The 17th EFMC Short Course on Medicinal Chemistry took place April 23-26, 2023 in Oegstgeest, near Leiden in the Netherlands. It covered for the first time the exciting topic of Targeted Protein... Show moreThe 17th EFMC Short Course on Medicinal Chemistry took place April 23-26, 2023 in Oegstgeest, near Leiden in the Netherlands. It covered for the first time the exciting topic of Targeted Protein Degradation (full title: Small Molecule Protein Degraders: A New Opportunity for Drug Design and Development). The course was oversubscribed, with 35 attendees and 6 instructors mainly from Europe but also from the US and South Africa, and representing both industry and academia. This report summarizes the successful event, key lectures given and topics discussed. Show less
Liu, X.; Ye, K.; Vlijmen, H. van; IJzerman, A.P.; Westen, G.J.P. van 2023
Rational drug design often starts from specific scaffolds to which side chains/substituents are added or modified due to the large drug-like chemical space available to search for novel drug-like... Show moreRational drug design often starts from specific scaffolds to which side chains/substituents are added or modified due to the large drug-like chemical space available to search for novel drug-like molecules. With the rapid growth of deep learning in drug discovery, a variety of effective approaches have been developed for de novo drug design. In previous work we proposed a method named DrugEx, which can be applied in polypharmacology based on multi-objective deep reinforcement learning. However, the previous version is trained under fixed objectives and does not allow users to input any prior information (i.e. a desired scaffold). In order to improve the general applicability, we updated DrugEx to design drug molecules based on scaffolds which consist of multiple fragments provided by users. Here, a Transformer model was employed to generate molecular structures. The Transformer is a multi-head self-attention deep learning model containing an encoder to receive scaffolds as input and a decoder to generate molecules as output. In order to deal with the graph representation of molecules a novel positional encoding for each atom and bond based on an adjacency matrix was proposed, extending the architecture of the Transformer. The graph Transformer model contains growing and connecting procedures for molecule generation starting from a given scaffold based on fragments. Moreover, the generator was trained under a reinforcement learning framework to increase the number of desired ligands. As a proof of concept, the method was applied to design ligands for the adenosine A2A receptor (A2AAR) and compared with SMILES-based methods. The results show that 100% of the generated molecules are valid and most of them had a high predicted affinity value towards A2AAR with given scaffolds. Show less
