheminformatics Drug Discovery Proteins Neural Networks, Computer
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Exploring proteasome inhibition using atomic weighted vector indices and machine learning approaches.

Molecular diversity Apr 5, 2023
Ubiquitin-proteasome system (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. The UPS is involved in different biological activities, such as the regulation of gene transcription and cell cycle. Several research...
Machine Learning Quantitative Structure-Activity Relationship Algorithms Cheminformatics Proteasome Inhibitors Proteasome Endopeptidase Complex
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Machine Learning Models to Predict Protein-Protein Interaction Inhibitors.

Molecules (Basel, Switzerland) Nov 17, 2022
Protein-protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and ...
Machine Learning Support Vector Machine Logistic Models Cheminformatics Algorithms
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Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast.

Journal of chemical information and modeling May 31, 2022
Deep learning has been a prevalence in computational chemistry and widely implemented in molecular property predictions. Recently, self-supervised learning (SSL), especially contrastive learning (CL), has gathered growing attention for the potential ...
Computational Chemistry Machine Learning Cheminformatics Neural Networks, Computer
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Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery.

Journal of computer-aided molecular design Mar 19, 2022
The support vector machine (SVM) algorithm is one of the most widely used machine learning (ML) methods for predicting active compounds and molecular properties. In chemoinformatics and drug discovery, SVM has been a state-of-the-art ML approach for ...
Cheminformatics Drug Discovery Support Vector Machine Algorithms
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