Structure-based drug discovery in 2026 looks nothing like it did five years ago. Where teams used to wait on crystal structures, negotiate expensive molecular docking runs, and enumerate fragments by hand, they now chain AI-predicted structures into pocket-aware generative models into chemistry-literate reasoning LLMs. The result is a pipeline that runs in hours instead of months and produces better starting points.
This post is the playbook. We will walk the full chain from target sequence to ranked candidates, name the tools that matter at each step, and call out the failure modes that you need to guard against. At the end we will wire the whole thing together through DiffSBDD and the SciRouter drug discovery lab.
Stage 1: Target to structure
Everything starts with a protein structure. If you have a co-crystal structure from the PDB, use it — nothing beats real ground truth. If you do not, predict the structure from sequence with a modern structural model.
- AlphaFold for high-quality single-chain structural predictions.
- Boltz-2 for structural prediction plus protein-ligand complex prediction, which also gives you a pocket definition.
- ESMFold for very fast single-sequence predictions when speed matters more than absolute accuracy.
Pick based on the question you are asking. For pocket-conditioned generation you need a model that either produces pocket annotations natively (Boltz-2) or lets you run a pocket-detection tool on top (AlphaFold output into fpocket, for example).
Stage 2: Structure to pocket
A predicted structure alone is not enough. You need to know which cavity you are designing against. The active site, an allosteric pocket, or a protein-protein interface each require different pocket definitions.
- Run a pocket detection tool or use Boltz-2's built-in pocket output.
- Inspect the top pockets visually. Filter by size, druggability, and biological relevance.
- Decide which pocket to target before committing to generation. Generating against the wrong pocket produces useless candidates.
When pocket prediction fails
For disordered regions, cryptic pockets that form only in complex, and proteins with novel folds, pocket prediction is less reliable. Expert review matters more in those cases, and it is worth running multiple pocket-detection tools and comparing.
Stage 3: Pocket to binder
With a pocket in hand, the generative step produces candidate molecules. In the AI playbook, this step is usually a 3D diffusion model conditioned on the pocket.
- DiffSBDD for pocket-aware generation with equivariant diffusion.
- TargetDiff as an alternative with different architectural choices.
- REINVENT4 as a reinforcement-learning alternative when you need explicit property optimization.
The pattern we recommend is to run a diffusion generator first for pocket-aware candidates, then optionally pipe the best candidates into an RL optimizer for property tuning. For more on this pattern, see Diffusion vs RL for drug design.
Stage 4: Binder to filtered shortlist
Raw generator output contains a lot of noise. The filtering step removes molecules that would never make it past developability checks.
- Drug-likeness filters. QED, logP bounds, Lipinski, Veber.
- Synthetic accessibility. A SAS score or a retrosynthesis check. Molecules that cannot be made are not leads.
- PAINS and reactive motifs. Remove known problem substructures.
- ADMET panel. Predicted solubility, permeability, hERG, CYP, and toxicity from a calibrated predictor.
Do this filtering before you spend GPU time on the next stages. Every molecule that survives should be a real candidate.
Stage 5: Shortlist to ranked candidates
After filtering you have a shortlist, but they are not ranked. Ranking combines structural binding prediction with chemistry-aware reasoning.
- Redock with a traditional program.AutoDock Vina, Glide, or equivalent. This validates that the generator's pose is plausible.
- Binding-affinity predictor. An ML model trained to predict binding score from a pose. Boltz-2 can also produce affinity estimates.
- Chemistry LLM rationale. Call TxGemma for a short written rationale on each top candidate. This gives you a review surface that is richer than raw numbers.
The rationale step is the quiet game-changer. Before LLMs, ranking was a list of scores and a chemist had to supply the context. Now the context is generated with the ranking, and chemists spend their time reviewing rather than summarizing.
Stage 6: Human review
The playbook ends where drug discovery always has: with a chemist reading the candidates and deciding what to do next. The AI pipeline gets you to that point faster and with more context, but it does not make the decision.
- Read the top 10-20 candidates with their rationales.
- Flag concerns: unusual chemistry, synthesis risk, missing functional groups, off-target risk.
- Select the shortlist that goes to synthesis and assay.
- Feed the wet-lab results back into the pipeline to bias the next round of generation.
Failure modes to guard against
Garbage pocket
If the pocket is wrong, everything downstream is wrong. Always inspect the pocket before generating. When in doubt, run multiple pocket detectors and compare.
Reward hacking in the optimizer
If you add a REINVENT stage, watch out for molecules that maximize the reward in ways a chemist would reject. Multi-objective rewards and chemist review catch most of these.
Over-reliance on any single score
Docking scores, QED, and LLM confidence are all imperfect. A candidate that looks good on one metric and bad on another is an interesting candidate, not a failure.
Skipping validation
The pipeline produces hypotheses. Assays produce answers. Nothing matters until you test the molecules.
Putting it together on SciRouter
Every tool in the playbook is exposed as a managed endpoint on SciRouter. You can run the whole pipeline through a single API key — Boltz-2 for structure and pocket, DiffSBDD for generation, the chemistry panel for filtering, and TxGemma for rationale. The drug discovery lab wraps all of this into a UI if you do not want to write code. For a hands-on walkthrough, see From Protein Pocket to Lead Compound.
Bottom line
Structure-based drug discovery used to mean expensive crystal structures, slow docking runs, and months of manual fragment growing. The 2026 playbook replaces every step before experimental validation with AI tools that chain cleanly through a single gateway. The wet lab still matters. The iteration loops still matter. What changes is the speed and quality of the starting point — and in drug discovery, a better starting point is half the battle.
Try DiffSBDD on SciRouter → or open the drug discovery lab →