Portfolio item number 1
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Posts by Collection
portfolio
Portfolio item number 2
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publications
Identification of a novel dual-target scaffold for 3CLpro and RdRp proteins of SARS-CoV-2 using 3D-similarity search, molecular docking, molecular dynamic and ADMET evaluation
Published in Journal of Biomolecular Structure & Dynamics, 2020
Using computational screening and molecular dynamics, we identified three promising compounds targeting SARS-CoV-2’s main protease and polymerase as potential dual inhibitors to combat COVID-19.
Recommended citation: A. Aouidate et al. (2020). "Identification of a novel dual-target scaffold for 3CLpro and RdRp proteins of SARS-CoV-2 using 3D-similarity search, molecular docking, molecular dynamics and ADMET evaluation." J Biomol Struct Dyn. 1(3).
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Exploring the chemical space of BRAF Inhibitors: A cheminformatic and Machine learning analysis
Published in Journal of Molecular Liquids, 2024
This study systematically analyzed and modeled 3,952 BRAF kinase inhibitors using cheminformatics and machine learning to reveal chemical space, scaffold diversity, and structure–activity relationships, identifying key scaffolds and activity cliff molecules to guide the design of more effective BRAF inhibitors against melanoma with acquired resistance.
Recommended citation: Adnane Aouidate. (2024). "Exploring the chemical space of BRAF Inhibitors: A cheminformatic and Machine learning analysis." Journal of Molecular Liquids. 1(1).
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Supervised Machine learning and Molecular docking modeling to Identify Potential Anti-Parkinson’s Agents
Published in Journal of Molecular Graphics and Modelling, 2025
This study used CHEMBL data to build and evaluate machine learning models with different descriptors, identifying the XGBoost model with RDkit features as the best for predicting adenosine A2A receptor inhibitors, supported by molecular docking, to aid in discovering new Anti-Parkinson’s agents.
Recommended citation: A.Ghaleb, A.Aouidate, M. Aarjane, H.Anane. (2025). "Supervised Machine learning and Molecular docking modeling to Identify Potential Anti-Parkinson’s Agents." Journal of Molecular Graphics and Modelling. 1(2).
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talks
Identification of a novel dual-target scaffold for 3CLpro and RdRp proteins of SARS-CoV-2 using 3D-similarity search, molecular docking, molecular dynamic and ADMET evaluation
Published:
Using computational screening and molecular dynamics, we identified three promising compounds targeting SARS-CoV-2’s main protease and polymerase as potential dual inhibitors to combat COVID-19.
Introduction to KNIME for Cheminformatics: Molecular Fingerprints, Machine Learning, and Pipeline Creation
Published:
I delivered a talk introducing KNIME as a powerful tool for data analysis, explained molecular fingerprints and descriptors along with their associated file formats, and demonstrated fundamental machine learning concepts. Additionally, I guided participants through the process of building machine learning models and creating automated pipelines using KNIME.
teaching
Chemical Bonding for undergraduate students of Physical and Chemical Sciences
Undergraduate course, School of Applied Sciences, Ait-Melloul, Chemistry Department, 2022
A set of exercises were used to cover concepts such as covalent bonds, Lewis structures, polarity, molecular orbital diagrams, and the VSEPR and hybridisation theories.
Organic Chemistry for undergraduate students of Physical and Chemical Sciences
BCG Undergraduate course, School of Applied Sciences, Ait-Melloul, Chemistry Department, 2023
I delivered a course on organic chemistry principles to students of Biology, Chemistry, and Geology (Spring Semester), covering the study of carbon-based compounds, their structures, functional groups, and systematic naming conventions. The course included fundamentals of hydrocarbons, particularly alkanes, and the distinction between organic and inorganic carbon compounds.
Hands-On Cheminformatics Using the KNIME Analytics Platform
Workshop, NyBerMan Bioinformatics, 2023
I delivered a talk introducing KNIME as a powerful tool for data analysis, explained molecular fingerprints and descriptors along with their associated file formats, and demonstrated fundamental machine learning concepts. Additionally, I guided participants through the process of building machine learning models and creating automated pipelines using KNIME.
Organic Chemistry for undergraduate students of Physical and Chemical Sciences
PC Undergraduate course, School of Applied Sciences, Ait-Melloul, Chemistry Department, 2023
I delivered a course about organic chemistry principles to the students of Physical and Chemical Sciences (Fall Semester), including the study of carbon-based compounds, their structures, functional groups, and systematic naming conventions. Experienced with the fundamentals of hydrocarbons, particularly alkanes, and the differentiation between organic and inorganic carbon compounds.