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Izdebski, A.* ; Olszewski, J.* ; Gawade, P. ; Koras, K.* ; Korkmaz, S. ; Rauscher, V.* ; Tomczak, J.M.* ; Szczurek, E.

Synergistic benefits of joint molecule generation and property prediction.

Trans. Machine Learn. Res. 2026-January, 1-32 (2026)
Postprint
Modeling the joint distribution of data samples and their properties allows to construct a single model for both data generation and property prediction, with synergistic benefits reaching beyond purely generative or predictive models. However, training joint models presents daunting architectural and optimization challenges. Here, we propose Hyformer, a transformer-based joint model that successfully blends the generative and predictive functionalities, using an alternating attention mechanism and a joint pre-training scheme. We show that Hyformer is simultaneously optimized for molecule generation and property prediction, while exhibiting synergistic benefits in conditional sampling, out-of-distribution property prediction and representation learning. Finally, we demonstrate the benefits of joint learning in a drug design use case of discovering novel antimicrobial peptides.
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Publication type Article: Journal article
Document type Scientific Article
ISSN (print) / ISBN 2835-8856
e-ISSN 2835-8856
Journal Transactions on Machine Learning Research
Quellenangaben Volume: 2026-January, Issue: , Pages: 1-32 Article Number: , Supplement: ,
Publisher Journal of Machine Learning Research Inc.
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)