Synergistic Benefits of Joint Molecule Generation and Property Prediction
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910201036144640 |
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| author | Izdebski, Adam Olszewski, Jan Gawade, Pankhil Koras, Krzysztof Korkmaz, Serra Rauscher, Valentin Tomczak, Jakub M. Szczurek, Ewa |
| author_facet | Izdebski, Adam Olszewski, Jan Gawade, Pankhil Koras, Krzysztof Korkmaz, Serra Rauscher, Valentin Tomczak, Jakub M. Szczurek, Ewa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16559 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Synergistic Benefits of Joint Molecule Generation and Property Prediction Izdebski, Adam Olszewski, Jan Gawade, Pankhil Koras, Krzysztof Korkmaz, Serra Rauscher, Valentin Tomczak, Jakub M. Szczurek, Ewa Machine Learning Quantitative Methods 68T01 I.2.1 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. |
| title | Synergistic Benefits of Joint Molecule Generation and Property Prediction |
| topic | Machine Learning Quantitative Methods 68T01 I.2.1 |
| url | https://arxiv.org/abs/2504.16559 |