<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>InSilicoBench: tutorials and notes</title><description>Hands-on, version-pinned tutorials and free browser tools for molecular docking, GROMACS molecular dynamics, MM-PBSA binding free energies, QSAR and cheminformatics.</description><link>https://insilicobench.com/</link><language>en</language><item><title>Honest QSAR Models Start With the Assay, Not the Algorithm</title><link>https://insilicobench.com/notes/honest-qsar-models-start-with-the-assay-not-the-algorithm/</link><guid isPermaLink="true">https://insilicobench.com/notes/honest-qsar-models-start-with-the-assay-not-the-algorithm/</guid><description>Notes on an Optibrium webinar: curate the assay, match the method to the noise, score the test set once, plot instead of R², and respect the applicability domain.</description><pubDate>Sun, 25 Oct 2026 00:00:00 GMT</pubDate><category>QSAR</category><category>ADMET</category><category>model-validation</category><category>cytochrome-P450</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>Redocking with AutoDock Vina 1.2 and Meeko: a reproducible 1HSG walkthrough</title><link>https://insilicobench.com/tutorials/autodock-vina-meeko-docking/</link><guid isPermaLink="true">https://insilicobench.com/tutorials/autodock-vina-meeko-docking/</guid><description>Prepare receptor and ligand with Meeko, define the box, dock with Vina 1.2.7, and validate by redocking indinavir into HIV-1 protease (PDB 1HSG).</description><pubDate>Fri, 23 Oct 2026 00:00:00 GMT</pubDate><category>docking</category><category>autodock-vina</category><category>meeko</category><category>rdkit</category><category>validation</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>Choosing GROMACS .mdp parameters: a stage-by-stage guide for GROMACS 2026</title><link>https://insilicobench.com/tutorials/choosing-gromacs-mdp-parameters/</link><guid isPermaLink="true">https://insilicobench.com/tutorials/choosing-gromacs-mdp-parameters/</guid><description>What every line of an EM, NVT, NPT and production .mdp file does, which defaults to trust, and the force-field-specific settings that matter.</description><pubDate>Fri, 23 Oct 2026 00:00:00 GMT</pubDate><category>gromacs</category><category>molecular-dynamics</category><category>mdp</category><category>thermostat</category><category>barostat</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>Common GROMACS errors and how to fix them (2026 edition)</title><link>https://insilicobench.com/tutorials/common-gromacs-errors/</link><guid isPermaLink="true">https://insilicobench.com/tutorials/common-gromacs-errors/</guid><description>Real grompp, pdb2gmx and mdrun error messages from GROMACS 2026.3, what causes each one, and the fix. No -maxwarn shortcuts.</description><pubDate>Fri, 23 Oct 2026 00:00:00 GMT</pubDate><category>gromacs</category><category>troubleshooting</category><category>molecular-dynamics</category><category>grompp</category><category>mdrun</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>Protein–ligand MD in GROMACS 2026 with AMBER99SB-ILDN and GAFF2 (ACPYPE): HIV-1 protease + indinavir</title><link>https://insilicobench.com/tutorials/gromacs-protein-ligand-md/</link><guid isPermaLink="true">https://insilicobench.com/tutorials/gromacs-protein-ligand-md/</guid><description>A tested protein–ligand MD setup in GROMACS 2026.3: GAFF2/AM1-BCC ligand via ACPYPE, topology merging, solvation, ions, EM, NVT, NPT, production and analysis.</description><pubDate>Fri, 23 Oct 2026 00:00:00 GMT</pubDate><category>gromacs</category><category>molecular-dynamics</category><category>protein-ligand</category><category>acpype</category><category>gaff2</category><category>amber</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>MM-GBSA and MM-PBSA with gmx_MMPBSA 1.7: a tested workflow and how to read the numbers</title><link>https://insilicobench.com/tutorials/mm-pbsa-gmx-mmpbsa/</link><guid isPermaLink="true">https://insilicobench.com/tutorials/mm-pbsa-gmx-mmpbsa/</guid><description>End-state binding energies from a GROMACS trajectory with gmx_MMPBSA 1.7.0: inputs, GB vs PB, dielectric, decomposition, entropy, and how to read the numbers.