Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912106846093312 |
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| author | Qayyum, Alif Bin Abdul Mertins, Susan D. Paulson, Amanda K. Urban, Nathan M. Yoon, Byung-Jun |
| author_facet | Qayyum, Alif Bin Abdul Mertins, Susan D. Paulson, Amanda K. Urban, Nathan M. Yoon, Byung-Jun |
| contents | The data-driven drug design problem can be formulated as an optimization task of a potentially expensive black-box objective function over a huge high-dimensional and structured molecular space. The junction tree variational autoencoder (JTVAE) has been shown to be an efficient generative model that can be used for suggesting legitimate novel drug-like small molecules with improved properties. While the performance of the generative molecular design (GMD) scheme strongly depends on the initial training data, one can improve its sampling efficiency for suggesting better molecules with enhanced properties by optimizing the latent space. In this work, we propose how mechanistic models - such as pathway models described by differential equations - can be used for effective latent space optimization(LSO) of JTVAEs and other similar models for GMD. To demonstrate the potential of our proposed approach, we show how a pharmacodynamic model, assessing the therapeutic efficacy of a drug-like small molecule by predicting how it modulates a cancer pathway, can be incorporated for effective LSO of data-driven models for GMD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_03460 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy Qayyum, Alif Bin Abdul Mertins, Susan D. Paulson, Amanda K. Urban, Nathan M. Yoon, Byung-Jun Machine Learning Quantitative Methods The data-driven drug design problem can be formulated as an optimization task of a potentially expensive black-box objective function over a huge high-dimensional and structured molecular space. The junction tree variational autoencoder (JTVAE) has been shown to be an efficient generative model that can be used for suggesting legitimate novel drug-like small molecules with improved properties. While the performance of the generative molecular design (GMD) scheme strongly depends on the initial training data, one can improve its sampling efficiency for suggesting better molecules with enhanced properties by optimizing the latent space. In this work, we propose how mechanistic models - such as pathway models described by differential equations - can be used for effective latent space optimization(LSO) of JTVAEs and other similar models for GMD. To demonstrate the potential of our proposed approach, we show how a pharmacodynamic model, assessing the therapeutic efficacy of a drug-like small molecule by predicting how it modulates a cancer pathway, can be incorporated for effective LSO of data-driven models for GMD. |
| title | Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2411.03460 |