Recent advancements in deep learning and generative models have significantly expanded the applications of virtual screening for drug-like compounds. Here, we introduce a multitarget transformer model, PCMol, that leverages the latent protein embeddings derived from AlphaFold2 as a means of conditioning a de novo generative model on different targets. Incorporating rich protein representations allows the model to capture their structural relationships, enabling the chemical space interpolation of active compounds and target-side generalization to new proteins based on embedding similarities. In this work, we benchmark against other existing target-conditioned transformer models to illustrate the validity of using AlphaFold protein representations over raw amino acid sequences. We show that low-dimensional projections of these protein embeddings cluster appropriately based on target families and that model performance declines when these representations are intentionally corrupted. We also show that the PCMol model generates diverse, potentially active molecules for a wide array of proteins, including those with sparse ligand bioactivity data. The generated compounds display higher similarity known active ligands of held-out targets and have comparable molecular docking scores while maintaining novelty. Additionally, we demonstrate the important role of data augmentation in bolstering the performance of generative models in low-data regimes. Software package and AlphaFold protein embeddings are freely available at https://github.com/CDDLeiden/PCMol.

CZ OPENSCREEN National Infrastructure for Chemical Biology Department of Informatics and Chemistry Faculty of Chemical Technology University of Chemistry and Technology Prague Technická 5 166 28 Prague Czech Republic

Leiden Academic Centre for Drug Research Leiden University Einsteinweg 55 2333CC Leiden The Netherlands

Leiden Institute of Advanced Computer Science Leiden University Niels Bohrweg 1 2333CA Leiden The Netherlands

Leiden Institute of Chemistry Leiden University Einsteinweg 55 2333CC Leiden The Netherlands

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