Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy

• URL: http://arxiv.org/abs/2411.03460v1
• Date: Tue, 05 Nov 2024 19:20:30 GMT
• Title: Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy
• Authors: Alif Bin Abdul Qayyum, Susan D. Mertins, Amanda K. Paulson, Nathan M. Urban, Byung-Jun Yoon,
• Abstract summary: The junction tree variational autoencoder (JTVAE) has been shown to be an efficient generative model. We show how a pharmacodynamic model, assessing the therapeutic efficacy of a drug-like small molecule, can be incorporated for effective latent space optimization.
• Score: 1.8210200978176423
• License:
• Abstract: 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.

Related papers

• Conditional Latent Space Molecular Scaffold Optimization for Accelerated Molecular Design [17.175846006359674]
  We introduce Conditional Latent Space Molecular Scaffold Optimization (CLaSMO) to modify molecules strategically while maintaining similarity to the original input. Our LSBO setting improves the sample-efficiency of our optimization, and our modification approach helps us to obtain molecules with higher chances of real-world applicability. We also provide an open-source web application that enables chemical experts to apply CLaSMO in a Human-in-the-Loop setting.
  arXiv  Detail & Related papers  (2024-11-03T03:17:38Z)
• Cliqueformer: Model-Based Optimization with Structured Transformers [102.55764949282906]
  We develop a model that learns the structure of an MBO task and empirically leads to improved designs. We evaluate Cliqueformer on various tasks, ranging from high-dimensional black-box functions to real-world tasks of chemical and genetic design.
  arXiv  Detail & Related papers  (2024-10-17T00:35:47Z)
• Leveraging Latent Evolutionary Optimization for Targeted Molecule Generation [0.0]
  We present an innovative approach, Latent Evolutionary Optimization for Molecule Generation (LEOMol) LEOMol is a generative modeling framework for the efficient generation of optimized molecules. Our approach consistently demonstrates superior performance compared to previous state-of-the-art models.
  arXiv  Detail & Related papers  (2024-07-02T13:42:21Z)
• Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization [147.7899503829411]
  AliDiff is a novel framework to align pretrained target diffusion models with preferred functional properties. It can generate molecules with state-of-the-art binding energies with up to -7.07 Avg. Vina Score.
  arXiv  Detail & Related papers  (2024-07-01T06:10:29Z)
• Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders [2.0701439270461184]
  A critical challenge for pre-trained generative molecular design models is to fine-tune them to be better suited for downstream design tasks. In this work, we propose a novel approach for a generative uncertainty decoder (VAE)-based GMD model through performance feedback in an active setting.
  arXiv  Detail & Related papers  (2024-05-31T02:00:25Z)
• Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation [9.442146563809953]
  We develop a versatile 'plug-in' molecular generation model that incorporates objectives related to target affinity, drug-likeness, and synthesizability. We identify PSO-ENP as the optimal variant for multi-objective molecular generation and optimization.
  arXiv  Detail & Related papers  (2024-04-10T02:37:24Z)
• Diffusion Model for Data-Driven Black-Box Optimization [54.25693582870226]
  We focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization. We study two practical types of labels: 1) noisy measurements of a real-valued reward function and 2) human preference based on pairwise comparisons. Our proposed method reformulates the design optimization problem into a conditional sampling problem, which allows us to leverage the power of diffusion models.
  arXiv  Detail & Related papers  (2024-03-20T00:41:12Z)
• Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting [53.44684898432997]
  Generation of molecules with desired chemical and biological properties is critical for drug discovery. We propose a probabilistic generative model to capture the joint distribution of molecules and their properties. Our method achieves very strong performances on various molecule design tasks.
  arXiv  Detail & Related papers  (2023-06-09T03:04:21Z)
• Multi-Objective Latent Space Optimization of Generative Molecular Design Models [3.1996400013865656]
  We propose a multi-objective latent space optimization (LSO) method that can significantly enhance the performance of generative molecular design (GMD) We demonstrate that our multi-objective GMD LSO method can significantly improve the performance of GMD for jointly optimizing multiple molecular properties.
  arXiv  Detail & Related papers  (2022-03-01T15:12:05Z)
• Molecular Attributes Transfer from Non-Parallel Data [57.010952598634944]
  We formulate molecular optimization as a style transfer problem and present a novel generative model that could automatically learn internal differences between two groups of non-parallel data. Experiments on two molecular optimization tasks, toxicity modification and synthesizability improvement, demonstrate that our model significantly outperforms several state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-30T06:10:22Z)
• Differentiable Scaffolding Tree for Molecular Optimization [47.447362691543304]
  We propose differentiable scaffolding tree (DST) that utilizes a learned knowledge network to convert discrete chemical structures to locally differentiable ones. Our empirical studies show the gradient-based molecular optimizations are both effective and sample efficient.
  arXiv  Detail & Related papers  (2021-09-22T01:16:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.