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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A beginner’s approach to deep learning applied to VS and MD techniques
This paper in Journal of Cheminformatics reviews how deep learning (DL) is being integrated into molecular modelling, particularly in virtual screening (VS) and molecular dynamics (MD) simulations, to enhance drug discovery workflows. DL techniques are shown to improve accuracy, speed, and analysis of simulations by addressing limitations in traditional computational chemistry, such as hardware constraints and algorithmic inefficiencies. The review is structured around four key areas: DL-enhanced VS workflows, DL-guided MD simulations, neural network-based force field approximations, and DL-driven MD trajectory analysis. A broad range of DL models are presented —including convolutional neural networks, graph neural networks, and generative models like GANs and VAEs—alongside real-world applications such as DEEPScreen, DiffDock, and AlphaFold. The paper concludes that DL holds the potential to transform molecular modelling, but also notes challenges like data quality, model interpretability, and the need for broader accessibility and standardization.