A live online cohort on Modern AI-Assisted In-Silico Drug Discovery Workflows: structure intelligence, protein curation, ligand preparation, multi-engine docking, AI-assisted scoring, pose validation, molecular dynamics design, MD interpretation, affinity thinking, and decision-making.
Real science, not protocol cloning.
Classical tools are still useful. Outdated habits are not.
Most weak in-silico workflows are not weak because the tools are bad. They are weak because the workflow is copied without enough scientific reasoning.
What are we trying to test, and what evidence would actually support the claim?
Docking scores, AI outputs, and MD plots are signals — not automatic proof.
Use classical and AI-assisted tools together with assumptions, controls, and uncertainty.
A practical map from scientific question to stronger computational evidence.
What are we trying to test?
UniProt · PDB · SIFTS · Literature
PDBFixer · MODELLER · Protonation
RDKit · Tautomers · Stereochemistry
Vina/Smina · GNINA · AI-assisted scoring
PoseBusters · ProLIF · Visual inspection
OpenMM/GROMACS · Waters/Ions · Replicas
MDAnalysis · Interaction persistence · Convergence
MM/PBSA carefully · OpenFE concepts · ADMET
Stronger evidence · Better decisions
Beginner-accessible, but not basic. The cohort starts with foundations and moves toward modern workflow design.
Docking foundations, MD basics, scoring limits, and common beginner mistakes.
PDB vs predicted structures, UniProt/PDB/SIFTS mapping, and uncertainty.
Protein repair logic, ligand states, RDKit preparation, multi-engine docking, and AI-assisted scoring.
Pose validation, interaction fingerprints, MD interpretation, affinity thinking, and decisions.
Select your seat type in the registration form. Payment is handled securely after registration through Stripe.
For students, PhD researchers, postdocs, and academic researchers. Available until 30 June.
Reserve seatAcademic seat after the founding deadline with the same live cohort access and materials.
Reserve seatFor industry, CRO, biotech, pharma, and professional researchers joining individually.
Reserve seatFor labs, departments, and teams that want group access or a private session.
Request accessStructured, independent pages based on the DeepDrug AI visual concepts. Each article expands one principle behind the cohort.
Predictions become useful only when they are connected to context, evidence, expert interpretation, and a justified decision.
Read article
A docking or model score can help prioritize hypotheses, but it cannot replace chemical plausibility, biological context, and validation.
Read article
Biological systems are interdependent, contextual, and dynamic. Shortcuts fail when they ignore the system around the molecule.
Read articleComplete the registration form. If you choose to pay now, you will be redirected to the correct Stripe payment page.
No. GNINA is only one example of AI-assisted scoring. The cohort focuses on the full workflow.
It is beginner-accessible but not basic. There is an optional primer, then the main cohort builds toward modern workflow design.
No. Docking and MD are included, but the focus is workflow reasoning, validation, uncertainty, and scientific decision-making.
No. Classical tools are still useful. What is outdated is blindly copying protocols without validation or scientific reasoning.
Yes. Recordings access and a certificate of completion are included.
