Protocols that re‑run.

Step-by-step tutorials pinned to exact software versions, each showing the output you should get. Free browser tools catch the .mdp mistakes that cost a week of simulation time.

GROMACS
2026.3
✓ 4 tutorials · 23 Oct
AutoDock Vina
1.2.7
✓ 1 tutorial · 23 Oct
gmx_MMPBSA
1.7.0
✓ 1 tutorial · 23 Oct
AmberTools
26.0
✓ 2 tutorials · 23 Oct
Plate I · Specimen MK1Drag to rotate
LigandIndinavir (MK1) · C₃₆H₄₇N₅O₄ · 613.8 g/mol GeometryCrystal pose, PDB 1HSG · heavy atoms only Used inISB-0001 · ISB-0004 · ISB-0005
§ 01

The 1HSG series

One system, start to finish: HIV‑1 protease with indinavir, from docking pose to binding free energy.

§ 02

Tutorials

All tutorials →

In technical review · commands version-pinned · final author sign-off pending

Computational chemistry

7 tutorials · docking · molecular dynamics · free energy
ISB-0001Chem Redocking with AutoDock Vina 1.2 and Meeko: a reproducible 1HSG walkthrough 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). Vina 1.2.7 Docking · 7 min ISB-0002Chem Choosing GROMACS .mdp parameters: a stage-by-stage guide for GROMACS 2026 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. GROMACS 2026.3 Molecular dynamics · 6 min ISB-0003Chem Common GROMACS errors and how to fix them (2026 edition) Real grompp, pdb2gmx and mdrun error messages from GROMACS 2026.3, what causes each one, and the fix. No -maxwarn shortcuts. GROMACS 2026.3 Troubleshooting · 6 min ISB-0004Chem Protein–ligand MD in GROMACS 2026 with AMBER99SB-ILDN and GAFF2 (ACPYPE): HIV-1 protease + indinavir 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. GROMACS 2026.3 Molecular dynamics · 9 min ISB-0005Chem MM-GBSA and MM-PBSA with gmx_MMPBSA 1.7: a tested workflow and how to read the numbers 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. gmx_MMPBSA 1.7.0 Free energy · 9 min ISB-0006Chem How to Curate and Classify Bioactivity Data From ChEMBL 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. Data curation · 4 min ISB-0007Chem Using PaDELPy to Generate Molecular Fingerprints for Machine-Learning QSAR 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. QSAR · 4 min
§ 03

Learn CADD

Start the course →

A free course in computer-aided drug design: 18 modules from target biology to docking, QSAR, molecular dynamics and ADMET, each with a knowledge check and several with interactive playgrounds.

  1. 01Introduction to Drug Discovery
  2. 02Target Identification, Validation & Druggability
  3. 03Fundamentals of Ligand-Receptor Interactions
  4. 04Molecular Mechanics & Force Fields
  5. 05Cheminformatics & Molecular Representations
  6. 06Molecular Docking
  7. 07Pharmacophore Modeling
  8. 08Virtual Screening Strategies
  9. 09QSAR Modeling & AI Interpretation
  10. 10Molecular Dynamics & Free Energy Methods
  11. 11De Novo & Generative Molecular Design
  12. 12In Silico ADMET & Safety Assessment
  13. 13Structural Bioinformatics & Molecular Visualization
  14. 14Applied CADD Workflows & Reproducibility
  15. 15Pharmacokinetics & DMPK
  16. 16Protein, Antibody & Protein-Protein Modeling
  17. 17Bioinformatics & Systems Biology Foundations
  18. 18Sequence Bioinformatics & Evolution
§ 04

The workbench

Free tools. The calculators run entirely in your browser, and nothing you paste is uploaded; Assayer's planner asks a hosted AI model.

T-01 Molecular dynamics GROMACS .mdp checker Paste an .mdp file and get grompp-style errors, warnings and fixes, or build EM/NVT/NPT/MD templates. 2 errors · 3 warnings on broken NPT T-02 Binding & assays IC50 → Ki converter Cheng–Prusoff for competitive, noncompetitive, uncompetitive, mixed and binding assays, with tight-binding checks. IC50 120 nM → Ki 40 nM T-03 Binding & assays Kd ↔ ΔG + ligand efficiency Convert Kd, Ki or IC50 to binding free energy at any temperature, and compute LE, LLE and BEI. Kd 10 nM → ΔG -10.9 kcal/mol T-04 Docking Vina box generator Paste a PDB, pick residues or a ligand, get center/size, a Vina config file and a Python snippet. 1HSG MK1 · box 24.9 × 20.2 × 18.6 Å T-05 Discovery planning Assayer: tool catalogue & planner A curated catalogue of drug discovery software by pipeline stage, and a planner that turns a research question into a step-by-step protocol with checkpoints and tools for each step. 304 curated tools · 17 stages T-06 ADMET BBBScope: blood–brain barrier Predict BBB permeability from SMILES, with each compound’s distance from the training data and the accuracy that distance implies. T-07 Docking Pose validation: redocking & cross-docking Load crystal ligands and docked poses, get symmetry-corrected RMSD, top-1, top-N and sampling success, and a cross-docking matrix. 8 redocks · top-1 5/8 · sampling 6/8 < 2 Å
§ 05

How this site works

01

Version-pinned

Each tutorial lists the exact software versions it was checked against (GROMACS, AutoDock Vina, Meeko, AmberTools, gmx_MMPBSA and so on).

02

Cited

Every method links back to the original paper or the official manual.

03

Written by a practitioner

Every page is written, run and checked by Yassir Boulaamane. See about the site, the author page and the editorial & AI-use policy.

§ 06

Who runs the bench

Yassir Boulaamane
Postdoctoral researcher, computational drug discovery Yassir Boulaamane Postdoctoral researcher at the Universitat Autònoma de Barcelona (InSiliChem), with a PhD in computational drug discovery. Works on molecular docking, molecular dynamics, free energy calculations and machine-learning QSAR. Molecular docking · Molecular dynamics (GROMACS) · MM-PBSA / MM-GBSA · QSAR and machine learning for molecular properties · Drug metabolism (cytochrome P450)
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