</description><pubDate>Fri, 23 Oct 2026 00:00:00 GMT</pubDate><category>mm-pbsa</category><category>mm-gbsa</category><category>gmx_mmpbsa</category><category>binding-free-energy</category><category>gromacs</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>BindingDB Meets PubChem: A Practical Workflow for Mining Bioactivity and Patent Chemistry</title><link>https://insilicobench.com/notes/bindingdb-pubchem-patent-mining/</link><guid isPermaLink="true">https://insilicobench.com/notes/bindingdb-pubchem-patent-mining/</guid><description>BindingDB holds the measurement; PubChem holds the crosswalk. Christopher Southan&apos;s webinar follows a patent series from one to the other without counting deposits twice.</description><pubDate>Mon, 19 Oct 2026 00:00:00 GMT</pubDate><category>BindingDB</category><category>PubChem</category><category>patents</category><category>provenance</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>Making AI Work in Discovery Chemistry: Precision, Trust, and Practical Value</title><link>https://insilicobench.com/notes/ai-in-discovery-chemistry-panel/</link><guid isPermaLink="true">https://insilicobench.com/notes/ai-in-discovery-chemistry-panel/</guid><description>Notes from an Optibrium panel on AI in discovery chemistry: value over marginal accuracy, leakage and open benchmarks, plausible errors, and what LLMs change.</description><pubDate>Fri, 28 Aug 2026 00:00:00 GMT</pubDate><category>machine-learning</category><category>generative-chemistry</category><category>benchmarks</category><category>webinar</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>BindingDB for Computational Drug Discovery: From Experimental Affinity Data to ML and Docking Benchmarks</title><link>https://insilicobench.com/notes/bindingdb-guide/</link><guid isPermaLink="true">https://insilicobench.com/notes/bindingdb-guide/</guid><description>BindingDB stores measured affinities. Michael Gilson&apos;s BindingDB webinar is a guide to which rows can supervise a docking or ML job, and which lookalikes cannot.</description><pubDate>Sun, 02 Aug 2026 00:00:00 GMT</pubDate><category>BindingDB</category><category>binding-affinity</category><category>docking</category><category>machine-learning</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>3D-QSAR Explained: CoMFA vs CoMSIA</title><link>https://insilicobench.com/notes/comfa-vs-comsia-3d-qsar/</link><guid isPermaLink="true">https://insilicobench.com/notes/comfa-vs-comsia-3d-qsar/</guid><description>What CoMFA and CoMSIA actually compute, how their fields differ, and what that means for alignment, PLS models and contour maps.</description><pubDate>Mon, 02 Feb 2026 00:00:00 GMT</pubDate><category>3D-QSAR</category><category>CoMFA</category><category>CoMSIA</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>Bioisosteric Replacement In Silico: From Classical Rules to Data-Driven Suggestions</title><link>https://insilicobench.com/notes/bioisosteric-replacement-in-silico/</link><guid isPermaLink="true">https://insilicobench.com/notes/bioisosteric-replacement-in-silico/</guid><description>A replacement is bioisosteric for one target and one liability, not in general. Property-space neighbours, matched pairs and SwissBioisostere give the idea list.</description><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><category>bioisosteres</category><category>matched-molecular-pairs</category><category>medicinal-chemistry</category><category>lead-optimization</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>Chemical Space Is Not One Map: PCA, t-SNE, UMAP, and What They Actually Show</title><link>https://insilicobench.com/notes/pca-tsne-umap-chemical-space/</link><guid isPermaLink="true">https://insilicobench.com/notes/pca-tsne-umap-chemical-space/</guid><description>A chemical-space plot is a chain of choices: representation, distance, projection. PCA, t-SNE and UMAP keep different structure, and a 2D map is not a similarity measure.