Abstract
This work deals with the problem of recognising chemical entities that are able to act as polypharmacological compounds by binding and modulating two different proteins that have been demonstrated to be critical for cancer metastatic potential and aggressivity. In particular, the aim is to develop a method that automatically indicates which of the given compounds are likely to be ‘active’ with respect to the two proteins in question. In medicinal chemistry, an active compound is defined as a ligand that is capable of modulating the e-mail: dieter.kranzlmueller@lrz.de. In this work, on the other hand, we aim to develop a parallel algorithm that is able to provide an exact solution to the problem using classical techniques of isomorphism and similarity between graphs.
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Branco, D., Di Martino, B., Cosconati, S., Kranzlmueller, D., D’Angelo, S. (2023). Towards a Parallel Graph Approach to Drug Discovery. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2023. Lecture Notes in Networks and Systems, vol 655. Springer, Cham. https://doi.org/10.1007/978-3-031-28694-0_12
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