Your partner in computational drug design
Silicos-it is a Belgian consultancy company, founded in 2010 and specialised in computational drug design. We provide support in all aspects of modern computational drug design, including virtual screening, molecular dynamics applications, and state-of-the-art structure-activity machine learning models. In addition, we are also involved in writing open source software tools.
Since 2013, a strong relation with the University of Antwerp (UA) was established. Because of this, we can provide you with a complete set of solutions to all your questions related to computational drug design. This may include service work under the wings of the UA, submission and execution of a VLAIO project with the UA as academic partner, or some well-defined fee-for-service projects with Silicos-it as the commercial partner.
Do you like to find out more of what we can do for you? Then have a look at the research examples page on this website for ideas and opportunities. And the who are we page gives you insight into our history and where we are right now.
Do you think we can help you? We are open to many forms of collaboration. Therefore, do not hesitate to contact us to discuss your potential research ideas. Maybe it is the beginning of a mind-blowing research collaboration!
Our latest post
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In vitro kinetic profiling by enzymatic approaches to select FAPIs with a long residence time
We have just published a new paper in the European Journal of Medicinal Chemistry showing how to design better FAP-targeting radioligands by focusing on how long they stay bound to their target, not just how tightly they bind. In this work, we introduce a practical kinetic screening workflow that measures binding and unbinding rates for fibroblast activation protein inhibitors (FAPIs), giving a much clearer picture of which molecules are likely to remain longer in tumors. We then combine these experiments with molecular docking simulations to visualize how different chemical designs interact with the enzyme at the atomic level and to explain why some compounds dissociate much more slowly than others. By linking experimental kinetics with computational binding models, the study provides a rational, structure-based roadmap for optimizing FAPIs with longer residence times—a key requirement for effective theranostic applications. Overall, this approach helps researchers move from trial-and-error chemistry to data-driven, computationally guided radioligand design.