Have AI pre-select drug candidates computationally

Pharmaceutical researchers search for new drug candidates using molecular simulation

Future prospectAI Act: no extra obligationsDesigning & simulatingChemicals & pharma

Fits: Research & development

The aim: out of a huge number of conceivable molecules, only the few that computationally fit the target protein best go to the lab. Honestly: this is not ready for broad routine work. Individual candidates from such methods are in clinical trials, but their efficacy in later phases has not so far been shown to be better than that of molecules found the conventional way.

Is this for you?

  • You are researching a target protein for which usable structural or binding data exist, and you want to try a limited project.
  • You have your own measurement data from screening or assays that can be analysed for comparison with predictions.
  • You can stop a project without a programme decision depending on it.
First step today

Without any of your own unpublished structures: use the template below to compile the state of the art for your target protein area, with sources, and use it to define questions for vendors and pilot criteria.

How to go about it

  1. Define the target protein, quality measures and stop criteria
  2. Prepare your own measurement and structural data
  3. Have a limited number of candidates generated
  4. Review and select in the expert panel
  5. Measure in the lab and compare against chance
  6. Feed the measurements back and decide again

Templates to copy

Try it now
in the training
Analyse
in the training
Plan the rollout
in the training
Included in the training

The complete guide

  • 6 steps explaining exactly what to do
  • 3 proven templates to copy into your own AI
  • Safe use: data, checking, deciding, informing
  • Pitfalls, effort and how to recognise success

With the training you get all 430 guides for as long as your access runs.

See the trainingOne-off per person, no subscription

Same approach, other use cases

15 more use cases follow the pattern “Designing & simulating”.

Develop image ideas and a mood board for a consistent feedCreate furniture designs and cutting lists fasterDesign lighter components with generative designEstimate crash and flow behaviour of early variantsModel price increases and supply failures in advancePre-plan photovoltaics and heat pumps from aerial images and consumptionRun stress scenarios for the portfolioWork through design variants and floor plans fasterHave AI suggest hypotheses and experimental designsHave synthesis routes suggestedPilot AI-suggested structural optionsPre-develop recipe variants with AI
‹ PreviousSee all use casesNext ›

CP-01 · Legal status 08.10.2026