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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Cosolvent MD on DPP4, DPP8 and DPP9
In this recent Journal of Chemical Information and Modeling paper by Olivier Beyens, our efforts in computational drug design against dipeptidyl peptidase 4 (DPP4), DPP8 and DPP9 are presented. Cosolvent molecular dynamics (MD) simulations have been applied to these three protein targets. The cosolvent molecular dynamics simulations reproduce key ligand binding features and known binding pockets, while also highlighting interesting fragment positions for future ligand optimization. The resulting fragment maps from the cosolvent molecular dynamics are freely available for use in future research (). Detailed instructions for easy visualization of the fragment maps are provided, ensuring that the results are usable by both computational and medicinal chemists. Additionally, we used the fragment maps to search for the binding pockets with significant potential using an algorithmic approach combining top fragment locations. To discover novel binding scaffolds, a limited pharmacophore screening was performed, where the pharmacophores were based on the analyses of the cosolvent simulations.