Yassir Boulaamane
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  • Blog
    • 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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    • Sorbent
    • Assayer
    • BioLatent
    • Learn CADD
    • QSARBoost
    • QSARBioPred
    • EnsembleBBB
    • Awesome Drug Discovery
    • ArtemisiaDB
    • CoumarinDB
  • 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
  • Projects
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QSARBioPred

Feb 20, 2024 · 1 min read
Go to Project Site

A Jupyter Notebook pipeline for building QSAR classification models for bioactivity prediction, supporting descriptor calculation, feature selection, model training, and applicability domain assessment.

Last updated on Feb 20, 2024
QSAR Machine Learning Drug Discovery Bioactivity
Yassir Boulaamane
Authors
Yassir Boulaamane
Postdoctoral Researcher
← QSARBoost Aug 9, 2025
EnsembleBBB Feb 13, 2024 →

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

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