01🧬
Drug Discovery Foundations
Learn targets, ligands, SMILES, affinity, and why a promising candidate is not a medicine.
- Target biology
- Molecular representations
- Evidence ladder
DEEP2LEAD ACADEMY
Learn drug discovery
Learn drug discovery
by doing it.
Build scientific intuition from target biology to multi-objective lead evaluation—with hands-on computational experiments and an honest view of uncertainty.
1LEARNBuild the concept
→
2EXPERIMENTTest a hypothesis
→
3EVALUATERead the tradeoffs
→
4REFLECTRecord evidence
GUIDED CURRICULUM
View learning activity →Four tracks. One discovery journey.
02⚗️
Molecule Design Studio
Form a hypothesis, generate candidates, inspect 3D context, and save a reproducible computational experiment.
- Structure–property thinking
- Candidate generation
- Experiment records
03📊
Lead Evaluation & Tradeoffs
Compare potency, physicochemical quality, ADME/toxicity simulations, synthesis proxies, confidence, and Pareto tradeoffs.
- Multi-objective scoring
- Applicability
- Uncertainty & limits
04⚛️
Quantum Computing for Discovery
Explore active spaces, qubits, Hamiltonians, VQE convergence, and the boundary between a teaching simulation and quantum chemistry.
- Electronic structure
- VQE workflow
- Classical vs quantum evidence
REAL
COMPUTATIONAL
WORK
COMPUTATIONAL
WORK
HANDS-ON EXPERIMENT STUDIO
Move beyond watching tutorials.
Choose a protein target, write a hypothesis, provide a seed molecule, generate candidates, calculate molecular properties, inspect 3D structure context, and save the result. Your inputs and outputs form a reproducible computational experiment—not wet-lab or clinical evidence.
Open Experiment Studio →Inputs, calculations, and scientific reasoning
SMILES validation, RDKit descriptors, curated protein structures, saved hypotheses, multi-objective comparisons, and explicit provenance.
Some predictive and quantum learning outputs
Every simulated ADME, toxicity, patient, or quantum result is labeled. It teaches workflow concepts and must not be read as a measurement.
Independent experimental validation
A strong computational candidate earns further study. It does not establish efficacy, safety, synthesis feasibility, or regulatory readiness.
WHEN YOU GET STUCK