Multi-Objective Latent Space Optimization of Generative Molecular Design Models

• URL: http://arxiv.org/abs/2203.00526v3
• Date: Mon, 22 Jul 2024 01:26:32 GMT
• Title: Multi-Objective Latent Space Optimization of Generative Molecular Design Models
• Authors: A N M Nafiz Abeer, Nathan Urban, M Ryan Weil, Francis J. Alexander, Byung-Jun Yoon,
• Abstract summary: 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.
• Score: 3.1996400013865656
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Molecular design based on generative models, such as variational autoencoders (VAEs), has become increasingly popular in recent years due to its efficiency for exploring high-dimensional molecular space to identify molecules with desired properties. While the efficacy of the initial model strongly depends on the training data, the sampling efficiency of the model for suggesting novel molecules with enhanced properties can be further enhanced via latent space optimization. In this paper, we propose a multi-objective latent space optimization (LSO) method that can significantly enhance the performance of generative molecular design (GMD). The proposed method adopts an iterative weighted retraining approach, where the respective weights of the molecules in the training data are determined by their Pareto efficiency. We demonstrate that our multi-objective GMD LSO method can significantly improve the performance of GMD for jointly optimizing multiple molecular properties.

Related papers

• Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy [1.8210200978176423]
  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.
  arXiv  Detail & Related papers  (2024-11-05T19:20:30Z)
• 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)
• Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model [77.50732023411811]
  We propose a text-guided multi-property molecular optimization method utilizing transformer-based diffusion language model (TransDLM) TransDLM leverages standardized chemical nomenclature as semantic representations of molecules and implicitly embeds property requirements into textual descriptions. Our approach surpasses state-of-the-art methods in optimizing molecular structural similarity and enhancing chemical properties on the benchmark dataset.
  arXiv  Detail & Related papers  (2024-10-17T14:30:27Z)
• XMOL: Explainable Multi-property Optimization of Molecules [2.320539066224081]
  We propose Explainable Multi-property Optimization of Molecules (XMOL) to optimize multiple molecular properties simultaneously. Our approach builds on state-of-the-art geometric diffusion models, extending them to multi-property optimization. We integrate interpretive and explainable techniques throughout the optimization process.
  arXiv  Detail & Related papers  (2024-09-12T06:35:04Z)
• Many-Shot In-Context Learning for Molecular Inverse Design [56.65345962071059]
  Large Language Models (LLMs) have demonstrated great performance in few-shot In-Context Learning (ICL) We develop a new semi-supervised learning method that overcomes the lack of experimental data available for many-shot ICL. As we show, the new method greatly improves upon existing ICL methods for molecular design while being accessible and easy to use for scientists.
  arXiv  Detail & Related papers  (2024-07-26T21:10:50Z)
• 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)
• 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)
• CELLS: Cost-Effective Evolution in Latent Space for Goal-Directed Molecular Generation [23.618366377098614]
  We propose a cost-effective evolution strategy in latent space, which optimize the molecular latent representation vectors. We adopt a pre-trained molecular generative model to map the latent and observation spaces. We conduct extensive experiments on multiple optimization tasks comparing the proposed framework to several advanced techniques.
  arXiv  Detail & Related papers  (2021-11-30T11:02:18Z)
• 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)
• Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)

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.