Yassir Boulaamane
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    • Honest QSAR Models Start With the Assay, Not the Algorithm
    • A Chemist's Glossary of Neural Network and GNN Training Terms
    • QM Cluster Models vs QM/MM for Enzyme Active Sites: What Each One Actually Models
    • BindingDB Meets PubChem: A Practical Workflow for Mining Bioactivity and Patent Chemistry
    • From Molecules to Decisions: My Takeaways from EFMC-ISMC 2026
    • Making AI Work in Discovery Chemistry: Precision, Trust, and Practical Value
    • BindingDB for Computational Drug Discovery: From Experimental Affinity Data to ML and Docking Benchmarks
    • What Can ORCA Tell Us About a Drug Molecule? A Practical Quantum Chemistry Guide
    • Peptides, Hype, and Biophysical Reality: A Computational Chemist’s Perspective
    • Beyond SMILES: The Evolving Landscape of Molecular Representations
    • What Agentic Engineering Means for Computational Drug Discovery
    • DFT Reproduction of Gold(III)-Mediated Selenocysteine Arylation: An ORCA Tutorial
    • From Prompts to Systems: How AI Engineering Changed Between 2023 and 2026
    • 3D-QSAR Explained: CoMFA vs CoMSIA
    • Bioisosteric Replacement In Silico: From Classical Rules to Data-Driven Suggestions
    • Chemical Space Is Not One Map: PCA, t-SNE, UMAP, and What They Actually Show
    • Trapped in the Basin: Why Metastable States Rule Molecular Dynamics
    • Beyond Conventional MD: Exploring Rare Molecular Events
    • Getting Real Work Out of Claude Code: A Practical Workflow Guide
    • Beyond Parameter Counts: The Shift Toward Rigorous Evaluation in Scientific AI
    • Protein Descriptors for Machine Learning in Drug Discovery
    • Graph Neural Networks and DGL: A Beginner's Guide
    • Ensemble Docking for Binding and Activity Prediction
    • Beyond Static Models: Agentic AI and Multi-Agent Systems in Drug Discovery
    • You Are Not an Impostor: Agentic Coding and the New Computational Scientist
    • Reproducible ≠ Robust: Why One UMAP Seed Isn't Enough to Trust a Split
    • Building a 3D Pharmacophore Model from PDB Data: A Free Python Workflow
    • Choosing the Right Partial Charges for Molecular Docking: AM1-BCC, PM6 and Beyond
    • Does data leakage really inflate binding-affinity GNNs? A laptop-scale reproduction
    • A History of Graph Neural Networks in Drug Discovery
    • A Practical Guide to QSAR Model Validation: Internal, Cross, and External Checks
    • Choosing the Right PDB Structure: A Systematic Guide for Docking and MD Simulations
    • Computational Strategies for Accelerating Drug Discovery: A Comprehensive Review
    • Point Cloud Classification with Graph Neural Networks Using PyTorch Geometric
    • Getting Started with Graph Neural Networks for Protein–Ligand Complexes Using DGL
    • Beyond 2D Fingerprints: Encoding Protein-Ligand Interactions for Machine Learning
    • Understanding Binding Energetics in Molecular Docking
    • Practical System Preparation Tips for Molecular Dynamics Simulations
    • Stop Using B3LYP/6-31G*: Critical Pitfalls in Modern DFT Calculations and How to Avoid Them
    • Ten Critical Pitfalls in Molecular Dynamics Simulations and Strategies for Mitigation
    • Unlocking the Undruggable: How Biotechnology Is Rewriting Drug Discovery
    • How to Use DataWarrior for Drug Discovery: Key Workflows From the Villoutreix Tutorials
    • Energy Minimization with Open Babel: Practical Guide for Ligand Preparation
    • A Quick Guide to Temperature Replica Exchange Molecular Dynamics
    • 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
    • Interpretable Machine Learning as a Key to Understanding BBB Permeability
    • A Comprehensive Guide to Scientific Writing and Publishing
    • 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
    • Molecular Dynamics-Guided Triage of Approved Drugs Against Escherichia coli Resistance Targets Nominates Capmatinib, Metergoline and Nilotinib as Candidates for Experimental Testing
    • Combined Enhanced Conformational Sampling and Protein-Ligand Dockings on CYP3A4 Improves in Silico Prediction of Drug Metabolism
    • Integrative computational repositioning of Foretinib and PF-03715455 as potential GSTP1 inhibitors for cancer chemosensitization
    • QSAR-Guided Virtual Screening and Molecular Dynamics Reveal Olaparib as a Repurposing Lead Against α-Synuclein Aggregation
    • FDA-Approved Drug Repurposing as p53 Mutants Rescue Candidates Using Structure-Based Virtual Screening and Molecular Simulations
    • Repurposing FDA-Approved Drugs as Nav1.7 Channel Modulators: An Integrated Structure-Based Virtual Screening and Molecular Dynamics Study
    • ARumenamides as Multitarget Ion Channel Modulators: Insights from Fenestration-Focused Docking, ADMET Profiling, and Molecular Dynamics
    • Odorant-Binding Protein Interactions with Herbivore-Induced Volatiles Drive Behavioral Attraction of Harmonia axyridis (Coleoptera: Coccinellidae) to Tuta Absoluta-Infested Tomato Plant
    • Repurposing DrugBank compounds as NAD-dependent deacetylase sirtuin 2 inhibitors via QSAR modelling with gradient boosting algorithms and all-atom molecular simulations
    • Anti-cancer and dual inhibitory potential of PI3K/AKT and Ras/MAPK/ERK signalling of a novel zinc (II) trinuclear complex with tetradentate schiff base ligand and azido ion in prostate adenocarcinoma: synthesis, in silico and in vitro evaluation
    • Drug Repurposing for AML: Structure-Based Virtual Screening and Molecular Simulations of FDA-Approved Compounds with Polypharmacological Potential
    • 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
  • ChemDeck
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  • Conferences
    • Combined enhanced conformational sampling and protein-ligand dockings on CYP3A4 improves in silico prediction of drug metabolism
    • 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
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Enhanced accuracy in predicting drug blood-brain barrier permeability with a Machine Learning Ensemble model

Oct 6, 2023·
Yassir Boulaamane
· 0 min read
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Last updated on Oct 6, 2023
← Antibiotic discovery with artificial intelligence for the treatment of Acinetobacter baumannii infections Oct 31, 2024
QSAR and molecular modeling studies for the discovery of natural products as multi-target-directed drugs for Parkinson's disease Oct 9, 2022 →

© 2026 Yassir Boulaamane, PhD. This work is licensed under CC BY NC ND 4.0

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