Yassir Boulaamane
  • Bio
  • Publications
  • Conferences
  • Blog
  • CV
  • Projects
  • Projects
    • Awesome Drug Discovery
    • QSARBoost
    • VinaScreen
    • QSARBioPred
    • EnsembleBBB
    • ArtemisiaDB
    • CoumarinDB
    • GMXPlotter
  • Blog
    • A Quick Guide to Temperature Replica Exchange Molecular Dynamics (T-REMD)
    • Principal Component Analysis and Free Energy Landscape Mapping Using GROMACS
    • Validating Molecular Docking Poses with DFT: A Quick Guide
    • AI + Chemistry: Building Drug Discovery Pipelines with Free Tools
    • Step-by-Step MD Simulation of a Protein–Ligand Complex with GROMACS
    • Molecular Simulation in Drug Discovery: A Strategic Guide to Core Methods
    • Computational Drug Repurposing with Multiscale Interactomes
    • How to Validate AlphaFold Structures
    • A Comprehensive Guide to Scientific Writing and Publishing
    • Interpretable Machine Learning as a Key to Understanding BBB Permeability
    • How to build and validate 3D-QSAR models - Insights from Xu et al., 2020
    • A Comprehensive Guide to Hybrid Assembly Pipeline for Genomic Sequencing
    • Data-Driven Chemistry: How Data Science Empowers Drug Discovery
    • Using PaDELPy to Generate Molecular Fingerprints for Machine Learning-Based QSAR
    • Understanding Molecular Dynamics Simulations
    • How Long Should Molecular Dynamics Simulations Run? A Practical Guide
    • Supervised vs. Unsupervised Methods in Machine Learning
    • Chemical Databases Every ML Scientist Should Know for Drug Discovery
    • How to Perform Data Curation and Classify Bioactivity Data on ChEMBL Database
  • Publications
    • Computational screening of natural products as tryptophan 2,3-dioxygenase inhibitors: Insights from CNN-based QSAR, molecular docking, ADMET, and molecular dynamics simulations
    • Virtual Screening and Identification of Natural Molecules as Promising Quorum Sensing Inhibitors against Pseudomonas aeruginosa
    • Computational Investigation of Phytochemicals from Aloysia citriodora as Drug Targets for Parkinson’s Disease-Associated Proteins
    • Metal and Metal Oxide Nanoparticles: Computational Analysis of Their Interactions and Antibacterial Activities Against Pseudomonas aeruginosa
    • Computational exploration of acefylline derivatives as MAO-B inhibitors for Parkinson’s disease: insights from molecular docking, DFT, ADMET, and molecular dynamics approaches
    • In silico Discovery of Dual Ligands Targeting MAO-B and AA2AR from African Natural Products Using Pharmacophore Modelling, Molecular Docking, and Molecular Dynamics Simulations
    • Identification of Natural Inhibitors of SARS-CoV-2 Main Protease (Mpro) via Structure-Based Virtual Screening and Molecular Dynamics Simulations
    • Dendrobium nobile alkaloids modulate calcium dysregulation and neuroinflammation in Alzheimer's disease: A bioinformatic analysis
    • Antibiotic discovery with artificial intelligence for the treatment of Acinetobacter baumannii infections
    • Identification of novel dual acting ligands targeting the adenosine A2A and serotonin 5-HT1A receptors
    • Exploring natural products as multi-target-directed drugs for Parkinson’s disease: an in-silico approach integrating QSAR, pharmacophore modeling, and molecular dynamics simulations
    • Probing the molecular mechanisms of α-synuclein inhibitors unveils promising natural candidates through machine-learning QSAR, pharmacophore modeling, and molecular dynamics simulations
    • Chemical library design, QSAR modeling and molecular dynamics simulations of naturally occurring coumarins as dual inhibitors of MAO-B and AChE
    • Insights into the Structure-Activity Relationship of Alkynyl-Coumarinyl Ethers as Selective MAO-B Inhibitors Using Molecular Docking
    • β-amino carbonyl derivatives: Synthesis, Molecular Docking, ADMET, Molecular Dynamic and Herbicidal studies
    • In silico studies of natural product-like caffeine derivatives as potential MAO-B inhibitors/AA2AR antagonists for the treatment of Parkinson's disease
    • Structural exploration of selected C6 and C7-substituted coumarin isomers as selective MAO-B inhibitors
  • Conferences
    • Computational screening of natural products as tryptophan 2,3-dioxygenase inhibitors: Insights from machine learning QSAR, molecular docking, ADMET, and molecular dynamics simulations
    • Antibiotic discovery with artificial intelligence for the treatment of Acinetobacter baumannii infections
    • Enhanced accuracy in predicting drug blood-brain barrier permeability with a Machine Learning Ensemble model
    • QSAR and molecular modeling studies for the discovery of natural products as multi-target-directed drugs for Parkinson's disease
    • Computational studies of African Natural Products Databases to identify natural dual-target-directed antiparkinsonian drugs
    • Machine Learning model to predict potential Monoamine Oxidase B inhibitors from Cannabis Compound Database
    • Docking-based virtual screening and ADME evaluation of caffeine-based phytochemicals as inhibitors of Monoamine Oxidase B
  • Projects
  • CV
  • Teaching
    • Learn JavaScript
    • Learn Python

Machine Learning model to predict potential Monoamine Oxidase B inhibitors from Cannabis Compound Database

Dec 27, 2021·
Yassir Boulaamane
· 0 min read
PDF
Last updated on Dec 27, 2021
← Computational studies of African Natural Products Databases to identify natural dual-target-directed antiparkinsonian drugs Dec 27, 2021
Docking-based virtual screening and ADME evaluation of caffeine-based phytochemicals as inhibitors of Monoamine Oxidase B Jun 23, 2021 →

© 2025 Yassir Boulaamane, PhD. This work is licensed under CC BY NC ND 4.0

Published with Hugo Blox Builder — the free, open source website builder that empowers creators.