</description><pubDate>Fri, 12 Dec 2025 00:00:00 GMT</pubDate><category>chemical-space</category><category>UMAP</category><category>PCA</category><category>t-SNE</category><category>fingerprints</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>A Practical Guide to QSAR Model Validation: Internal, Cross, and External Checks</title><link>https://insilicobench.com/notes/qsar-model-validation/</link><guid isPermaLink="true">https://insilicobench.com/notes/qsar-model-validation/</guid><description>A three-tier QSAR validation framework: internal statistics, cross-validation and external checks, with the cuts papers report and where each one comes from.</description><pubDate>Mon, 18 Nov 2024 00:00:00 GMT</pubDate><category>QSAR</category><category>model-validation</category><category>cheminformatics</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>How to Use DataWarrior for Drug Discovery: Key Workflows From the Villoutreix Tutorials</title><link>https://insilicobench.com/notes/datawarrior-drug-discovery/</link><guid isPermaLink="true">https://insilicobench.com/notes/datawarrior-drug-discovery/</guid><description>DataWarrior is the triage step, not the model. A guide to the import-and-filter path in Bruno Villoutreix’s tutorial series, and where it sits in a project.</description><pubDate>Wed, 07 Feb 2024 00:00:00 GMT</pubDate><category>DataWarrior</category><category>cheminformatics</category><category>visualization</category><category>substructure-search</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>How to Build and Validate 3D-QSAR Models: Insights From Xu et al. (2020)</title><link>https://insilicobench.com/notes/3d-qsar-workflow-validation-metrics/</link><guid isPermaLink="true">https://insilicobench.com/notes/3d-qsar-workflow-validation-metrics/</guid><description>A 3D-QSAR model maps aligned fields onto potency. Statistics say whether it predicts; contours say where to change the molecule. Both rest on the alignment.</description><pubDate>Sun, 02 Apr 2023 00:00:00 GMT</pubDate><category>3D-QSAR</category><category>CoMFA</category><category>CoMSIA</category><category>model-validation</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>Using PaDELPy to Generate Molecular Fingerprints for Machine-Learning QSAR</title><link>https://insilicobench.com/tutorials/padelpy-fingerprints-qsar/</link><guid isPermaLink="true">https://insilicobench.com/tutorials/padelpy-fingerprints-qsar/</guid><description>Turn a SMILES table into PaDEL fingerprints and a random forest, then score a held-out split with Matthews correlation. The CV number is computed, not assumed.</description><pubDate>Sun, 15 Jan 2023 00:00:00 GMT</pubDate><category>PaDEL</category><category>fingerprints</category><category>QSAR</category><category>random-forest</category><category>python</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>Chemical Databases Every ML Scientist Should Know for Drug Discovery</title><link>https://insilicobench.com/notes/chemical-databases-for-ml/</link><guid isPermaLink="true">https://insilicobench.com/notes/chemical-databases-for-ml/</guid><description>ChEMBL, BindingDB, PubChem, PDBbind and BRENDA answer different questions. The useful choice is the table that holds the measurement you mean to model.</description><pubDate>Mon, 03 Oct 2022 00:00:00 GMT</pubDate><category>databases</category><category>ChEMBL</category><category>BindingDB</category><category>PubChem</category><category>machine-learning</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item><item><title>How to Curate and Classify Bioactivity Data From ChEMBL</title><link>https://insilicobench.com/tutorials/chembl-bioactivity-data-curation/</link><guid isPermaLink="true">https://insilicobench.com/tutorials/chembl-bioactivity-data-curation/</guid><description>How a ChEMBL IC50 table for one target becomes a labelled set: one row per molecule, a mean potency, and a class that leaves the uncertain middle out.</description><pubDate>Wed, 07 Sep 2022 00:00:00 GMT</pubDate><category>ChEMBL</category><category>data-curation</category><category>classification</category><category>python</category><author>contact@insilicobench.com (Yassir Boulaamane)</author></item></channel></rss>