Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy
This work addresses the challenge of efficient drug discovery for cancer treatment by integrating mechanistic models into data-driven approaches, representing an incremental improvement over existing methods.
The authors tackled the problem of improving generative molecular design for cancer therapy by optimizing the latent space of models like JTVAE using mechanistic pathway models, resulting in enhanced sampling efficiency for suggesting better drug-like molecules with improved properties.
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.